[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"_public_publisher_byId_9619b35c-43fb-4ce0-ad0e-5fca919f51cf":3,"_public_publication_all{\"sortAscending\":false,\"sortField\":\"updateTime\",\"page\":0,\"size\":10,\"facet\":true,\"searchKey\":\"publisherId:9619b35c-43fb-4ce0-ad0e-5fca919f51cf,\"}":173},{"code":4,"data":5,"meta":18},"SUCCESS",{"id":6,"createTime":7,"updateTime":8,"relativeEntities":9,"slug":10,"properties":11,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":20,"manageAffiliations":37,"indexDatabases":62,"url":18,"thumbnailPath":18,"statistic":103,"gsStatistic":18,"type":172,"analyzePriority":18},"9619b35c-43fb-4ce0-ad0e-5fca919f51cf","2024-04-10T00:03:28.753+00:00","2025-11-21T10:00:47.458+00:00",[],"Mathematical-Geosciences",{"issn":12,"title":14},{"VOID":13},"18748953",{"VOID":15},"Mathematical Geosciences","PUBLISHER","PENDING",null,0,[21,29],{"id":22,"createTime":23,"updateTime":24,"relativeEntities":25,"label":26,"description":28,"parentId":18,"standard":18,"scholarHubFieldId":18},"cd0e9c62-9445-4f11-88d8-ffaaa645b234","2023-05-29T10:24:10.509+00:00","2023-11-21T07:57:34.083+00:00",[],{"EN":27},"Earth and Planetary Sciences (miscellaneous)",{},{"id":30,"createTime":31,"updateTime":32,"relativeEntities":33,"label":34,"description":36,"parentId":18,"standard":18,"scholarHubFieldId":18},"37634bef-3565-4ad6-b1ba-c43cf4d196be","2023-05-29T10:24:10.937+00:00","2023-11-21T07:37:14.631+00:00",[],{"EN":35},"Mathematics (miscellaneous)",{},[38,51],{"id":39,"createTime":40,"updateTime":41,"relativeEntities":42,"slug":43,"properties":44,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":48,"url":18,"parentIds":49,"statistic":18},"b2bfac93-563a-4fa4-bd81-e546a66bf9bd","2023-05-29T10:24:01.641+00:00","2025-11-21T10:06:17.751+00:00",[],"Springer-Netherlands",{"title":45},{"EN":46},"Springer Netherlands","AFFILIATION",8,[50],"9a7c7208-b28a-42c2-a634-5a7f90eee3ab",{"id":52,"createTime":53,"updateTime":54,"relativeEntities":55,"slug":56,"properties":57,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":60,"url":18,"parentIds":61,"statistic":18},"869fc292-62ea-48f4-960e-51fea58b02ba","2023-05-29T10:24:53.274+00:00","2024-02-20T18:47:04.755+00:00",[],"Springer-Heidelberg",{"title":58},{"EN":59},"Springer Heidelberg",9,[50],[63,83],{"id":64,"indexDatabase":65,"url":79,"indexYears":18,"academicFieldIds":80,"indexDatabaseRanking":18},"a30c21d3-b11b-49c2-b789-74292704c771",{"id":66,"createTime":67,"updateTime":68,"relativeEntities":69,"label":70,"description":72,"key":75,"publicationTags":76,"standard":18},"a4921856-b128-4d9f-8f1f-e80813d3bbd4","2023-05-22T09:59:31.026+00:00","2025-11-21T10:07:52.153+00:00",[],{"EN":71,"VI":71},"ISI\u002FSCIE - Science Citation Index Expanded",{"VI":73,"EN":74},"Cơ sở dữ liệu SCIE","SCIE database","scie",[77,78],"SCIE","ISI","https:\u002F\u002Fmjl.clarivate.com\u002Fsearch-results?issn=1874-8961",[81,82],"0db73426-2364-455f-81a4-efe0f91d712e","dd61be45-caa6-47de-bc01-81a2330f90fe",{"id":84,"indexDatabase":85,"url":97,"indexYears":98,"academicFieldIds":99,"indexDatabaseRanking":102},"219744ae-4f73-4bf7-9947-8c95919801f6",{"id":86,"createTime":87,"updateTime":88,"relativeEntities":89,"label":90,"description":92,"key":94,"publicationTags":95,"standard":18},"3c7051d4-eb7d-4c57-a56b-36fc74c5d1e9","2023-05-22T09:57:18.509+00:00","2025-11-21T10:07:52.274+00:00",[],{"EN":91,"VI":91},"Scopus - Elsevier",{"EN":91,"VI":93},"Cơ sở dữ liệu Scopus thuộc Elsevier","scopus",[96],"SCOPUS","https:\u002F\u002Fwww.scopus.com\u002Fsourceid\u002F12400154723","2008-2025",[100,101],"825d41e1-f1f9-472e-aa11-78b85d501870","1689391c-5702-4349-aaa7-d720ee4321fc","SCOPUS__Q1",{"impactFactor":19,"impactFactorByYear":104,"i10Index":115,"i10IndexLast5Year":48,"totalPublication":116,"totalPublicationByYear":117,"totalCitation":134,"totalCitationByYear":135,"totalCitationPerPublication":152,"totalCitationPerPublicationByYear":153,"hindexLast5Year":127,"hindex":127},{"2012":105,"2013":106,"2014":107,"2015":108,"2016":109,"2017":110,"2018":111,"2019":112,"2020":111,"2021":111,"2022":113,"2023":114},0.47,0.35,0.37,0.31,0.36,0.19,0.32,0.54,0.29,0.56,55,692,{"1997":118,"2007":119,"2008":120,"2009":121,"2010":122,"2011":123,"2012":124,"2013":125,"2014":126,"2015":121,"2016":127,"2017":128,"2018":123,"2019":129,"2020":124,"2021":130,"2022":131,"2023":132,"2024":133},2,4,48,31,35,37,44,49,54,27,33,53,52,36,56,12,2096,{"1997":136,"2008":137,"2009":138,"2010":139,"2011":140,"2012":141,"2013":142,"2014":137,"2015":143,"2016":144,"2017":145,"2018":143,"2019":146,"2020":147,"2021":148,"2022":149,"2023":150,"2024":151},3,225,347,168,175,143,127,91,25,203,69,40,51,67,41,5,3.03,{"1997":154,"2008":155,"2009":156,"2010":157,"2011":158,"2012":159,"2013":160,"2014":161,"2015":162,"2016":163,"2017":164,"2018":165,"2019":166,"2020":167,"2021":168,"2022":169,"2023":170,"2024":171},1.5,4.69,11.19,4.8,4.73,3.25,2.59,4.17,2.94,0.93,6.15,2.46,1.3,0.91,0.98,1.86,0.73,0.42,"JOURNAL",{"meta":174,"data":176},{"total":175},"692",[177,267,361,450,561,669,900,977,1098,1188],{"id":178,"createTime":179,"updateTime":180,"relativeEntities":181,"slug":182,"properties":183,"entityType":191,"verifyStatus":192,"verifyTime":180,"verifyNote":193,"syncStatus":17,"languages":194,"translateLanguages":18,"viewCount":19,"primaryUrl":196,"fullTextUrl":18,"authors":197,"publicationType":234,"publisherRelationship":235,"citationCount":18,"citationInfo":18,"publishDate":263,"publishYear":264,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":265,"isForceReanalyzing":266},"1229d53b-ceb3-4c27-97e2-bbf5a1ebca21","2024-04-11T05:42:13.311+00:00","2025-01-08T23:57:13.276+00:00",[],"Introduction-to-the-Special-Issue-in-Honor-of-Andr%C3%A9-G-Journel",{"keywords":184,"abstract":186,"title":187,"doi":189},{"EN":185},"",{"EN":185},{"EN":188},"Introduction to the Special Issue in Honor of André G. Journel",{"VOID":190},"10.1007\u002Fs11004-021-09925-1","PUBLICATION","VERIFIED","Auto Verify",[195],"EN","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11004-021-09925-1",[198,217],{"id":199,"sortIndex":19,"researcher":18,"roles":200,"affiliations":201,"properties":212},"4080ab52-17e3-418a-b337-9b616e464da5",[],[202],{"id":18,"sortIndex":19,"affiliation":203,"properties":18},{"id":204,"createTime":205,"updateTime":206,"relativeEntities":207,"slug":208,"properties":209,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"6bc276c1-0cf7-41aa-8d20-01ff8ad09779","2023-12-15T02:31:12.530+00:00","2024-09-01T08:40:52.879+00:00",[],"Institute-of-Water-and-Environmental-Engineering-Universitat-Polit%C3%A8cnica-de-Val%C3%A8ncia-Valencia-Spain",{"title":210},{"VI":211},"Institute of Water and Environmental Engineering, Universitat Politècnica de València, Valencia, Spain",{"title":213,"email":215},{"EN":214},"J. Jaime Gómez-Hernández",{"VOID":216},"jgomez@upv.es",{"id":218,"sortIndex":219,"researcher":18,"roles":220,"affiliations":221,"properties":231},"2766007f-9893-4834-99cf-2640465f7bcb",1,[],[222],{"id":18,"sortIndex":19,"affiliation":223,"properties":18},{"id":224,"createTime":225,"updateTime":225,"relativeEntities":226,"slug":227,"properties":228,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"22ab7b6f-27df-46e9-a24a-806f75853aaf","2024-04-11T05:42:13.323+00:00",[],"Tristar-Gold-Inc-Vancouver-Canada",{"title":229},{"EN":230},"Tristar Gold Inc., Vancouver, Canada",{"title":232},{"EN":233},"R. Mohan Srivastava","ARTICLE",{"url":18,"publisher":236,"properties":18},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":237,"slug":10,"properties":238,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":241,"manageAffiliations":242,"indexDatabases":243,"url":18,"thumbnailPath":18,"statistic":258,"gsStatistic":18,"type":172,"analyzePriority":18},[],{"issn":239,"title":240},{"VOID":13},{"VOID":15},[],[],[244,251],{"id":64,"indexDatabase":245,"url":79,"indexYears":18,"academicFieldIds":250,"indexDatabaseRanking":18},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":246,"label":247,"description":248,"key":75,"publicationTags":249,"standard":18},[],{"EN":71,"VI":71},{"VI":73,"EN":74},[77,78],[81,82],{"id":84,"indexDatabase":252,"url":97,"indexYears":98,"academicFieldIds":257,"indexDatabaseRanking":102},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":253,"label":254,"description":255,"key":94,"publicationTags":256,"standard":18},[],{"EN":91,"VI":91},{"EN":91,"VI":93},[96],[100,101],{"impactFactor":19,"impactFactorByYear":259,"i10Index":115,"i10IndexLast5Year":48,"totalPublication":116,"totalPublicationByYear":260,"totalCitation":134,"totalCitationByYear":261,"totalCitationPerPublication":152,"totalCitationPerPublicationByYear":262,"hindexLast5Year":127,"hindex":127},{"2012":105,"2013":106,"2014":107,"2015":108,"2016":109,"2017":110,"2018":111,"2019":112,"2020":111,"2021":111,"2022":113,"2023":114},{"1997":118,"2007":119,"2008":120,"2009":121,"2010":122,"2011":123,"2012":124,"2013":125,"2014":126,"2015":121,"2016":127,"2017":128,"2018":123,"2019":129,"2020":124,"2021":130,"2022":131,"2023":132,"2024":133},{"1997":136,"2008":137,"2009":138,"2010":139,"2011":140,"2012":141,"2013":142,"2014":137,"2015":143,"2016":144,"2017":145,"2018":143,"2019":146,"2020":147,"2021":148,"2022":149,"2023":150,"2024":151},{"1997":154,"2008":155,"2009":156,"2010":157,"2011":158,"2012":159,"2013":160,"2014":161,"2015":162,"2016":163,"2017":164,"2018":165,"2019":166,"2020":167,"2021":168,"2022":169,"2023":170,"2024":171},"2021-01-29",2021,[],false,{"id":268,"createTime":269,"updateTime":270,"relativeEntities":271,"slug":272,"properties":273,"entityType":191,"verifyStatus":192,"verifyTime":270,"verifyNote":193,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":282,"fullTextUrl":18,"authors":283,"publicationType":234,"publisherRelationship":328,"citationCount":18,"citationInfo":18,"publishDate":359,"publishYear":360,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":266},"666dc881-119c-4b77-af38-5e3157ccb600","2024-01-15T13:37:01.423+00:00","2025-02-16T23:56:53.967+00:00",[],"Space-Time-Distribution-of-Trichloroethylene-Groundwater-Concentrations-Geostatistical-Modeling-and-Visualization",{"references":274,"abstract":276,"title":278,"doi":280},{"VOID":275},"Aigner W, Miksch S, Mueller W, Schumann H, Tominski C (2007) Visualizing time-oriented data: a systematic view. Comput Graph 31:401–409\nAndrienko N, Andrienko G, Gatalsky P (2003) Exploratory spatio-temporal visualization: an analytical review. J Vis Lang Comput 14:503–541\nArcher NP, Bradford CM, Villanacci JF, Crain NE, Corsi RL, Chambers DM, Burk T, Blount BC (2015) Relationship between vapor intrusion and human exposure to trichloroethylene. J Environ Sci Health A 50(13):1360–1368\nCameron K, Hunter P (2002) Using spatial models and kriging techniques to optimize long-term groundwater monitoring networks: a case study. Environmetrics 13:629–656\nCappello C, De Iaco S, Posa D (2018) Testing the type of non-separability and some classes of space–time covariance function models. Stoch Environ Res Risk Assess 32:17–35\nCappello C, De Iaco S, Posa D (2020) Covatest: an R package for selecting a class of space–time covariance functions. J Stat Softw 94:1–42\nCDC (2020) Trichloroethylene—ToxFAQs™. https:\u002F\u002Fwww.atsdr.cdc.gov\u002Ftoxfaqs\u002Ftfacts19.pdf Accessed 6 July 2023\nCressie N, Huang HC (1999) Classes of nonseparable, spatio-temporal stationary covariance functions. J Am Stat Assoc 94:1330–1340\nDe Cesare L, Myers DE, Posa D (2002) FORTRAN programs for space–time modeling. Comput Geosc 28:205–212\nDe Iaco S (2010) Space–time correlation analysis: a comparative study. J Appl Stat 37:1027–1041\nDe Iaco S, Myers DE, Posa D (2001) Space–time analysis using a general product-sum model. Stat Probab Lett 52:21–28\nDe Iaco S, Posa D, Cappello C, Maggio S (2019) Isotropy, symmetry, separability and strict positive definiteness for covariance functions: a critical review. Spat Stat 29:89–108\nDeutsch CV (1993) Kriging in a finite domain. Math Geol 25:41–52\nDeutsch CV, Journel AG (1998) Geostatistical software library and user guide. Oxford University Press, New York\nDimitrakopoulos R, Luo X (1994) Spatiotemporal modeling: covariances and ordinary kriging systems. In: Dimitrakopoulos R (ed) Geostatistics for the next century. Kluwer, Dordrecht, pp 88–93\nEPA (2023) TCE Consumer Fact Sheet. https:\u002F\u002Fwww.epa.gov\u002Fassessing-and-managing-chemicals-under-tsca\u002Ffact-sheet-trichloroethylene-tce. Accessed 6 July 2023\nFouedjio F (2017) Second-order non-stationary modeling approaches for univariate geostatistical data. Stoch Environ Res Risk Assess 31:1887–1906\nFouedjio F, Desassis N, Romary T (2015) Estimation of space deformation model for non-stationary random functions. Spat Stat 13:45–61\nGneiting T (2002) Nonseparable, stationary covariance functions for space–time data. J Am Stat Assoc 97:590–600\nGneiting T, Genton MG, Guttorp P (2007) Geostatistical space–time models, stationarity, separability and full symmetry. In: Finkenstaedt B, Isham V, Held L (eds) Statistics of spatio-temporal systems. Monographs in statistics and applied probability. Chapman & Hall\u002FCRC Press, Boca Raton, pp 151–175\nGoovaerts P (1997) Geostatistics for Natural Resources Evaluation. Oxford Univ. Press, New York\nGoovaerts P (2010) Three-dimensional visualization, interactive analysis and contextual mapping of space–time cancer data. In: Painho M, Santos MY, Pundt H (eds) Proceedings AGILE 2010: the 13th AGILE international conference on geographic information science. Springer Verlag, Guimarães, Portugal\nGuo L, Lei L, Zeng Z (2015) Evaluation of spatio-temporal variogram models for mapping Xco2 using satellite observations: a case study in China. IEEE J Sel Top Appl Earth Observ Remote Sens 8:376–385\nHeuvelink GBM, Griffith DA (2010) Space–time geostatistics for geography: a case study of radiation monitoring across parts of Germany. Geogr Anal 42:161–179\nJacquez GM, Goovaerts P, Kaufmann A, Rommel R (2014) SpaceStat 4.0 user manual: software for the space–time analysis of dynamic complex systems, 4th ed. BioMedware, Ann Arbor. https:\u002F\u002Fwww.biomedware.com\u002Ffiles\u002FSpaceStat_4.0_Documentation.pdf. Accessed 3 Feb 2023\nJohnson PD, Goldberg SJ, Mays MZ, Dawson BV (2003) Threshold of trichloroethylene contamination in maternal drinking waters affecting fetal heart development in the rat. Environ Health Perspect 111(3):289–292\nJúnez-Ferreira HE, Hernández-Hernández MA, Herrera GS, González-Trinidad J, Cappello C, Maggio S, De Iaco S (2023) Assessment of changes in regional groundwater levels through spatio-temporal kriging: application to the southern Basin of Mexico aquifer system. Hydrogeol J. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10040-023-02681-y\nKazemi H, Sarukkalige R, Shao Q (2021) Evaluation of non-uniform groundwater level data using spatiotemporal modelling. Groundw Sustain Dev 15:100659\nMcLean MI (2018) Spatio-temporal models for the analysis and optimisation of groundwater quality monitoring networks. PhD thesis. http:\u002F\u002Ftheses.gla.ac.uk\u002F38975\u002F\nMichigan Department of Environment, Great Lakes, and Energy, Wikes Manufacturing TCE Plume (2020) https:\u002F\u002Fwww.michigan.gov\u002Fegle\u002F0,9429,7-135-3311_4109_9846_30022-385691--,00.html. Accessed 3 Feb 2023\nMontero JM, Fernandez-Aviles G, Mateu J (2015) Spatial and spatio-temporal geostatistical modeling and kriging. Wiley, New York\nPorcu E, Furrer R, Nychka D (2021) 30 Years of space–time covariance functions. Wires Comput Stat 13:e1512. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fwics.1512\nRautman CA, Istok JD (1996) Probabilistic assessment of ground-water contamination: 1. Geostat Framew Ground Water 34:899–909\nRemy N, Boucher A, Wu J (2008) Applied geostatistics with SGeMS: a user’s guide. Cambridge University Press, Cambridge\nRihana-Abdallah A, Pang Y (2019) Fate and transport of a TCE groundwater contamination plume. In: Proceedings of the 2019 ASEE (American Society for Engineering Education) North-Central Section conference. https:\u002F\u002Fasee-ncs.org\u002Fproceedings\u002F2019\u002F1\u002F71.pdf. Accessed 3 Feb 2023\nRuybal CJ, Hogue TS, McCray JE (2019) Evaluation of groundwater levels in the Arapahoe aquifer using spatiotemporal regression kriging. Water Resour Res 55:2820–2837\nSampson PD, Guttorp P (1992) Nonparametric estimation of nonstationary spatial covariance structure. J Am Stat Assoc 87:108–119\nSAS Institute Inc. (2011) SAS\u002FSTAT 9.3 user’s guide. SAS Institute Inc., Cary\nTominski C, Schulze-Wollgast P, Schuman H (2005) 3D information visualization for time dependent data on maps. In: Proceedings of the ninth international conference on information visualisation. IEEE. https:\u002F\u002Fdoi.org\u002F10.1109\u002FIV.2005.3\nVan Driel JN (1989) Three dimensional display of geologic data. In: Raper J (ed) Three dimensional applications in Geological Information System. Taylor & Francis, London, pp 1–9\nVarouchakis EA, Hristopulos DT (2017) Comparison of spatiotemporal variogram functions based on a sparse dataset of ground-water level variations. Spat Stat 34:66\nVarouchakis EA, Theodoridou PG, Karatzas GP (2019) Spatiotemporal geostatistical modeling of groundwater levels under a Bayesian framework using means of physical background. J Hydrol 575:487–498\nVarouchakis EA, Guardiola-Alber C, Karatzas GP (2022) Spatiotemporal geostatistical analysis of groundwater level in aquifer aystems of complex hydrogeology. Water Resour Res 58(3):e2021WR029988\nWackernagel H (1998) Multivariate geostatistics, 2nd edn. Springer, Berlin",{"EN":277},"This paper describes a geostatistical approach to model and visualize the space–time distribution of groundwater contaminants. It is illustrated using data from one of the world’s largest plume of trichloroethylene (TCE) contamination, extending over 23 km2, which has polluted drinking water wells in northern Michigan. A total of 613 TCE concentrations were recorded at 36 wells between May 2003 and October 2018. To account for the non-stationarity of the spatial covariance, the data were first projected in a new space using multidimensional scaling. During this spatial deformation the domain is stretched in regions of relatively lower spatial correlation (i.e., higher spatial dispersion), while being contracted in regions of higher spatial correlation. The range of temporal autocorrelation is 43 months, while the spatial range is 11 km. The sample semivariogram was fitted using three different types of non-separable space–time models, and their prediction performance was compared using cross-validation. The sum-metric and product-sum semivariogram models performed equally well, with a mean absolute error of prediction corresponding to 23% of the mean TCE concentration. The observations were then interpolated every 6 months to the nodes of a 150 m spacing grid covering the study area and results were visualized using a three-dimensional space–time cube. This display highlights how TCE concentrations increased over time in the northern part of the study area, as the plume is flowing to the so-called Chain of Lakes.",{"EN":279},"Space–Time Distribution of Trichloroethylene Groundwater Concentrations: Geostatistical Modeling and Visualization",{"VOID":281},"10.1007\u002Fs11004-023-10107-4","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11004-023-10107-4",[284,300,316],{"id":285,"sortIndex":219,"researcher":18,"roles":286,"affiliations":288,"properties":297},"068fbc37-3c28-437b-b043-1ecaa91fd745",[287],"AUTHOR",[289],{"id":18,"sortIndex":19,"affiliation":290,"properties":18},{"id":291,"createTime":292,"updateTime":292,"relativeEntities":293,"slug":18,"properties":294,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"c718bbaa-400c-4dd2-ac27-5214eaef79fa","2024-01-15T13:37:01.476+00:00",[],{"title":295},{"VI":296},"Department of Civil, Architectural and Environmental Engineering, University of Detroit Mercy, Detroit, USA",{"title":298},{"VI":299},"Alexa Rihana-Abdallah",{"id":301,"sortIndex":19,"researcher":18,"roles":302,"affiliations":303,"properties":313},"16e3b152-2d41-4ae6-9e7c-3b5225db31c9",[287],[304],{"id":18,"sortIndex":19,"affiliation":305,"properties":18},{"id":306,"createTime":307,"updateTime":307,"relativeEntities":308,"slug":309,"properties":310,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"211e6cee-12c8-4e31-9b41-958836a3b3b2","2024-04-18T23:51:33.975+00:00",[],"BioMedware-Inc-Ann-Arbor-USA",{"title":311},{"EN":312},"BioMedware Inc., Ann Arbor, USA",{"title":314},{"VI":315},"Pierre Goovaerts",{"id":317,"sortIndex":118,"researcher":18,"roles":318,"affiliations":319,"properties":325},"9b4ca4f7-6a0d-4b8d-889e-09586a39c19b",[287],[320],{"id":18,"sortIndex":19,"affiliation":321,"properties":18},{"id":291,"createTime":292,"updateTime":292,"relativeEntities":322,"slug":18,"properties":323,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":324},{"VI":296},{"title":326},{"VI":327},"Yuncong Pang",{"url":282,"publisher":329,"properties":356},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":330,"slug":10,"properties":331,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":334,"manageAffiliations":335,"indexDatabases":336,"url":18,"thumbnailPath":18,"statistic":351,"gsStatistic":18,"type":172,"analyzePriority":18},[],{"issn":332,"title":333},{"VOID":13},{"VOID":15},[],[],[337,344],{"id":64,"indexDatabase":338,"url":79,"indexYears":18,"academicFieldIds":343,"indexDatabaseRanking":18},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":339,"label":340,"description":341,"key":75,"publicationTags":342,"standard":18},[],{"EN":71,"VI":71},{"VI":73,"EN":74},[77,78],[81,82],{"id":84,"indexDatabase":345,"url":97,"indexYears":98,"academicFieldIds":350,"indexDatabaseRanking":102},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":346,"label":347,"description":348,"key":94,"publicationTags":349,"standard":18},[],{"EN":91,"VI":91},{"EN":91,"VI":93},[96],[100,101],{"impactFactor":19,"impactFactorByYear":352,"i10Index":115,"i10IndexLast5Year":48,"totalPublication":116,"totalPublicationByYear":353,"totalCitation":134,"totalCitationByYear":354,"totalCitationPerPublication":152,"totalCitationPerPublicationByYear":355,"hindexLast5Year":127,"hindex":127},{"2012":105,"2013":106,"2014":107,"2015":108,"2016":109,"2017":110,"2018":111,"2019":112,"2020":111,"2021":111,"2022":113,"2023":114},{"1997":118,"2007":119,"2008":120,"2009":121,"2010":122,"2011":123,"2012":124,"2013":125,"2014":126,"2015":121,"2016":127,"2017":128,"2018":123,"2019":129,"2020":124,"2021":130,"2022":131,"2023":132,"2024":133},{"1997":136,"2008":137,"2009":138,"2010":139,"2011":140,"2012":141,"2013":142,"2014":137,"2015":143,"2016":144,"2017":145,"2018":143,"2019":146,"2020":147,"2021":148,"2022":149,"2023":150,"2024":151},{"1997":154,"2008":155,"2009":156,"2010":157,"2011":158,"2012":159,"2013":160,"2014":161,"2015":162,"2016":163,"2017":164,"2018":165,"2019":166,"2020":167,"2021":168,"2022":169,"2023":170,"2024":171},{"pages":357},{"VOID":358},"1-28","2023-10-14",2023,{"id":362,"createTime":363,"updateTime":364,"relativeEntities":365,"slug":366,"properties":367,"entityType":191,"verifyStatus":192,"verifyTime":364,"verifyNote":193,"syncStatus":17,"languages":378,"translateLanguages":18,"viewCount":19,"primaryUrl":379,"fullTextUrl":18,"authors":380,"publicationType":234,"publisherRelationship":402,"citationCount":19,"citationInfo":437,"publishDate":439,"publishYear":440,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":441,"isForceReanalyzing":266},"c9fb90e6-0789-417a-8711-d08e80e60cb1","2024-04-18T17:57:56.920+00:00","2024-12-11T23:54:29.543+00:00",[],"Michael-Greenacre-Compositional-Data-Analysis-in-Practice",{"mag":368,"keywords":370,"openalex":371,"abstract":373,"title":374,"doi":376},{"VOID":369},"2937271826",{},{"VOID":372},"W2937271826",{},{"EN":375},"Michael Greenacre: Compositional Data Analysis in Practice",{"VOID":377},"10.1007\u002Fs11004-019-09804-w",[195],"http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs11004-019-09804-w",[381],{"id":382,"sortIndex":19,"researcher":18,"roles":383,"affiliations":384,"properties":395},"a6a0c186-55a9-46a8-8943-9e9f2c1c33f3",[],[385],{"id":386,"sortIndex":19,"affiliation":387,"properties":18},"a5b97cc4-3142-41ce-8451-175160dadc9f",{"id":388,"createTime":389,"updateTime":389,"relativeEntities":390,"slug":391,"properties":392,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"50925c65-0d99-4f0f-9f6a-bd682be1a076","2024-04-18T17:57:56.981+00:00",[],"Department-Modelling-and-Evaluation-Helmholtz-Institute-Freiberg-for-Resource-Technology-Helmholtz-Zentrum-Dresden-Rossendorf-Freiberg-Germany",{"title":393},{"EN":394},"Department Modelling and Evaluation, Helmholtz Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf, Freiberg, Germany",{"openalex":396,"orcid":398,"title":400},{"VOID":397},"A5080950597",{"VOID":399},"https:\u002F\u002Forcid.org\u002F0000-0001-9847-0462",{"EN":401},"Raimon Tolosana‐Delgado",{"url":18,"publisher":403,"properties":430},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":404,"slug":10,"properties":405,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":408,"manageAffiliations":409,"indexDatabases":410,"url":18,"thumbnailPath":18,"statistic":425,"gsStatistic":18,"type":172,"analyzePriority":18},[],{"issn":406,"title":407},{"VOID":13},{"VOID":15},[],[],[411,418],{"id":64,"indexDatabase":412,"url":79,"indexYears":18,"academicFieldIds":417,"indexDatabaseRanking":18},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":413,"label":414,"description":415,"key":75,"publicationTags":416,"standard":18},[],{"EN":71,"VI":71},{"VI":73,"EN":74},[77,78],[81,82],{"id":84,"indexDatabase":419,"url":97,"indexYears":98,"academicFieldIds":424,"indexDatabaseRanking":102},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":420,"label":421,"description":422,"key":94,"publicationTags":423,"standard":18},[],{"EN":91,"VI":91},{"EN":91,"VI":93},[96],[100,101],{"impactFactor":19,"impactFactorByYear":426,"i10Index":115,"i10IndexLast5Year":48,"totalPublication":116,"totalPublicationByYear":427,"totalCitation":134,"totalCitationByYear":428,"totalCitationPerPublication":152,"totalCitationPerPublicationByYear":429,"hindexLast5Year":127,"hindex":127},{"2012":105,"2013":106,"2014":107,"2015":108,"2016":109,"2017":110,"2018":111,"2019":112,"2020":111,"2021":111,"2022":113,"2023":114},{"1997":118,"2007":119,"2008":120,"2009":121,"2010":122,"2011":123,"2012":124,"2013":125,"2014":126,"2015":121,"2016":127,"2017":128,"2018":123,"2019":129,"2020":124,"2021":130,"2022":131,"2023":132,"2024":133},{"1997":136,"2008":137,"2009":138,"2010":139,"2011":140,"2012":141,"2013":142,"2014":137,"2015":143,"2016":144,"2017":145,"2018":143,"2019":146,"2020":147,"2021":148,"2022":149,"2023":150,"2024":151},{"1997":154,"2008":155,"2009":156,"2010":157,"2011":158,"2012":159,"2013":160,"2014":161,"2015":162,"2016":163,"2017":164,"2018":165,"2019":166,"2020":167,"2021":168,"2022":169,"2023":170,"2024":171},{"volume":431,"pages":433,"issue":435},{"VOID":432},"51",{"VOID":434},"837-839",{"VOID":436},"6",{"total":19,"publishYear":18,"statisticByYear":438},{},"2019-08-01",2019,[442,446],{"id":18,"text":443,"url":18,"identifiers":444},"Aitchison J (1986) The statistical analysis of compositional data. Chapman & Hall, London",{"doi":445},"10.1007\u002F978-94-009-4109-0",{"id":18,"text":447,"url":18,"identifiers":448},"Egozcue JJ, Pawlowsky-Glahn V, Mateu-Figueras G, Barceló-Vidal C (2003) Isometric logratio transformations for compositional data. Math Geol 35:279–300",{"doi":449},"10.1023\u002FA:1023818214614",{"id":451,"createTime":452,"updateTime":453,"relativeEntities":454,"slug":455,"properties":456,"entityType":191,"verifyStatus":192,"verifyTime":453,"verifyNote":193,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":465,"fullTextUrl":18,"authors":466,"publicationType":234,"publisherRelationship":526,"citationCount":18,"citationInfo":18,"publishDate":559,"publishYear":560,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":266},"7470de6b-5108-4a4a-8834-2e425c1d5f17","2024-01-14T05:14:05.628+00:00","2024-12-13T23:53:10.445+00:00",[],"Which-Path-to-Choose-in-Sequential-Gaussian-Simulation",{"references":457,"abstract":459,"title":461,"doi":463},{"VOID":458},"Abdu H, Robinson DA, Seyfried M, Jones SB (2008) Geophysical imaging of watershed subsurface patterns and prediction of soil texture and water holding capacity. Water Resour Res 44(4):W00D18. doi:10.1029\u002F2008WR007043\nBarnsley MF, Devaney RL, Mandelbrot BB, Peitgen HO, Saupe D, Voss RF (1988) The science of fractal images. Springer, New York. doi:10.1007\u002F978-1-4612-3784-6\nBoulanger F (1990) Modélisation et simulation de variables régionalisées par des fonctions aléatoires stables. Ph.D. thesis, Ecole des Mines de Paris, Fontainebleau\nBox GEP, Jenkins GM, Reinsel GC (2008) Time series analysis, vol 37. Wiley, Hoboken. doi:10.1002\u002F9781118619193\nChilès JP, Delfiner P (1999) Geostatistics, Wiley series in probability and statistics, vol 497. Wiley, Hoboken. doi:10.1002\u002F9780470316993\nDaly C (2005) Higher order models using entropy, Markov random fields and sequential simulation. In: Leuangthong O, Deutsch CV (eds) Geostatistics Banff 2004. Quantitative Geology and Geostatistics, vol 14. Springer, Dordrecht\nDay-Lewis FD, Lane JW (2004) Assessing the resolution-dependent utility of tomograms for geostatistics. Geophys Res Lett 31(7):L07,503. doi:10.1029\u002F2004GL019617\nDelbari M, Afrasiab P, Loiskandl W (2009) Using sequential Gaussian simulation to assess the field-scale spatial uncertainty of soil water content. Catena 79(2):163–169. doi:10.1016\u002Fj.catena.2009.08.001\nDeutsch CV, Journel AG (1992) GSLIB: Geostatistical software library and user’s guide. Technical Representative, New York\nDimitrakopoulos R, Luo X (2004) Generalized sequential Gaussian simulation on group size and screen-effect approximations for large field simulations. Math Geol 36(5):567–591. doi:10.1023\u002FB:MATG.0000037737.11615.df\nDimitrakopoulos R, Farrelly CT, Godoy M (2002) Moving forward from traditional optimization: grade uncertainty and risk effects in open-pit design. Min Technol 111(1):82–88. doi:10.1179\u002Fmnt.2002.111.1.82\nEmery X (2004) Testing the correctness of the sequential algorithm for simulating Gaussian random fields. Stoch Env Res Risk Assess 18(6):401–413. doi:10.1007\u002Fs00477-004-0211-7\nEmery X, Peláez M (2011) Assessing the accuracy of sequential Gaussian simulation and cosimulation. Comput Geosci 15(4):673–689. doi:10.1007\u002Fs10596-011-9235-5\nFournier A, Fussell D, Carpenter L (1982) Computer rendering of stochastic models. Commun ACM 25(6):371–384. doi:10.1145\u002F358523.358553\nGómez-Hernández JJ, Cassiraga EF (1994) Theory and practice of sequential simulation. Kluwer Academic Publishers, Dordrecht. doi:10.1007\u002F978-94-015-8267-4_10\nGómez-Hernández JJ, Journel AG (1993) Geostatistics Tróia ’92, quantitative geology and geostatistics, vol 5. Springer, Dordrecht. doi:10.1007\u002F978-94-011-1739-5\nGoovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, Oxford\nGoovaerts P (2001) Geostatistical modelling of uncertainty in soil science. Geoderma 103(1–2):3–26. doi:10.1016\u002FS0016-7061(01)00067-2\nHalton JH (1960) On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals. Numer Math 2(1):84–90. doi:10.1007\u002FBF01386213\nHansen TM, Journel AG, Tarantola A, Mosegaard K (2006) Linear inverse Gaussian theory and geostatistics. Geophysics 71(6):R101–R111. doi:10.1190\u002F1.2345195\nIsaaks EH (1991) The application of Monte Carlo methods to the analysis of spatially correlated data. Ph.D. thesis, Stanford University\nIsaaks EH, Srivastava RM (1989) An introduction to applied geostatistics. Oxford University Press, New York\nJohnson ME (1987) Multivariate statistical simulation. Wiley series in probability and statistics. Wiley, Hoboken. doi:10.1002\u002F9781118150740\nJournel AG (1989) Fundamentals of geostatistics in five lessons, vol 16. American Geophysical Union, Washington. doi:10.1029\u002FSC008\nJuang KW, Chen YS, Lee DY (2004) Using sequential indicator simulation to assess the uncertainty of delineating heavy-metal contaminated soils. Environ Pollut 127(2):229–238. doi:10.1016\u002Fj.envpol.2003.07.001\nKocis L, Whiten WJ (1997) Computational investigations of low-discrepancy sequences. ACM Trans Math Softw 23(2):266–294. doi:10.1145\u002F264029.264064\nLantuéjoul C (2002) Geostatistical simulation. Springer, Berlin. doi:10.1007\u002F978-3-662-04808-5\nLee SY, Carle SF, Fogg GE (2007) Geologic heterogeneity and a comparison of two geostatistical models: sequential Gaussian and transition probability-based geostatistical simulation. Adv Water Resour 30(9):1914–1932. doi:10.1016\u002Fj.advwatres.2007.03.005\nLeuangthong O, McLennan JA, Deutsch CV (2004) Minimum acceptance criteria for geostatistical realizations. Nat Resour Res 13(3):131–141. doi:10.1023\u002FB:NARR.0000046916.91703.bb\nLin YP, Chang TK, Teng TP (2001) Characterization of soil lead by comparing sequential Gaussian simulation, simulated annealing simulation and kriging methods. Environ Geol 41(1–2):189–199. doi:10.1007\u002Fs002540100382\nMcLennan J (2002) The effect of the simulation path in sequential gaussian simulation. Technical Representative, University of Alberta\nMeyer TH (2004) The discontinuous nature of kriging interpolation for digital terrain modeling. Cartogr Geogr Inf Sci 31(4):209–216. doi:10.1559\u002F1523040042742385\nMowrer H (1997) Propagating uncertainty through spatial estimation processes for old-growth subalpine forests using sequential Gaussian simulation in GIS. Ecol Model 98(1):73–86. doi:10.1016\u002FS0304-3800(96)01938-2\nOmre H, Sølna K, Tjelmeland H (1993) Simulation of random functions on large lattices. In: Soares A (ed) Geostatistics Tròia ’92. Kluwer Academic Publishers, Dordrecht, pp 179–199. doi:10.1007\u002F978-94-011-1739-5_16\nRivoirard J (1984) Le comportement des poids de krigeage. Ph.D. thesis, Ecole des Mines de Paris, Fontainebleau\nSafikhani M, Asghari O, Emery X (2017) Assessing the accuracy of sequential gaussian simulation through statistical testing. Stoch Env Res Risk Assess 31(2):523–533. doi:10.1007\u002Fs00477-016-1255-1\nSrinivasan BV, Duraiswami R, Murtugudde R (2008) Efficient kriging for real-time spatio-temporal interpolation Linear kriging. In: 20th conference on probablility and statistics in atmospheric sciences, pp 228–235\nTran TT (1994) Improving variogram reproduction on dense simulation grids. Comput Geosci 20(7–8):1161–1168. doi:10.1016\u002F0098-3004(94)90069-8\nTrefethen LN, Bau D III (1997) Numerical linear algebra, vol 50. SIAM, Philadelphia\nVerly GW (1993) Sequential Gaussian cosimulation: a simulation method integrating several types of information. In: Soares A (ed) Geostatistics Tròia ’92. Kluwer Academic Publishers, Dordrecht, pp 543–554. doi:10.1007\u002F978-94-011-1739-5_42\nZhao Y, Xu X, Huang B, Sun W, Shao X, Shi X, Ruan X (2007) Using robust kriging and sequential Gaussian simulation to delineate the copper- and lead-contaminated areas of a rapidly industrialized city in Yangtze River Delta, China. Environ Geol 52(7):1423–1433. doi:10.1007\u002Fs00254-007-0667-0",{"EN":460},"Sequential Gaussian Simulation is a commonly used geostatistical method for populating a grid with a Gaussian random field. The theoretical foundation of this method implies that all previously simulated nodes, referred to as neighbors, should be included in the kriging system of each newly simulated node. This would, however, require solving a large number of linear systems of increasing size as the simulation progresses, which, for computational reasons, is generally not feasible. Traditionally, this problem is addressed by limiting the number of neighbors to the ones closest to the simulated node. This does, however, result in artifacts in the realization. The simulation path, that is, the order in which nodes are visited, is known to influence the location and magnitude of these artifacts. So far, few rigorous studies linking the simulation path to the associated biases are available and, correspondingly, recommendations regarding the choice of the simulation path are largely based on empirical evidence. In this study, a comprehensive analysis of the influence of the path on the simulation errors is presented, based on which guidelines for choosing an optimal path were developed. The most common path types are systematically assessed based on the comparison of the simulation covariance matrices with the covariance of the underlying spatial model. Our analysis indicates that the optimal path is defined as the one minimizing the information lost by the omission of neighbors. Classification into clustering paths, that is, paths simulating consecutively close nodes, and declustering paths, that is, paths simulating consecutively distant nodes, was found to be an efficient way of determining path performance. Common examples of the latter are multi-grid, mid-point, and quasi-random paths, while the former include row-by-row and spiral paths. Indeed, clustering paths tend to inadequately approximate covariances at intermediate and large lag distances, because their neighborhood is only composed of nearby nodes. On the other hand, declustering paths minimize the correlation among nodes, thus ensuring that the neighbors are more diverse, and that only weakly correlated neighbors are omitted.",{"EN":462},"Which Path to Choose in Sequential Gaussian Simulation",{"VOID":464},"10.1007\u002Fs11004-017-9699-5","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11004-017-9699-5",[467,482,497,514],{"id":468,"sortIndex":219,"researcher":18,"roles":469,"affiliations":470,"properties":479},"72850728-6c2f-4626-9fc8-b97ccfb6b0aa",[287],[471],{"id":18,"sortIndex":19,"affiliation":472,"properties":18},{"id":473,"createTime":474,"updateTime":474,"relativeEntities":475,"slug":18,"properties":476,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"1dcbed8f-a980-46a5-842c-baef78dfbeb2","2024-01-14T05:14:05.649+00:00",[],{"title":477},{"VI":478},"Institute of Earth Surface Dynamics, University de Lausanne, Lausanne, Switzerland",{"title":480},{"VI":481},"Grégoire Mariethoz",{"id":483,"sortIndex":136,"researcher":18,"roles":484,"affiliations":485,"properties":494},"2fde747b-23b3-446f-9f12-0e792386f797",[287],[486],{"id":18,"sortIndex":19,"affiliation":487,"properties":18},{"id":488,"createTime":489,"updateTime":489,"relativeEntities":490,"slug":18,"properties":491,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"fb3d6d9a-1c8d-42a1-a615-03fab8cdb0f1","2024-01-14T05:14:05.668+00:00",[],{"title":492},{"VI":493},"Institute of Earth Sciences, University de Lausanne, Lausanne, Switzerland",{"title":495},{"VI":496},"Klaus Holliger",{"id":498,"sortIndex":118,"researcher":18,"roles":499,"affiliations":500,"properties":511},"05e08de8-c7a7-4489-b6a4-ab6c63d52c37",[287],[501],{"id":18,"sortIndex":19,"affiliation":502,"properties":18},{"id":503,"createTime":504,"updateTime":505,"relativeEntities":506,"slug":507,"properties":508,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"1c65b936-7658-416f-b9df-fd5457a36a2f","2024-04-07T11:31:57.190+00:00","2024-09-03T14:52:54.500+00:00",[],"Centre-Eau-Terre-Environnement-Institut-national-de-la-recherche-scientifique-Qu%C3%A9bec-Canada",{"title":509},{"VI":510},"Centre – Eau Terre Environnement, Institut national de la recherche scientifique, Québec, Canada",{"title":512},{"VI":513},"Erwan Gloaguen",{"id":515,"sortIndex":19,"researcher":18,"roles":516,"affiliations":517,"properties":523},"b823fa9e-3a88-4740-996c-f7fc6526b25f",[287],[518],{"id":18,"sortIndex":19,"affiliation":519,"properties":18},{"id":488,"createTime":489,"updateTime":489,"relativeEntities":520,"slug":18,"properties":521,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":522},{"VI":493},{"title":524},{"VI":525},"Raphaël Nussbaumer",{"url":465,"publisher":527,"properties":554},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":528,"slug":10,"properties":529,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":532,"manageAffiliations":533,"indexDatabases":534,"url":18,"thumbnailPath":18,"statistic":549,"gsStatistic":18,"type":172,"analyzePriority":18},[],{"issn":530,"title":531},{"VOID":13},{"VOID":15},[],[],[535,542],{"id":64,"indexDatabase":536,"url":79,"indexYears":18,"academicFieldIds":541,"indexDatabaseRanking":18},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":537,"label":538,"description":539,"key":75,"publicationTags":540,"standard":18},[],{"EN":71,"VI":71},{"VI":73,"EN":74},[77,78],[81,82],{"id":84,"indexDatabase":543,"url":97,"indexYears":98,"academicFieldIds":548,"indexDatabaseRanking":102},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":544,"label":545,"description":546,"key":94,"publicationTags":547,"standard":18},[],{"EN":91,"VI":91},{"EN":91,"VI":93},[96],[100,101],{"impactFactor":19,"impactFactorByYear":550,"i10Index":115,"i10IndexLast5Year":48,"totalPublication":116,"totalPublicationByYear":551,"totalCitation":134,"totalCitationByYear":552,"totalCitationPerPublication":152,"totalCitationPerPublicationByYear":553,"hindexLast5Year":127,"hindex":127},{"2012":105,"2013":106,"2014":107,"2015":108,"2016":109,"2017":110,"2018":111,"2019":112,"2020":111,"2021":111,"2022":113,"2023":114},{"1997":118,"2007":119,"2008":120,"2009":121,"2010":122,"2011":123,"2012":124,"2013":125,"2014":126,"2015":121,"2016":127,"2017":128,"2018":123,"2019":129,"2020":124,"2021":130,"2022":131,"2023":132,"2024":133},{"1997":136,"2008":137,"2009":138,"2010":139,"2011":140,"2012":141,"2013":142,"2014":137,"2015":143,"2016":144,"2017":145,"2018":143,"2019":146,"2020":147,"2021":148,"2022":149,"2023":150,"2024":151},{"1997":154,"2008":155,"2009":156,"2010":157,"2011":158,"2012":159,"2013":160,"2014":161,"2015":162,"2016":163,"2017":164,"2018":165,"2019":166,"2020":167,"2021":168,"2022":169,"2023":170,"2024":171},{"volume":555,"pages":557},{"VOID":556},"50",{"VOID":558},"97-120","2017-08-09",2017,{"id":562,"createTime":563,"updateTime":564,"relativeEntities":565,"slug":566,"properties":567,"entityType":191,"verifyStatus":192,"verifyTime":564,"verifyNote":193,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":576,"fullTextUrl":18,"authors":577,"publicationType":234,"publisherRelationship":634,"citationCount":18,"citationInfo":18,"publishDate":667,"publishYear":668,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":266},"83572c30-cafd-420e-ad5d-ab75b8bf33db","2024-01-09T21:11:18.157+00:00","2025-02-13T23:52:36.086+00:00",[],"Use-of-Gestalt-Theory-and-Random-Sets-for-Automatic-Detection-of-Linear-Geological-Features",{"references":568,"abstract":570,"title":572,"doi":574},{"VOID":569},"Achauer U, Masson F (2002) Seismic tomography of continental rifts revisited: from relative to absolute heterogeneities. Tectonophysics 358:17–37\nAwrangjeb M, Ravanbakhsh M, Fraser CS (2010) Automatic detection of residential buildings using LIDAR data and multispectral imagery. ISPRS J Photogramm Remote Sens 65(5):457–467\nBatschelet E (1981) Circular statistics in biology, vol 371. Academic Press, London\nBurns J, Hanson AR, Riseman EM (1986) Extracting straight lines. IEEE Trans Pattern Anal Mach Intell PAMI–8(4):425–455\nCanny J (1986) A computational approach to edge detection. IEEE Trans Pattern Analy Mach Intell 6:679–698\nChandrasiri Ekneligoda T, Henkel H (2010) Interactive spatial analysis of lineaments. Comput Geosci 36(8):1081–1090\nClark C, Wilson C (1994) Spatial analysis of lineaments. Comput Geosci 20(7–8):1237–1258\nDavis JC, Sampson RJ (2002) Statistics and data analysis in geology, vol 3. Wiley, New York\nDesolneux A, Moisan L, Morel JM (2000) Meaningful alignments. Int J Comput Vis 40(1):7–23\nDesolneux A, Moisan L, Morel JM (2001) Edge detection by Helmholtz principle. J Math Imaging Vis 14(3):271–284\nDesolneux A, Moisan L, Morel JM (2003) Computational gestalts and perception thresholds. J Physiol Paris 97(2):311–324\nERS ERI (2014) Sketch maps of extensional structures. http:\u002F\u002Fwww.europlanet-ri.eu\u002F\nERSDAC (2005) Aster user’s guide ver.4.0\nGrompone von Gioi R, Jakubowicz J, Morel JM, Randall G (2012) LSD: a line segment detector. Image Process On Line. doi:10.5201\u002Fipol.2012.gjmr-lsd. http:\u002F\u002Fwww.ipol.im\u002Fpub\u002Fart\u002F2012\u002Fgjmr-lsd\u002F\nGlasbey CA, Horgan GW (1995) Image analysis for the biological sciences, vol 1. Wiley, Chichester\nGómez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Eng Geol 78:11–27\nGoodchild MF, Jeansoulin R (1998) Statistical representation of relative positional uncertainty for geographical linear features. In: Data quality in geographic information: from error to uncertainty. Hermes, Paris pp 87–96\nGuru D, Shekar B, Nagabhushan P (2004) A simple and robust line detection algorithm based on small eigenvalue analysis. Pattern Recognit Lett 25(1):1–13. doi:10.1016\u002Fj.patrec.2003.08.007\nHashim M, Ahmad S, Johari MAM, Pour AB (2013) Automatic lineament extraction in a heavily vegetated region using Landsat Enhanced Thematic Mapper (ETM+) imagery. Adv Space Res 51(5):874–890\nHeipke C, Steger C, Multhammer R (1995) A hierarchical approach to automatic road extraction from aerial imagery. In: Proceedings of the SPIE The International Society for Optical Engineering, p 222\nHobbs WH (1904) Lineaments of the Atlantic border region. Geol Soc Am Bull 15:480–506\nHung L, Batelaan O, De Smedt F (2005) Lineament extraction and analysis, comparison of LANDSAT ETM and ASTER imagery. Case study: Suoimuoi tropical karst catchment, Vietnam, vol 5983\nJordan G, Schott B (2005) Application of wavelet analysis to the study of spatial pattern of morphotectonic lineaments in digital terrain models. A case study. Remote Sens Environ 94(1):31–38\nJuneja M, Sandhu PS (2009) Performance evaluation of edge detection techniques for images in spatial domain. Methodology 1(5):614–621\nKhomyakov M (2012) Comparative evaluation of linear edge detection methods. Pattern Recognit Image Anal 22(2):291–302\nKit O, Lüdeke M (2013) Automated detection of slum area change in Hyderabad, India using multitemporal satellite imagery. ISPRS J Photogramm Remote Sens 83:130–137\nKoike K, Nagano S, Ohmi M (1995) Lineament analysis of satellite images using a segment tracing algorithm (STA). Comput Geosci 21(9):1091–1104\nLee T, Moon W (2002) Lineament extraction from Landsat TM, JERS-1 SAR, and DEM for geological applications, vol 6, pp 3276–3278\nMardia KV, Jupp PE (2009) Directional statistics, vol 494. Wiley, New York\nMarghany M, Hashim M (2010) Lineament mapping using multispectral remote sensing satellite data. Int J Phys Sci 5(10):1501–1507\nMarghany M, Mansor S, Hashim M (2009) Geologic mapping of United Arab Emirates using multispectral remotely sensed data. Am J Eng Appl Sci 2(2):476\nMena JB, Malpica JA (2005) An automatic method for road extraction in rural and semi-urban areas starting from high resolution satellite imagery. Pattern Recognit Lett 26(9):1201–1220\nMolchanov I (2005) Expectations of random sets. In: Theory of random sets, probability and its applications. doi:10.1007\u002F1-84628-150-4_2\nMostafa ME, Bishta AZ (2005) Significance of lineament patterns in rock unit classification and designation: a pilot study on the Gharib-Dara area, Northern Eastern Desert, Egypt. Int J Remote Sens 26(7):1463–1475\nO’Leary DW, Friedman JD, Pohn HA (1976) Lineament, linear, lineation: some proposed new standards for old terms. Geol Soc Am Bull 87(10):1463–1469. doi:10.1130\u002F0016-7606(1976)87\u003C1463:LLLSPN>2.0.CO;2\nPal S, Majumdar T, Bhattacharya A (2006) Extraction of linear and anomalous features using ERS SAR data over Singhbhum Shear Zone, Jharkhand using fast Fourier transform. Int J Remote Sens 27(20):4513–4528\nPapazachos B, Scordilis E, Panagiotopoulos D, Papazachos C, Karakaisis G (2004) Global relations between seismic fault parameters and moment magnitude of earthquakes. Bull Geol Soc Greece 36:1482–1489\nRamli M, Yusof N, Yusoff M, Juahir H, Shafri H (2010) Lineament mapping and its application in landslide hazard assessment: a review. Bull Eng Geol Environ 69(2):215–233. doi:10.1007\u002Fs10064-009-0255-5\nSinghal B, Gupta R (2010) Fractures and discontinuities. In: Applied hydrogeology of fractured rocks. doi:10.1007\u002F978-90-481-8799-7-2\nSolomon S, Ghebreab W (2006) Lineament characterization and their tectonic significance using Landsat TM data and field studies in the central highlands of Eritrea. J Afr Earth Sci 46(4):371–378\nSoto-Pinto C, Arellano-Baeza A, Sánchez G (2013) A new code for automatic detection and analysis of the lineament patterns for geophysical and geological purposes (ADALGEO). Comput Geosci 57(0):93–103. doi:10.1016\u002Fj.cageo.2013.03.019\nThornton M, Atkinson PM, Holland D (2007) A linearised pixel-swapping method for mapping rural linear land cover features from fine spatial resolution remotely sensed imagery. Comput Geosci 33(10):1261–1272\nTurker M, Kok EH (2013) Field-based sub-boundary extraction from remote sensing imagery using perceptual grouping. ISPRS J Photogramm Remote Sens 79:106–121\nWang J, Howarth P (1990) Use of the Hough transform in automated lineament. IEEE Trans Geosci Remote Sens 28(4):561–567. doi:10.1109\u002FTGRS.1990.572949\nWladis D (1999) Automatic lineament detection using digital elevation models with second derivative filters. Photogramm Eng Remote Sens 65:453–458\nZhao X, Stein A, Chen X, Zhang X (2011) Quantification of extensional uncertainty of segmented image objects by random sets. IEEE Trans Geosci Remote Sens 49(7):2548–2557. doi:10.1109\u002FTGRS.2011.2109064\nZiou D, Tabbone S (1998) Edge detection techniques—an overview. Int J Pattern Recognit Image Anal 8:537–559",{"EN":571},"This paper presents the calibration and application of a Gestalt-based line segment method for automatic geological lineament detection from remote sensing images. This method involves estimation of the scale factor, the angle tolerance and a threshold on the false alarm rate. It identifies major lineaments as objects characterized by two edges on the image, which appear as transitions from dark to bright and vice versa. These objects were modelled as random sets with parameters drawn from their distributions. Following the geometry of detected segments, a novel validation method assesses the accuracy with respect to a linear vector reference. The methodology was applied to a study area in Kenya where lineaments are prominent in the landscape and are well identifiable from an ASTER image. Error rates were based on distance and local orientation, and the study showed that the existence and size of the objects were sensitive to parameter variation. False detection rate and missing detection rate were both equal to 0.50, which is better than values equal to 0.65 and 0.63, observed using the Canny edge detection. Modelling the uncertainty of geological lineaments with random sets further showed that no core set is formed, indicating that there is an inherent uncertainty in their existence and position, and that the variance is relatively high. Comparing the test area with four areas in the same region showed similar results. Despite some shortcomings in identifying full lineaments from partially observed lineaments, it is concluded that the procedure in this paper is well able to automatically extract lineaments from a remote sensing image and validate their existence.",{"EN":573},"Use of Gestalt Theory and Random Sets for Automatic Detection of Linear Geological Features",{"VOID":575},"10.1007\u002Fs11004-015-9584-z","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11004-015-9584-z",[578,593,610,622],{"id":579,"sortIndex":118,"researcher":18,"roles":580,"affiliations":581,"properties":590},"4159aa3b-aad0-4d70-9c7c-9505f13411e4",[287],[582],{"id":18,"sortIndex":19,"affiliation":583,"properties":18},{"id":584,"createTime":585,"updateTime":585,"relativeEntities":586,"slug":18,"properties":587,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"837f4ad9-fafd-4dac-8c6f-2d41c6044bca","2024-01-09T21:11:18.235+00:00",[],{"title":588},{"VI":589},"Faculty of Geoinformation Science and Earth Observation ITC, Enschede, The Netherlands",{"title":591},{"VI":592},"Alfred Stein",{"id":594,"sortIndex":136,"researcher":18,"roles":595,"affiliations":596,"properties":607},"45a3d6b8-12db-413f-9967-d280feb21c9e",[287],[597],{"id":18,"sortIndex":19,"affiliation":598,"properties":18},{"id":599,"createTime":600,"updateTime":601,"relativeEntities":602,"slug":603,"properties":604,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"468226aa-9fba-4e66-8b79-90dc9e5ddbbf","2023-12-29T18:37:39.137+00:00","2025-01-29T10:22:13.175+00:00",[],"School-of-Geosciences-University-of-the-Witwatersrand-Johannesburg-South-Africa",{"title":605},{"VI":606},"School of Geosciences, University of the Witwatersrand, Johannesburg, South Africa",{"title":608},{"VI":609},"Tsehaie Woldai",{"id":611,"sortIndex":219,"researcher":18,"roles":612,"affiliations":613,"properties":619},"00ef7e5d-3f58-4a3a-ba81-47fb429c4226",[287],[614],{"id":18,"sortIndex":19,"affiliation":615,"properties":18},{"id":584,"createTime":585,"updateTime":585,"relativeEntities":616,"slug":18,"properties":617,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":618},{"VI":589},{"title":620},{"VI":621},"Valentyn A. Tolpekin",{"id":623,"sortIndex":19,"researcher":18,"roles":624,"affiliations":625,"properties":631},"047c78f5-c3d1-4166-a1c0-1d8307aa027e",[287],[626],{"id":18,"sortIndex":19,"affiliation":627,"properties":18},{"id":584,"createTime":585,"updateTime":585,"relativeEntities":628,"slug":18,"properties":629,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":630},{"VI":589},{"title":632},{"VI":633},"Dafni Sidiropoulou Velidou",{"url":576,"publisher":635,"properties":662},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":636,"slug":10,"properties":637,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":640,"manageAffiliations":641,"indexDatabases":642,"url":18,"thumbnailPath":18,"statistic":657,"gsStatistic":18,"type":172,"analyzePriority":18},[],{"issn":638,"title":639},{"VOID":13},{"VOID":15},[],[],[643,650],{"id":64,"indexDatabase":644,"url":79,"indexYears":18,"academicFieldIds":649,"indexDatabaseRanking":18},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":645,"label":646,"description":647,"key":75,"publicationTags":648,"standard":18},[],{"EN":71,"VI":71},{"VI":73,"EN":74},[77,78],[81,82],{"id":84,"indexDatabase":651,"url":97,"indexYears":98,"academicFieldIds":656,"indexDatabaseRanking":102},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":652,"label":653,"description":654,"key":94,"publicationTags":655,"standard":18},[],{"EN":91,"VI":91},{"EN":91,"VI":93},[96],[100,101],{"impactFactor":19,"impactFactorByYear":658,"i10Index":115,"i10IndexLast5Year":48,"totalPublication":116,"totalPublicationByYear":659,"totalCitation":134,"totalCitationByYear":660,"totalCitationPerPublication":152,"totalCitationPerPublicationByYear":661,"hindexLast5Year":127,"hindex":127},{"2012":105,"2013":106,"2014":107,"2015":108,"2016":109,"2017":110,"2018":111,"2019":112,"2020":111,"2021":111,"2022":113,"2023":114},{"1997":118,"2007":119,"2008":120,"2009":121,"2010":122,"2011":123,"2012":124,"2013":125,"2014":126,"2015":121,"2016":127,"2017":128,"2018":123,"2019":129,"2020":124,"2021":130,"2022":131,"2023":132,"2024":133},{"1997":136,"2008":137,"2009":138,"2010":139,"2011":140,"2012":141,"2013":142,"2014":137,"2015":143,"2016":144,"2017":145,"2018":143,"2019":146,"2020":147,"2021":148,"2022":149,"2023":150,"2024":151},{"1997":154,"2008":155,"2009":156,"2010":157,"2011":158,"2012":159,"2013":160,"2014":161,"2015":162,"2016":163,"2017":164,"2018":165,"2019":166,"2020":167,"2021":168,"2022":169,"2023":170,"2024":171},{"volume":663,"pages":665},{"VOID":664},"47",{"VOID":666},"249-276","2015-02-28",2015,{"id":670,"createTime":671,"updateTime":672,"relativeEntities":673,"slug":674,"properties":675,"entityType":191,"verifyStatus":192,"verifyTime":684,"verifyNote":193,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":685,"fullTextUrl":18,"authors":686,"publicationType":234,"publisherRelationship":868,"citationCount":18,"citationInfo":18,"publishDate":899,"publishYear":360,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":266},"18e1e9aa-f5db-473a-9326-77ae827e90af","2024-01-11T19:56:39.303+00:00","2024-12-31T23:51:00.804+00:00",[],"A-Local-Parameterization-Based-Probabilistic-Cooperative-Coevolutionary-Algorithm-for-History-Matching",{"references":676,"abstract":678,"title":680,"doi":682},{"VOID":677},"Aanonsen SI, Nœvdal G, Oliver DS, Reynolds AC, Vallès B (2009) The ensemble Kalman filter in reservoir engineering—a review. SPE J 14(03):393–412\nChavent G, Dupuy M, Lemmonier P (1975) History matching by use of optimal theory. Soc Petrol Eng J 15(01):74–86\nChen Y, Oliver DS (2017) Localization and regularization for iterative ensemble smoothers. Comput Geosci 21(1):13–30. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10596-016-9599-7\nChen WH, Gavalas GR, Seinfeld JH, Wasserman ML (1974) A new algorithm for automatic history matching. Soc Petrol Eng J 14(06):593–608\nChen C, Gao G, Ramirez BA, Vink JC, Girardi AM (2016) Assisted history matching of channelized models by use of pluri-principal-component analysis. SPE J 21(05):1793–1812\nEmerick AA, Reynolds AC (2013) Ensemble smoother with multiple data assimilation. Comput Geosci 55:3–15. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.cageo.2012.03.011\nEvensen G, Van Leeuwen PJ (2000) An ensemble Kalman smoother for nonlinear dynamics. Mon Weather Rev 128(6):1852–1867\nFu J, Wen XH, Du S (2017) Efficient uncertainty quantification and history matching of large-scale fields through model reduction. Springer, Cham, pp 531–540. https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-3-319-46819-8_35\nGao G, Reynolds AC (2004) An improved implementation of the LBFGS algorithm for automatic history matching. In: SPE annual technical conference and exhibition. OnePetro\nGavalas G, Shah P, Seinfeld JH (1976) Reservoir history matching by Bayesian estimation. Soc Petrol Eng J 16(06):337–350\nHansen TM (2004) mgstat: a geostatistical matlab toolbox. Online web resource. http:\u002F\u002Fmgstatsourceforgenet\nHe J, Durlofsky LJ (2015) Constraint reduction procedures for reduced-order subsurface flow models based on POD-TPWL. Int J Numer Methods Eng 103(1):1–30. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fnme.4874\nJacquard P (1965) Permeability distribution from field pressure data. Soc Petrol Eng J 5(04):281–294\nJahns HO (1966) A rapid method for obtaining a two-dimensional reservoir description from well pressure response data. Soc Petrol Eng J 6(04):315–327\nJolliffe I (2005) Principal component analysis. Encycl Stat Behav Sci 30(3):487\nKarni E (2007) Foundations of Bayesian theory. J Econ Theory 132(1):167–188\nKumar D, Srinivasan S (2019) Ensemble-based assimilation of nonlinearly related dynamic data in reservoir models exhibiting non-Gaussian characteristics. Math Geosci 51(1):75–107. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11004-018-9762-x\nKumar D, Srinivasan S (2020) Indicator-based data assimilation with multiple-point statistics for updating an ensemble of models with non-gaussian parameter distributions. Adv Water Resour 141(103):611. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.advwatres.2020.103611\nLe DH, Emerick AA, Reynolds AC (2016) An adaptive ensemble smoother with multiple data assimilation for assisted history matching. SPE J 21(06):2195–2207. https:\u002F\u002Fdoi.org\u002F10.2118\u002F173214-pa\nLiu Y, Durlofsky LJ (2020) Multilevel strategies and geological parameterizations for history matching complex reservoir models. SPE J 25(01):081–104\nLuo X, Bhakta T, Nævdal G (2018) Correlation-based adaptive localization with applications to ensemble-based 4d-seismic history matching. SPE J 23(02):396–427. https:\u002F\u002Fdoi.org\u002F10.2118\u002F185936-pa\nMa W, Jafarpour B (2019) Integration of soft data into multiple-point statistical simulation: re-assessing the probability conditioning method for facies model calibration. Comput Geosci 23(4):683–703. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10596-019-9813-5\nMa X, Li X, Zhang Q, Tang K, Liang Z, Xie W, Zhu Z (2019) A survey on cooperative co-evolutionary algorithms. IEEE Trans Evol Comput 23(3):421–441. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTEVC.2018.2868770\nMa X, Zhang K, Zhang L, Wang Y, Wang H, Wang J, Yao J (2022) A distributed surrogate system assisted differential evolutionary algorithm for computationally expensive history matching problems. J Petrol Sci Eng 210(110):029\nMattax CC, Dalton RL (1990) Reservoir simulation (includes associated papers 21606 and 21620). J Petrol Technol 42(06):692–695\nMatthews JD, Carter JN, Stephen KD et al (2008) Assessing the effect of geological uncertainty on recovery estimates in shallow-marine reservoirs: the application of reservoir engineering to the SAIGUP project. Pet Geosci 14(1):35–44\nMunetomo M (2002) Linkage identification based on epistasis measures to realize efficient genetic algorithms. In: Proceedings of the 2002 congress on evolutionary computation. CEC’02 (Cat. No.02TH8600), vol 2, pp 1332–1337. https:\u002F\u002Fdoi.org\u002F10.1109\u002FCEC.2002.1004436\nOliver DS, Chen Y (2011) Recent progress on reservoir history matching: a review. Comput Geosci 15(1):185–221\nOmidvar MN, Kazimipour B, Li X, Yao X (2016) CBCC3—a contribution-based cooperative co-evolutionary algorithm with improved exploration\u002Fexploitation balance. In: 2016 IEEE congress on evolutionary computation (CEC). IEEE, pp 3541–3548\nOmidvar MN, Li X, Yao X (2010) Cooperative co-evolution with delta grouping for large scale non-separable function optimization. In: IEEE congress on evolutionary computation. IEEE, pp 1–8\nOmidvar MN, Li X, Yao X (2011) Smart use of computational resources based on contribution for cooperative co-evolutionary algorithms. In: Proceedings of the 13th annual conference on Genetic and evolutionary computation, pp 1115–1122\nPotter MA, Jong KAD (1994) A cooperative coevolutionary approach to function optimization. In: International conference on parallel problem solving from nature. Springer, pp 249–257\nPotter MA, Jong KAD (2000) Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol Comput 8(1):1–29\nRwechungura R, Dadashpour M, Kleppe J (2011) Advanced history matching techniques reviewed. In: SPE middle east oil and gas show and conference. OnePetro\nShi YJ, Teng HF, Li ZQ (2005) Cooperative co-evolutionary differential evolution for function optimization. In: International conference on natural computation. Springer, pp 1080–1088\nStorn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359\nTsuji M, Munetomo M (2008) Linkage analysis in genetic algorithms. Comput Intell Paradig 137:251–279\nVan Leeuwen PJ, Evensen G (1996) Data assimilation and inverse methods in terms of a probabilistic formulation. Mon Weather Rev 124(12):2898–2913\nVo HX, Durlofsky LJ (2015) Data assimilation and uncertainty assessment for complex geological models using a new PCA-based parameterization. Comput Geosci 19(4):747–767\nWall ME, Rechtsteiner A, Rocha LM (2003) Singular value decomposition and principal component analysis. Springer, Boston, pp 91–109. https:\u002F\u002Fdoi.org\u002F10.1007\u002F0-306-47815-3_5\nWen XH, Chen WH (2006) Real-time reservoir model updating using ensemble Kalman filter with confirming option. SPE J 11(04):431–442. https:\u002F\u002Fdoi.org\u002F10.2118\u002F92991-PA\nWen XH, Chen WH (2007) Some practical issues on real-time reservoir model updating using ensemble Kalman filter. SPE J 12(02):156–166. https:\u002F\u002Fdoi.org\u002F10.2118\u002F111571-PA\nWen XH, Deutsch CV, Cullick AS (1998) High-resolution reservoir models integrating multiple-well production data. SPE J 3(04):344–355. https:\u002F\u002Fdoi.org\u002F10.2118\u002F52231-PA\nWen X, Capilla J, Deutsch C, Gómez-Hernández JJ, Cullick AS (1999) A program to create permeability fields that honor single-phase flow rate and pressure data. Comput Geosci 25(3):217–230. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS0098-3004(98)00126-5\nWen XH, Lee S, Yu T (2006) Simultaneous integration of pressure, water cut,1 and 4-d seismic data in geostatistical reservoir modeling. Math Geol 38(3):301–325. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11004-005-9016-6\nXiao C, Tian L, Zhang L, Wang G, Deng Y (2020) Distributed gauss-newton optimization with smooth local parameterization for large-scale history-matching problems. SPE J 25(01):056–080\nXiao C, Lin HX, Leeuwenburgh O, Heemink A (2022) Surrogate-assisted inversion for large-scale history matching: comparative study between projection-based reduced-order modeling and deep neural network. J Petrol Sci Eng 208(109):287\nXiao C, Leeuwenburgh O, Lin HX, Heemink A (2019) Subdomain POD-TPWL with local parameterization for large-scale reservoir history matching problems. arXiv preprint arXiv:1901.08059\nYang Z, Tang K, Yao X (2008) Large scale evolutionary optimization using cooperative coevolution. Inf Sci 178(15):2985–2999\nZhao H, Kang Z, Zhang X, Sun H, Cao L, Reynolds AC (2016) A physics-based data-driven numerical model for reservoir history matching and prediction with a field application. SPE J 21(06):2175–2194\nZhao H, Rao X, Liu D, Xu Y, Zhan W, Peng X (2022) A flownet-based method for history matching and production prediction of shale or tight reservoirs with fracturing treatment. SPE J 27:2793–2819",{"EN":679},"History matching, as an essential part of reservoir development, aims to infer high-dimensional geological parameters of a reservoir with a small amount of observation. Despite the rapid development of optimization algorithms, finding optimal solutions for history matching is still challenging because of the large number of parameters that depend on the grid blocks of the numerical simulation model. Motivated by the divide-and-conquer strategy, in this work a novel probabilistic cooperative coevolutionary framework based on local parameterization (LP-PCC) is constructed to improve the convergence of the history matching of large-scale problems. First, the high-dimensional model parameters are decomposed based on smooth local parameterization, in which the divided low-dimensional parameters can reconstruct smooth boundaries of the geological structure during optimization. After that, a contribution-based cooperative coevolutionary algorithm is adopted to optimize the low-dimensional parameters in a round-robin fashion and allocate the computational resources reasonably. To further improve the performance of cooperative coevolution, a new probabilistic method integrated with contribution information is presented to select the subcomponents to be optimized. This framework incorporates domain knowledge for decomposition and a probabilistic mechanism to select subcomponents with large probability, which enhances both convergence and exploration in cooperative coevolution. Two synthetic reservoir cases are designed to validate the effectiveness and efficiency of the proposed method. The numerical results indicate that, compared with traditional strategies, the method can obtain better history-matching results and be adapted to large-scale reservoir problems.",{"EN":681},"A Local Parameterization-Based Probabilistic Cooperative Coevolutionary Algorithm for History Matching",{"VOID":683},"10.1007\u002Fs11004-023-10069-7","2024-12-31T23:51:00.803+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11004-023-10069-7",[687,702,719,736,762,775,787,802,819,834,846],{"id":688,"sortIndex":119,"researcher":18,"roles":689,"affiliations":690,"properties":699},"d9f66d19-0dba-4140-a2e3-018adcf4e937",[287],[691],{"id":18,"sortIndex":19,"affiliation":692,"properties":18},{"id":693,"createTime":694,"updateTime":694,"relativeEntities":695,"slug":18,"properties":696,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"9716a5cc-e211-43b6-a95a-4020ff952d74","2023-12-26T07:19:56.069+00:00",[],{"title":697},{"VI":698},"College of Petroleum Engineering, Xi’an Shiyou University, Xi’an, China",{"title":700},{"VI":701},"Xiaopeng Ma",{"id":703,"sortIndex":60,"researcher":18,"roles":704,"affiliations":705,"properties":716},"86811c07-9978-4ff9-863e-3cb8d7597e1a",[287],[706],{"id":18,"sortIndex":19,"affiliation":707,"properties":18},{"id":708,"createTime":709,"updateTime":710,"relativeEntities":711,"slug":712,"properties":713,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"b18dfbed-0722-4670-8172-53db6814b16d","2024-01-09T20:19:52.822+00:00","2024-11-26T15:25:04.346+00:00",[],"School-of-Petroleum-Engineering-China-University-of-Petroleum-East-China-Qingdao-China",{"title":714},{"VI":715},"School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, China",{"title":717},{"VI":718},"Chuanjin Yao",{"id":720,"sortIndex":151,"researcher":18,"roles":721,"affiliations":722,"properties":733},"527dd25d-2ca8-4815-b77f-b4ea529027f6",[287],[723],{"id":18,"sortIndex":19,"affiliation":724,"properties":18},{"id":725,"createTime":726,"updateTime":727,"relativeEntities":728,"slug":729,"properties":730,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"4217ecbe-2ec6-430b-b852-b13f5484a750","2023-12-05T10:51:46.825+00:00","2024-10-11T21:09:45.907+00:00",[],"College-of-Control-Science-and-Engineering-China-University-of-Petroleum-East-China-Qingdao-China",{"title":731},{"VI":732},"College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China",{"title":734},{"VI":735},"Weifeng Liu",{"id":737,"sortIndex":738,"researcher":18,"roles":739,"affiliations":740,"properties":759},"718a8081-9bac-4b3a-8095-7b631a2ea4d9",7,[287],[741,751],{"id":742,"sortIndex":219,"affiliation":743,"properties":750},"21336f38-7178-4279-a431-e5a89f31d5b6",{"id":744,"createTime":745,"updateTime":745,"relativeEntities":746,"slug":18,"properties":747,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"0fcb84d5-3e04-4bbe-b4d5-1c4923101e9c","2023-12-13T08:40:56.121+00:00",[],{"title":748},{"VI":749},"CNOOC Research Institute Ltd., Beijing, China",{},{"id":18,"sortIndex":19,"affiliation":752,"properties":18},{"id":753,"createTime":754,"updateTime":754,"relativeEntities":755,"slug":18,"properties":756,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"a85fe091-a953-43a8-acef-2ce18b9ad4c3","2024-01-21T07:44:10.703+00:00",[],{"title":757},{"VI":758},"State Key Laboratory of Offshore Oil Exploitation, Beijing, China",{"title":760},{"VI":761},"Chen Liu",{"id":763,"sortIndex":764,"researcher":18,"roles":765,"affiliations":766,"properties":772},"722c5734-4ce8-4b04-b222-41855f5243f2",10,[287],[767],{"id":18,"sortIndex":19,"affiliation":768,"properties":18},{"id":708,"createTime":709,"updateTime":710,"relativeEntities":769,"slug":712,"properties":770,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":771},{"VI":715},{"title":773},{"VI":774},"Jun Yao",{"id":776,"sortIndex":48,"researcher":18,"roles":777,"affiliations":778,"properties":784},"70d2fc2f-6fd2-4f57-bf17-ff29bd5495eb",[287],[779],{"id":18,"sortIndex":19,"affiliation":780,"properties":18},{"id":708,"createTime":709,"updateTime":710,"relativeEntities":781,"slug":712,"properties":782,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":783},{"VI":715},{"title":785},{"VI":786},"Yongfei Yang",{"id":788,"sortIndex":118,"researcher":18,"roles":789,"affiliations":790,"properties":799},"70fe2bca-4dfb-453e-a861-706e1ee94f5a",[287],[791],{"id":18,"sortIndex":19,"affiliation":792,"properties":18},{"id":793,"createTime":794,"updateTime":794,"relativeEntities":795,"slug":18,"properties":796,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"907894d9-3b70-4b18-8055-780df57e97c2","2024-01-11T19:56:39.358+00:00",[],{"title":797},{"VI":798},"CNPC Offshore Engineering Company Ltd., Beijing, China",{"title":800},{"VI":801},"Zihao Zhao",{"id":803,"sortIndex":804,"researcher":18,"roles":805,"affiliations":806,"properties":816},"be225c07-a7ac-4649-9f33-8b211ccf94a6",6,[287],[807],{"id":18,"sortIndex":19,"affiliation":808,"properties":18},{"id":809,"createTime":810,"updateTime":810,"relativeEntities":811,"slug":812,"properties":813,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"e2943204-93c1-40ae-86ea-4e966f0e1c25","2024-04-21T18:09:40.039+00:00",[],"College-of-Science-China-University-of-Petroleum-East-China-Qingdao-China",{"title":814},{"EN":815},"College of Science, China University of Petroleum (East China), Qingdao, China",{"title":817},{"VI":818},"Jian Wang",{"id":820,"sortIndex":219,"researcher":18,"roles":821,"affiliations":822,"properties":831},"1d47b4cd-f42e-40ed-a32f-7e74bf6d4a07",[287],[823],{"id":18,"sortIndex":19,"affiliation":824,"properties":18},{"id":825,"createTime":826,"updateTime":826,"relativeEntities":827,"slug":18,"properties":828,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"45df275a-1b07-43bb-b030-6192c3081c85","2024-01-11T19:56:39.345+00:00",[],{"title":829},{"VI":830},"Chuanzhong Oil & Gas Mines, PetroChina Southwest Oil & Gasfield Company, Suining, China",{"title":832},{"VI":833},"Xin Guo",{"id":835,"sortIndex":19,"researcher":18,"roles":836,"affiliations":837,"properties":843},"74d2eab5-4760-4b04-97bb-be7f539d4f13",[287],[838],{"id":18,"sortIndex":19,"affiliation":839,"properties":18},{"id":708,"createTime":709,"updateTime":710,"relativeEntities":840,"slug":712,"properties":841,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":842},{"VI":715},{"title":844},{"VI":845},"Jinding Zhang",{"id":847,"sortIndex":136,"researcher":18,"roles":848,"affiliations":849,"properties":865},"eadc7168-b79c-4d47-bf72-8914d4243419",[287],[850,860],{"id":851,"sortIndex":219,"affiliation":852,"properties":859},"d1c9461c-3ed7-46e0-86c5-3b4a6d1e5c41",{"id":853,"createTime":854,"updateTime":854,"relativeEntities":855,"slug":18,"properties":856,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"239ff510-98b2-4f48-b32c-1fee92fdfa99","2024-01-18T15:31:39.211+00:00",[],{"title":857},{"VI":858},"Civil Engineering School, Qingdao University of Technology, Qingdao, China",{},{"id":18,"sortIndex":19,"affiliation":861,"properties":18},{"id":708,"createTime":709,"updateTime":710,"relativeEntities":862,"slug":712,"properties":863,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":864},{"VI":715},{"title":866},{"VI":867},"Kai Zhang",{"url":685,"publisher":869,"properties":896},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":870,"slug":10,"properties":871,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":874,"manageAffiliations":875,"indexDatabases":876,"url":18,"thumbnailPath":18,"statistic":891,"gsStatistic":18,"type":172,"analyzePriority":18},[],{"issn":872,"title":873},{"VOID":13},{"VOID":15},[],[],[877,884],{"id":64,"indexDatabase":878,"url":79,"indexYears":18,"academicFieldIds":883,"indexDatabaseRanking":18},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":879,"label":880,"description":881,"key":75,"publicationTags":882,"standard":18},[],{"EN":71,"VI":71},{"VI":73,"EN":74},[77,78],[81,82],{"id":84,"indexDatabase":885,"url":97,"indexYears":98,"academicFieldIds":890,"indexDatabaseRanking":102},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":886,"label":887,"description":888,"key":94,"publicationTags":889,"standard":18},[],{"EN":91,"VI":91},{"EN":91,"VI":93},[96],[100,101],{"impactFactor":19,"impactFactorByYear":892,"i10Index":115,"i10IndexLast5Year":48,"totalPublication":116,"totalPublicationByYear":893,"totalCitation":134,"totalCitationByYear":894,"totalCitationPerPublication":152,"totalCitationPerPublicationByYear":895,"hindexLast5Year":127,"hindex":127},{"2012":105,"2013":106,"2014":107,"2015":108,"2016":109,"2017":110,"2018":111,"2019":112,"2020":111,"2021":111,"2022":113,"2023":114},{"1997":118,"2007":119,"2008":120,"2009":121,"2010":122,"2011":123,"2012":124,"2013":125,"2014":126,"2015":121,"2016":127,"2017":128,"2018":123,"2019":129,"2020":124,"2021":130,"2022":131,"2023":132,"2024":133},{"1997":136,"2008":137,"2009":138,"2010":139,"2011":140,"2012":141,"2013":142,"2014":137,"2015":143,"2016":144,"2017":145,"2018":143,"2019":146,"2020":147,"2021":148,"2022":149,"2023":150,"2024":151},{"1997":154,"2008":155,"2009":156,"2010":157,"2011":158,"2012":159,"2013":160,"2014":161,"2015":162,"2016":163,"2017":164,"2018":165,"2019":166,"2020":167,"2021":168,"2022":169,"2023":170,"2024":171},{"pages":897},{"VOID":898},"1-30","2023-07-29",{"id":901,"createTime":902,"updateTime":902,"relativeEntities":903,"slug":18,"properties":904,"entityType":191,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":913,"fullTextUrl":18,"authors":914,"publicationType":234,"publisherRelationship":943,"citationCount":18,"citationInfo":18,"publishDate":976,"publishYear":264,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":266},"e9810594-c2ff-4ea0-b5e2-e6cfbac66ac2","2024-02-11T23:50:34.706+00:00",[],{"references":905,"abstract":907,"title":909,"doi":911},{"VOID":906},"Aanonsen S, Nævdal G, Oliver D, Reynolds A, Vallès B (2009) The ensemble Kalman filter in reservoir engineering—a review. SPE J 14(3):393–412\nAghasi A, Mendoza-Sanchez I, Miller EL, Ramsburg CA, Abriola LM (2013) A geometric approach to joint inversion with applications to contaminant source zone characterization. Inverse Prob 29(11):115014. https:\u002F\u002Fdoi.org\u002F10.1088\u002F0266-5611\u002F29\u002F11\u002F115014\nAla NK, Domenico PA (1992) Inverse analytical techniques applied to coincident contaminant distributions at Otis Air Force Base, Massachusetts. Groundwater 30(2):212–218. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1745-6584.1992.tb01793.x\nAmirabdollahian M, Datta B (2013) Identification of contaminant source characteristics and monitoring network design in groundwater aquifers: an overview. J Environ Protect. https:\u002F\u002Fdoi.org\u002F10.4236\u002Fjep.2013.45a004\nAral MM, Guan J (1996) Genetic algorithms in search of groundwater pollution sources. In: Advances in groundwater pollution control and remediation. Springer, Netherlands, Dordrecht, pp 347–369. https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-94-009-0205-3_17\nAyvaz MT (2016) A hybrid simulation–optimization approach for solving the areal groundwater pollution source identification problems. J Hydrol 538:161–176. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jhydrol.2016.04.008\nBagtzoglou AC, Tompson AFB, Dougherty DE (1991) Probabilistic simulation for reliable solute source identification in heterogeneous porous media. In: Water resources engineering risk assessment. Springer, Berlin, pp 189–201. https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-3-642-76971-9_12\nBagtzoglou AC, Dougherty DE, Tompson AFB (1992) Application of particle methods to reliable identification of groundwater pollution sources. Water Resour Manag 6(1):15–23. https:\u002F\u002Fdoi.org\u002F10.1007\u002FBF00872184\nCao T, Zeng X, Wu J, Wang D, Sun Y, Zhu X, Lin J, Long Y (2019) Groundwater contaminant source identification via Bayesian model selection and uncertainty quantification. Hydrogeol J 27(8):2907–2918. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10040-019-02055-3\nCapilla JE, Rodrigo J, Gómez-Hernández JJ (1999) Simulation of non-Gaussian transmissivity fields honoring piezometric data and integrating soft and secondary information. Math Geosci 31(7):907–927\nCarrera J (1984) Estimation of aquifer parameters under transient and steady-state conditions. PhD thesis, University of Arizona, Department of Hydrology and Water Resources\nCarrera J, Neuman SP (1986) Estimation of aquifer parameters under transient and steady state conditions. 1. Maximum likelihood method incorporating prior information. Water Resour Res 22(2):199–210\nChen Y, Zhang D (2006) Data assimilation for transient flow in geologic formations via ensemble Kalman filter. Adv Water Resour 29(8):1107–1122\nDagan G (1982) Stochastic modeling of groundwater flow by unconditional and conditional probabilities: 2. The solute transport. Water Resour Res 18(4):835–848\nDatta B, Beegle J, Kavvas M, Orlob G (1989) Development of an expert system embedding pattern recognition techniques for pollution source identification. University of California-Davis, Technical report\nEvensen G (2003) The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn 53(4):343–367\nGómez-Hernández J, Wen XH (1994) Probabilistic assessment of travel times in groundwater modeling. Stoch Hydrol Hydraul 8(1):19–55\nGorelick SM (1981) Numerical management models of groundwater pollution. Ph.D., Stanford University\nGorelick SM, Evans B, Remson I (1983) Identifying sources of groundwater pollution: an optimization approach. Water Resour Res 19(3):779–790. https:\u002F\u002Fdoi.org\u002F10.1029\u002FWR019i003p00779\nHaario H, Laine M, Mira A, Saksman E (2006) Dram: efficient adaptive McMC. Statist Comput 16(4):339–354\nHosseini AH, Deutsch CV, Mendoza CA, Biggar KW (2011) Inverse modeling for characterization of uncertainty in transport parameters under uncertainty of source geometry in heterogeneous aquifers. J Hydrol 405(3–4):402–416. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jhydrol.2011.05.039\nHwang JC, Koerner RM (1983) Groundwater pollution source identification from limited monitoring data. Part 1—theory and feasibility. J Hazard Mater 8:105–119\nJha MK, Datta B (2014) Linked simulation–optimization based dedicated monitoring network design for unknown pollutant source identification using dynamic time warping distance. Water Resour Manag 28(12):4161–4182. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11269-014-0737-5\nJin X, Ranjithan RS, Mahinthakumar GK (2014) A monitoring network design procedure for three-dimensional (3D) groundwater contaminant source identification. Environ Forensics 15(1):78–96. https:\u002F\u002Fdoi.org\u002F10.1080\u002F15275922.2013.873095\nLi L, Zhou H, Franssen H, Gómez-Hernández J (2011) Groundwater flow inverse modeling in non-multigaussian media: performance assessment of the normal-score ensemble kalman filter. Hydrol Earth Syst Sci Discuss 8(4):6749–6788\nLi L, Zhou H, Gómez-Hernández JJ (2011) A comparative study of three-dimensional hydraulic conductivity upscaling at the macro-dispersion experiment (made) site, Columbus Air Force Base, Mississippi (USA). J Hydrol 404(3–4):278–293\nLi L, Zhou H, Gómez-Hernández J, Hendricks Franssen H (2012) Jointly mapping hydraulic conductivity and porosity by assimilating concentration data via ensemble Kalman filter. J Hydrol 428:152\nMahinthakumar GK, Sayeed M (2005) Hybrid genetic algorithm-local search methods for solving groundwater source identification inverse problems. J Water Resourc Plan Manag 131(1):45–57. https:\u002F\u002Fdoi.org\u002F10.1061\u002F(asce)0733-9496(2005)131:1(45)\nMahinthakumar GK, Sayeed M (2006) Reconstructing groundwater source release histories using hybrid optimization approaches. Environ Forensics 7(1):45–54. https:\u002F\u002Fdoi.org\u002F10.1080\u002F15275920500506774\nMirghani BY, Mahinthakumar KG, Tryby ME, Ranjithan RS, Zechman EM (2009) A parallel evolutionary strategy based simulation–optimization approach for solving groundwater source identification problems. Adv Water Resour 32(9):1373–1385. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.advwatres.2009.06.001\nSingh RM, Datta B (2004) Groundwater pollution source identification and simultaneous parameter estimation using pattern matching by artificial neural network. Environ Forensics 5(3):143–153. https:\u002F\u002Fdoi.org\u002F10.1080\u002F15275920490495873\nSingh RM, Datta B, Jain A (2004) Identification of unknown groundwater pollution sources using artificial neural networks. J Water Resour Plan Manag 130(6):506–514. https:\u002F\u002Fdoi.org\u002F10.1061\u002F(ASCE)0733-9496(2004)130:6(506)\nSkaggs TH, Kabala ZJ (1994) Recovering the release history of a groundwater contaminant. Water Resour Res 30(1):71–79. https:\u002F\u002Fdoi.org\u002F10.1029\u002F93WR02656\nSnodgrass MF, Kitanidis PK (1997) A geostatistical approach to contaminant source identification. Water Resour Res 33(4):537–546. https:\u002F\u002Fdoi.org\u002F10.1029\u002F96WR03753\nTarantola A (2005) Inverse problem theory and methods for model parameter estimation. SIAM, Philadelphia\nTodaro, D’Oria M, Tanda MG, Gómez-Hernández JJ (2021) Ensemble smoother with multiple data assimilation to simultaneously estimate the source location and the release history of a contaminant spill in an aquifer. J Hydrol. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jhydrol.2021.126215\nWagner BJ (1992) Simultaneous parameter estimation and contaminant source characterization for coupled groundwater flow and contaminant transport modelling. J Hydrol 135(1–4):275–303. https:\u002F\u002Fdoi.org\u002F10.1016\u002F0022-1694(92)90092-A\nWasilkowski GW, Wozniakowski H (1995) Explicit cost bounds of algorithms for multivariate tensor product problems. J Complex 11(1):1–56\nWoodbury A, Sudicky E, Ulrych TJ, Ludwig R (1998) Three-dimensional plume source reconstruction using minimum relative entropy inversion. J Contam Hydrol 32(1–2):131–158. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS0169-7722(97)00088-0\nWoodbury AD, Ulrych TJ (1996) Minimum relative entropy inversion: theory and application to recovering the release history of a groundwater contaminant. Water Resour Res 32(9):2671–2681. https:\u002F\u002Fdoi.org\u002F10.1029\u002F95WR03818\nXu T, Gómez-Hernández JJ (2016) Joint identification of contaminant source location, initial release time, and initial solute concentration in an aquifer via ensemble Kalman filtering. Water Resour Res 52(8):6587–6595. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2016WR019111\nXu T, Gómez-Hernández JJ (2018) Simultaneous identification of a contaminant source and hydraulic conductivity via the restart normal-score ensemble Kalman filter. Adv Water Resour 112:106–123. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.advwatres.2017.12.011\nXu T, Gómez-Hernández JJ, Zhou H, Li L (2013) The power of transient piezometric head data in inverse modeling: an application of the localized normal-score EnKF with covariance inflation in a heterogenous bimodal hydraulic conductivity field. Adv Water Resour 54:100–118. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.advwatres.2013.01.006\nXu T, Jaime Gómez-Hernández J, Li L, Zhou H (2013) Parallelized ensemble Kalman filter for hydraulic conductivity characterization. Comput Geosci 52:42–49\nYeh HD, Lin CC, Chen CF (2016) Reconstructing the release history of a groundwater contaminant based on AT123D. J Hydro-Environ Res 13:89–102. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jher.2015.06.001\nZeng L, Shi L, Zhang D, Wu L (2012) A sparse grid based Bayesian method for contaminant source identification. Adv Water Resour 37:1–9. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.advwatres.2011.09.011\nZhou H, Gómez-Hernández JJ, Li L (2014) Inverse methods in hydrogeology: evolution and recent trends. Adv Water Resour 63:22–37\nZhou Z, Tartakovsky DM (2021) Markov chain Monte Carlo with neural network surrogates: application to contaminant source identification. Stoch Environ Res Risk Assess 35(3):639–651",{"EN":908},"Forty years and 157 papers later, research on contaminant source identification has grown exponentially in number but seems to be stalled concerning advancement towards the problem solution and its field application. This paper presents a historical evolution of the subject, highlighting its major advances. It also shows how the subject has grown in sophistication regarding the solution of the core problem (the source identification), forgetting that, from a practical point of view, such identification is worthless unless it is accompanied by a joint identification of the other uncertain parameters that characterize flow and transport in aquifers.",{"EN":910},"Contaminant Source Identification in Aquifers: A Critical View",{"VOID":912},"10.1007\u002Fs11004-021-09976-4","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11004-021-09976-4",[915,926],{"id":916,"sortIndex":19,"researcher":18,"roles":917,"affiliations":918,"properties":924},"d6f898f8-a1dc-4585-85b2-c5655c49c307",[287],[919],{"id":18,"sortIndex":19,"affiliation":920,"properties":18},{"id":204,"createTime":205,"updateTime":206,"relativeEntities":921,"slug":208,"properties":922,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":923},{"VI":211},{"title":925},{"VI":214},{"id":927,"sortIndex":219,"researcher":18,"roles":928,"affiliations":929,"properties":940},"b01090db-d874-4ad0-b168-27a1c2c5428e",[287],[930],{"id":18,"sortIndex":19,"affiliation":931,"properties":18},{"id":932,"createTime":933,"updateTime":934,"relativeEntities":935,"slug":936,"properties":937,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"19e2cb7f-061e-4a22-88ac-8df8cd50b8cf","2023-12-22T12:36:07.166+00:00","2024-12-03T05:25:19.374+00:00",[],"State-Key-Laboratory-of-Hydrology-Water-Resources-and-Hydraulic-Engineering-Hohai-University-Nanjing-China",{"title":938},{"VI":939},"State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China",{"title":941},{"VI":942},"Teng Xu",{"url":913,"publisher":944,"properties":971},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":945,"slug":10,"properties":946,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":949,"manageAffiliations":950,"indexDatabases":951,"url":18,"thumbnailPath":18,"statistic":966,"gsStatistic":18,"type":172,"analyzePriority":18},[],{"issn":947,"title":948},{"VOID":13},{"VOID":15},[],[],[952,959],{"id":64,"indexDatabase":953,"url":79,"indexYears":18,"academicFieldIds":958,"indexDatabaseRanking":18},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":954,"label":955,"description":956,"key":75,"publicationTags":957,"standard":18},[],{"EN":71,"VI":71},{"VI":73,"EN":74},[77,78],[81,82],{"id":84,"indexDatabase":960,"url":97,"indexYears":98,"academicFieldIds":965,"indexDatabaseRanking":102},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":961,"label":962,"description":963,"key":94,"publicationTags":964,"standard":18},[],{"EN":91,"VI":91},{"EN":91,"VI":93},[96],[100,101],{"impactFactor":19,"impactFactorByYear":967,"i10Index":115,"i10IndexLast5Year":48,"totalPublication":116,"totalPublicationByYear":968,"totalCitation":134,"totalCitationByYear":969,"totalCitationPerPublication":152,"totalCitationPerPublicationByYear":970,"hindexLast5Year":127,"hindex":127},{"2012":105,"2013":106,"2014":107,"2015":108,"2016":109,"2017":110,"2018":111,"2019":112,"2020":111,"2021":111,"2022":113,"2023":114},{"1997":118,"2007":119,"2008":120,"2009":121,"2010":122,"2011":123,"2012":124,"2013":125,"2014":126,"2015":121,"2016":127,"2017":128,"2018":123,"2019":129,"2020":124,"2021":130,"2022":131,"2023":132,"2024":133},{"1997":136,"2008":137,"2009":138,"2010":139,"2011":140,"2012":141,"2013":142,"2014":137,"2015":143,"2016":144,"2017":145,"2018":143,"2019":146,"2020":147,"2021":148,"2022":149,"2023":150,"2024":151},{"1997":154,"2008":155,"2009":156,"2010":157,"2011":158,"2012":159,"2013":160,"2014":161,"2015":162,"2016":163,"2017":164,"2018":165,"2019":166,"2020":167,"2021":168,"2022":169,"2023":170,"2024":171},{"volume":972,"pages":974},{"VOID":973},"54",{"VOID":975},"437-458","2021-09-27",{"id":978,"createTime":979,"updateTime":980,"relativeEntities":981,"slug":982,"properties":983,"entityType":191,"verifyStatus":192,"verifyTime":992,"verifyNote":193,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":993,"fullTextUrl":18,"authors":994,"publicationType":234,"publisherRelationship":1063,"citationCount":18,"citationInfo":18,"publishDate":1096,"publishYear":1097,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":266},"47538b81-cae8-472e-975d-80b58600a671","2023-12-11T03:40:02.462+00:00","2025-01-07T23:49:31.274+00:00",[],"An-Improved-Parallel-Multiple-point-Algorithm-Using-a-List-Approach",{"references":984,"abstract":986,"title":988,"doi":990},{"VOID":985},"Arpat G, Caers J (2007) Conditional simulation with patterns. Math Geol 39(2):177–203\nCaers J (2005) Petroleum geostatistics. Society of Petroleum Engineers, Richardson\nCaers J, Strebelle S, Payrazyan K (2003) Stochastic integration of seismic data and geologic scenarios: a west Africa submarine channel saga. Lead Edge 22(3):192–196\nChugunova T, Hu L (2008) Multiple-point statistical simulations constrained by continuous auxiliary data. Math Geosci 40(2):133–146\nDaly C, Caers J (2010) Multipoint geostatistics—an introductory review. First Break 28(9). doi:10.3997\u002F1365-2397.2010020\nDaly C, Knudby C (2007) Multipoint statistics in reservoir modelling and in computer vision. In: Petroleum geostatistics 2007, EAGE, Cascais, Portugal\nde Vries LM, Carrera J, Falivene O, Gratacos O, Luit JS (2009) Application of multiple point geostatistics to non-stationary images. Math Geosci 41(1):29–42\nDongarra J, Huss-Lederman S, Otto S, Snir M, Walker D (1998) MPI—the complete reference: the MPI core, 2nd edn, vol. 1. MIT Press, Cambridge\nGropp W, Huss-Lederman S, Lumsdaine A, Lusk E, Nitzberg B, Saphir W, Snir M (1998) MPI—the complete reference: the MPI extensions, vol 2. MIT Press, Cambridge\nGuardiano F, Strivastava R (1993) Multivariate geostatistics: beyond bivariate moments. In: Soares A (ed) Geostatistics Troia, vol 1. Kluwer Academic, Dordrecht, pp 133–144\nHu L, Chugunova T (2008) Multiple-point geostatistics for modeling subsurface heterogeneity: a comprehensive review. Water Resour Res 44:W11413\nJournel A, Zhang T (2006) Necessity of a multiple-point prior model. Math Geol 38(5):591–610\nLiu Y (2006) Using the snesim program for multiple-point statistical simulation. Comput Geosci 32:1544–1563\nLiu Y, Harding A, Abriel W, Strebelle S (2004) Multiple-point simulation integrating wells, three-dimensional seismic data, and geology. Am Assoc Pet Geol Bull 88(7):905–921\nMariethoz G, Renard P, Straubhaar J (2009) The direct sampling method to perform multiple-points geostatistical simulations. Water Resour Res (submitted)\nOkabe H, Blunt MJ (2007) Pore space reconstruction of vuggy carbonates using microtomography and multiple-point statistics. Water Resour Res 43:W12S02\nRemy N, Boucher A, Wu J (2009) Applied geostatistics with SGeMS: a user’s guide. Cambridge University Press, New York\nRenard P (2007) Stochastic hydrogeology: what professionals really need? Ground Water 45(5):531–541\nRivoirard J, Cojan I, Renard D, Geffroy F (2008) Advances in quantification of process–based models for meandering channelized reservoirs. In: Ortiz J, Emery X (eds) VIII international geostatistics congress, GEOSTATS 2008, Santiago, Chile\nRonayne M, Gorelick S, Caers J (2008) Identifying discrete geologic structures that produce anomalous hydraulic response: an inverse modeling approach. Water Resour Res 44:8\nStien M, Hauge R, Kolbjørnsen O, Abrahamsen P (2007) Modification of the snesim algorithm. In: Petroleum geostatistics 2007, EAGE, Cascais, Portugal\nStrebelle S (2002) Conditional simulation of complex geological structures using multiple-points statistics. Math Geol 34(1):1–21\nStrebelle S, Remy N (2005) Post-processing of multiple-point geostatistical models to improve reproduction of training patterns. In: Leuangthong O, Deutsch C (eds) Geostatistics Banff 2004. Springer, Berlin, pp 979–988\nSuzuki S, Strebelle S (2007) Real–time post–processing method to enhance multiple-point statistics simulation. In: Petroleum geostatistics 2007, EAGE, Cascais, Portugal\nTran TT (1994) Improving variogram reproduction on dense simulation grids. Comput Geosci 20(7):1161–1168\nVargas H, Caetano H, Mata-Lima H (2008) A new parallelization approach for sequential simulation. In: Soares A, Pereira MJ, Dimitrakopoulos R (eds) geoENV VI geostatistics for environmental applications. Springer, Berlin, pp 489–496\nWu J, Zhang T, Journel A (2008) Fast filtersim simulation with score-based distance. Math Geosci 40(7):773–788\nZhang T, Switzer P, Journel AG (2006) Filter-based classification of training image patterns for spatial simulation. Math Geol 38(1):63–80\nZhang T, Pedersen SI, McCormick D (2008) Patched path and recursive servo system in multiple-point geostatistics simulation. In: Proceedings of the eighth international geostatistics congress, vol 2, pp 1119–1124. Gecamin, Santiago",{"EN":987},"Among the techniques used to simulate categorical variables, multiple-point statistics is becoming very popular because it allows the user to provide an explicit conceptual model via a training image. In classic implementations, the multiple-point statistics are inferred from the training image by storing all the observed patterns of a certain size in a tree structure. This type of algorithm has the advantage of being fast to apply, but it presents some critical limitations. In particular, a tree is extremely RAM demanding. For three-dimensional problems with numerous facies, large templates cannot be used. Complex structures are then difficult to simulate. In this paper, we propose to replace the tree by a list. This structure requires much less RAM. It has three main advantages. First, it allows for the use of larger templates. Second, the list structure being parsimonious, it can be extended to include additional information. Here, we show how this can be used to develop a new approach for dealing with non-stationary training images. Finally, an interesting aspect of the list is that it allows one to parallelize the part of the algorithm in which the conditional probability density function is computed. This is especially important for large problems that can be solved on clusters of PCs with distributed memory or on multicore machines with shared memory.",{"EN":989},"An Improved Parallel Multiple-point Algorithm Using a List Approach",{"VOID":991},"10.1007\u002Fs11004-011-9328-7","2025-01-07T23:49:31.273+00:00","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs11004-011-9328-7",[995,1010,1021,1036,1051],{"id":996,"sortIndex":19,"researcher":18,"roles":997,"affiliations":998,"properties":1007},"c5e52f85-9031-47d7-ab73-83e0e9e2bddb",[287],[999],{"id":18,"sortIndex":19,"affiliation":1000,"properties":18},{"id":1001,"createTime":1002,"updateTime":1002,"relativeEntities":1003,"slug":18,"properties":1004,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"4a8e7a5e-30b5-4c23-9a3a-3ebc8c87f397","2024-01-25T16:27:17.245+00:00",[],{"title":1005},{"VI":1006},"Centre of Hydrogeology and Geothermics (CHYN), University of Neuchâtel, Neuchâtel, Switzerland",{"title":1008},{"VI":1009},"Julien Straubhaar",{"id":1011,"sortIndex":118,"researcher":18,"roles":1012,"affiliations":1013,"properties":1019},"88835965-bde4-4736-b5e6-3967cb016952",[287],[1014],{"id":18,"sortIndex":19,"affiliation":1015,"properties":18},{"id":1001,"createTime":1002,"updateTime":1002,"relativeEntities":1016,"slug":18,"properties":1017,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1018},{"VI":1006},{"title":1020},{"VI":481},{"id":1022,"sortIndex":136,"researcher":18,"roles":1023,"affiliations":1024,"properties":1033},"cc900ac2-0c54-479c-96e9-545854f62999",[287],[1025],{"id":18,"sortIndex":19,"affiliation":1026,"properties":18},{"id":1027,"createTime":1028,"updateTime":1028,"relativeEntities":1029,"slug":18,"properties":1030,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"b307a6b7-c2a7-4a9e-8d98-0cb25179e076","2023-12-11T03:40:02.524+00:00",[],{"title":1031},{"VI":1032},"Ephesia Consult SA, Geneva, Switzerland",{"title":1034},{"VI":1035},"Roland Froidevaux",{"id":1037,"sortIndex":119,"researcher":18,"roles":1038,"affiliations":1039,"properties":1048},"f3b592be-7e88-4fc3-81d6-8b1d2edc6fc3",[287],[1040],{"id":18,"sortIndex":19,"affiliation":1041,"properties":18},{"id":1042,"createTime":1043,"updateTime":1043,"relativeEntities":1044,"slug":18,"properties":1045,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"8c4e6d3f-5169-4a79-9627-af5affbd9e0e","2023-12-11T03:40:02.537+00:00",[],{"title":1046},{"VI":1047},"Institute of Mathematics, University of Neuchâtel, Neuchâtel, Switzerland",{"title":1049},{"VI":1050},"Olivier Besson",{"id":1052,"sortIndex":219,"researcher":18,"roles":1053,"affiliations":1054,"properties":1060},"25dd11ec-0a85-4447-af1a-d63aef0f0e1b",[287],[1055],{"id":18,"sortIndex":19,"affiliation":1056,"properties":18},{"id":1001,"createTime":1002,"updateTime":1002,"relativeEntities":1057,"slug":18,"properties":1058,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1059},{"VI":1006},{"title":1061},{"VI":1062},"Philippe Renard",{"url":993,"publisher":1064,"properties":1091},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1065,"slug":10,"properties":1066,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":1069,"manageAffiliations":1070,"indexDatabases":1071,"url":18,"thumbnailPath":18,"statistic":1086,"gsStatistic":18,"type":172,"analyzePriority":18},[],{"issn":1067,"title":1068},{"VOID":13},{"VOID":15},[],[],[1072,1079],{"id":64,"indexDatabase":1073,"url":79,"indexYears":18,"academicFieldIds":1078,"indexDatabaseRanking":18},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":1074,"label":1075,"description":1076,"key":75,"publicationTags":1077,"standard":18},[],{"EN":71,"VI":71},{"VI":73,"EN":74},[77,78],[81,82],{"id":84,"indexDatabase":1080,"url":97,"indexYears":98,"academicFieldIds":1085,"indexDatabaseRanking":102},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":1081,"label":1082,"description":1083,"key":94,"publicationTags":1084,"standard":18},[],{"EN":91,"VI":91},{"EN":91,"VI":93},[96],[100,101],{"impactFactor":19,"impactFactorByYear":1087,"i10Index":115,"i10IndexLast5Year":48,"totalPublication":116,"totalPublicationByYear":1088,"totalCitation":134,"totalCitationByYear":1089,"totalCitationPerPublication":152,"totalCitationPerPublicationByYear":1090,"hindexLast5Year":127,"hindex":127},{"2012":105,"2013":106,"2014":107,"2015":108,"2016":109,"2017":110,"2018":111,"2019":112,"2020":111,"2021":111,"2022":113,"2023":114},{"1997":118,"2007":119,"2008":120,"2009":121,"2010":122,"2011":123,"2012":124,"2013":125,"2014":126,"2015":121,"2016":127,"2017":128,"2018":123,"2019":129,"2020":124,"2021":130,"2022":131,"2023":132,"2024":133},{"1997":136,"2008":137,"2009":138,"2010":139,"2011":140,"2012":141,"2013":142,"2014":137,"2015":143,"2016":144,"2017":145,"2018":143,"2019":146,"2020":147,"2021":148,"2022":149,"2023":150,"2024":151},{"1997":154,"2008":155,"2009":156,"2010":157,"2011":158,"2012":159,"2013":160,"2014":161,"2015":162,"2016":163,"2017":164,"2018":165,"2019":166,"2020":167,"2021":168,"2022":169,"2023":170,"2024":171},{"volume":1092,"pages":1094},{"VOID":1093},"43",{"VOID":1095},"305-328","2011-03-16",2011,{"id":1099,"createTime":1100,"updateTime":1100,"relativeEntities":1101,"slug":18,"properties":1102,"entityType":191,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1111,"fullTextUrl":18,"authors":1112,"publicationType":234,"publisherRelationship":1155,"citationCount":18,"citationInfo":18,"publishDate":1187,"publishYear":1097,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":266},"52b0494b-e0fc-4c3a-bbdf-eb17965679bf","2023-12-13T23:43:42.000+00:00",[],{"references":1103,"abstract":1105,"title":1107,"doi":1109},{"VOID":1104},"Auken E, Christiansen A, Jacobsen L, Sørensen K (2008) A resolution study of buried valleys using laterally constrained inversion of TEM data. J Appl Geophys 65:10–20\nBhattacharjya D, Eidsvik J, Mukerji T (2010) The value of information in spatial decision making. Math Geosci 42:141–163\nBickel JE, Gibson RL, McVay DA, Pickering S, Waggoner J (2006) Quantifying 3D land seismic reliability and value. Soc Pet Eng J SPE102340\nBickel JE (2008) The relationship between perfect and imperfect information in a two-action risk-sensitive problem. Decis Anal 5(3):116–128\nBickel JE, Bratvold RB (2008) From uncertainty quantification to decision-making in the oil and gas industry. Energy Explor Exploit 26(5):311–325\nBishop C (1995) Neural networks for pattern recognition. Oxford University Press, New York\nBratvold RB, Bickel JE, Lohne HP (2009) Value of information in the oil and gas industry: past, present, and future. Soc Pet Eng J SPE110378\nCaers JK (2005) Petroleum geostatistics. Society of Petroleum Engineers, Texas\nChristiansen AV (2003) Application of airborne TEM methods in Denmark and layered 2D inversion of resistivity data. PhD dissertation, University of Århus, Denmark, 136 p\nClemen RT, Reilly T (2001) Making hard decisions, 2nd edn. Duxbury Press, Pacific Grove\nCornell CA, Newmark NM (1978) On the seismic reliability of nuclear power plants. In: ANS topical meeting on probabilistic reactor safety. Newport Beach, California\nCoopersmith E, Burkholder M, Schluze J (2006) Value-of-information lookbacks—was the information you gathered really worth getting? Soc Pet Eng J SPE101540\nEidsvik J, Bhattacharjya D, Mukerji T (2008) Value of information of seismic amplitude and CSEM resistivity. Geophysics 70(4):R59–R69\nFeyen L, Gorelick S (2005) Framework to evaluate the worth of hydraulic conductivity data for optimal groundwater resources management in ecologically sensitive areas. Water Resour Res 41(3), 13 p\nHoward RA (1966) Decision analysis: applied decision theory. In: Proceedings of the fourth international conference on operational research. Wiley-Interscience, New York, pp 55–71\nHouck RT (2004) Predicting the economic impact of acquisition artifacts and noise. Lead Edge 23(10):1024–1031\nHouck RT, Pavlov DA (2006) Evaluating reconnaissance CSEM survey designs using detection theory. Lead Edge 25(8):994–1004\nHouck RT (2007) Time-lapse seismic repeatability—How much is enough? Lead Edge 26(7):828–834\nMarcotte D (1996) Variograms\u002Fcovariances in the frequency domain via the Fast Fourier Transform (FFT). Comput Geosci 22(10):1175–1186\nMatheson JE (1990) Using influence diagrams to value information and control. In: Oliver RM, Smith JQ (eds) Influence diagrams, belief nets and decision analysis. Wiley, New York, pp 25–48\nMavko G, Mukerji T, Dvorkin J (1998) The rock physics handbook. Cambridge University Press, Cambridge\nPaté-Cornell ME, Fischbeck PS (1993) Probabilistic risk analysis and risk-based priority scale for the tiles of the space shuttle. Reliab Eng Syst Saf 40(3):221–238\nPaté-Cornell ME (1990) Organizational aspects of engineering system safety: the case of offshore platforms. Science 250:1210–1217\nPaté-Cornell ME (2007) Engineering risk analysis, vol. 1, MS&E 250A Course Reader. Stanford University, Stanford\nPolasky S, Solow AR (2001) The value of information in reserve site selection. Biodivers Conserv 10:1051–1058\nRaiffa H (1968) Decision analysis. Addison-Wesley, Reading\nReichard EG, Evans JS (1989) Assessing the value of hydrogeologic information for risk-based remedial action decisions. Water Resour Res 25(7):1451–1460\nRemy N, Boucher A, Wu J (2009) Applied geostatistics with SGeMS: a user’s guide. Cambridge University Press, New York\nRipley BD (1996) Pattern recognition and neural networks. Cambridge University Press, Cambridge\nScheidt C, Caers J (2009) Uncertainty quantification in reservoir performance using distances and kernel methods—application to a West-Africa deepwater turbidite reservoir. Soc Pet Eng J 14(4):680–692, SPEJ 118740-PA\nStrebelle S (2002) Conditional simulation of complex geological structures using multiple-point statistics. Math Geol 34(1):1–26\nSuzuki S, Caers J (2008) A distance-based prior model parameterization for constraining solutions of spatial inverse problems. Math Geosci 40(4):445–469",{"EN":1106},"We propose a value of information (VOI) methodology for spatial Earth problems. VOI is a tool to determine whether purchasing a new information source would improve a decision-makers’ chances of taking the optimal action. A prior uncertainty assessment of key geologic parameters and a reliability of the data to resolve them are necessary to make a VOI assessment. Both of these elements are challenging to obtain, as this assessment is made before the information is acquired. We present a flexible prior geologic uncertainty modeling scheme that allows for the inclusion of many types of spatial parameter. Next, we describe how to obtain a physics-based reliability measure by simulating the geophysical measurement on the generated prior models and interpreting the simulated data. Repeating this simulation and interpretation for all datasets, a frequency table can be obtained that describes how many times a correct or false interpretation was made by comparing them to their respective original model. This frequency table is the reliability measure and allows a more realistic VOI calculation. An example VOI calculation is demonstrated for a spatial decision related to aquifer recharge where two geophysical techniques are considered for their ability to resolve channel orientations. As necessitated by spatial problems, this methodology preserves the structure, influence and dependence of spatial variables through the prior geological modeling and the explicit geophysical simulation and interpretations.",{"EN":1108},"A Methodology for Establishing a Data Reliability Measure for Value of Spatial Information Problems",{"VOID":1110},"10.1007\u002Fs11004-011-9367-0","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs11004-011-9367-0",[1113,1128,1143],{"id":1114,"sortIndex":19,"researcher":18,"roles":1115,"affiliations":1116,"properties":1125},"e65601be-1b55-47a2-a31a-ee6923efe1ed",[287],[1117],{"id":18,"sortIndex":19,"affiliation":1118,"properties":18},{"id":1119,"createTime":1120,"updateTime":1120,"relativeEntities":1121,"slug":18,"properties":1122,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"167e152d-842b-477d-99e6-899b49544533","2023-12-13T23:43:42.048+00:00",[],{"title":1123},{"VI":1124},"Program of Earth, Energy, and Environmental Science, Stanford University, Stanford, USA",{"title":1126},{"VI":1127},"W. J. Trainor-Guitton",{"id":1129,"sortIndex":118,"researcher":18,"roles":1130,"affiliations":1131,"properties":1140},"1b6325da-bdbc-410f-a597-4fad88c3f35b",[287],[1132],{"id":18,"sortIndex":19,"affiliation":1133,"properties":18},{"id":1134,"createTime":1135,"updateTime":1135,"relativeEntities":1136,"slug":18,"properties":1137,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"587dac44-c924-4228-bacf-1c64541bc016","2024-01-19T19:22:38.136+00:00",[],{"title":1138},{"VI":1139},"Energy Resources Engineering, Stanford University, Stanford, USA",{"title":1141},{"VI":1142},"T. Mukerji",{"id":1144,"sortIndex":219,"researcher":18,"roles":1145,"affiliations":1146,"properties":1152},"d31e6769-4216-4080-b918-b9b2a45928f0",[287],[1147],{"id":18,"sortIndex":19,"affiliation":1148,"properties":18},{"id":1134,"createTime":1135,"updateTime":1135,"relativeEntities":1149,"slug":18,"properties":1150,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1151},{"VI":1139},{"title":1153},{"VI":1154},"J. K. Caers",{"url":1111,"publisher":1156,"properties":1183},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1157,"slug":10,"properties":1158,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":1161,"manageAffiliations":1162,"indexDatabases":1163,"url":18,"thumbnailPath":18,"statistic":1178,"gsStatistic":18,"type":172,"analyzePriority":18},[],{"issn":1159,"title":1160},{"VOID":13},{"VOID":15},[],[],[1164,1171],{"id":64,"indexDatabase":1165,"url":79,"indexYears":18,"academicFieldIds":1170,"indexDatabaseRanking":18},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":1166,"label":1167,"description":1168,"key":75,"publicationTags":1169,"standard":18},[],{"EN":71,"VI":71},{"VI":73,"EN":74},[77,78],[81,82],{"id":84,"indexDatabase":1172,"url":97,"indexYears":98,"academicFieldIds":1177,"indexDatabaseRanking":102},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":1173,"label":1174,"description":1175,"key":94,"publicationTags":1176,"standard":18},[],{"EN":91,"VI":91},{"EN":91,"VI":93},[96],[100,101],{"impactFactor":19,"impactFactorByYear":1179,"i10Index":115,"i10IndexLast5Year":48,"totalPublication":116,"totalPublicationByYear":1180,"totalCitation":134,"totalCitationByYear":1181,"totalCitationPerPublication":152,"totalCitationPerPublicationByYear":1182,"hindexLast5Year":127,"hindex":127},{"2012":105,"2013":106,"2014":107,"2015":108,"2016":109,"2017":110,"2018":111,"2019":112,"2020":111,"2021":111,"2022":113,"2023":114},{"1997":118,"2007":119,"2008":120,"2009":121,"2010":122,"2011":123,"2012":124,"2013":125,"2014":126,"2015":121,"2016":127,"2017":128,"2018":123,"2019":129,"2020":124,"2021":130,"2022":131,"2023":132,"2024":133},{"1997":136,"2008":137,"2009":138,"2010":139,"2011":140,"2012":141,"2013":142,"2014":137,"2015":143,"2016":144,"2017":145,"2018":143,"2019":146,"2020":147,"2021":148,"2022":149,"2023":150,"2024":151},{"1997":154,"2008":155,"2009":156,"2010":157,"2011":158,"2012":159,"2013":160,"2014":161,"2015":162,"2016":163,"2017":164,"2018":165,"2019":166,"2020":167,"2021":168,"2022":169,"2023":170,"2024":171},{"volume":1184,"pages":1185},{"VOID":1093},{"VOID":1186},"929-949","2011-10-27",{"id":1189,"createTime":1190,"updateTime":1191,"relativeEntities":1192,"slug":1193,"properties":1194,"entityType":191,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":1205,"translateLanguages":18,"viewCount":19,"primaryUrl":1206,"fullTextUrl":18,"authors":1207,"publicationType":234,"publisherRelationship":1248,"citationCount":804,"citationInfo":1276,"publishDate":1278,"publishYear":1097,"citationAnalyzeStatus":1279,"lastCitationAnalyze":1191,"indexDatabases":18,"openAccess":18,"references":1280,"isForceReanalyzing":266},"d81a8aaa-0a40-498e-b579-55cebff03656","2024-04-11T19:46:33.045+00:00","2024-04-11T23:38:58.820+00:00",[],"Geometric-Covariograms-Indicator-Variograms-and-Boundaries-of-Planar-Closed-Sets",{"mag":1195,"keywords":1197,"openalex":1198,"abstract":1200,"title":1201,"doi":1203},{"VOID":1196},"1988079810",{},{"VOID":1199},"W1988079810",{},{"EN":1202},"Geometric Covariograms, Indicator Variograms and Boundaries of Planar Closed Sets",{"VOID":1204},"10.1007\u002Fs11004-011-9364-3",[195],"http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs11004-011-9364-3",[1208,1229],{"id":1209,"sortIndex":19,"researcher":18,"roles":1210,"affiliations":1211,"properties":1222},"86a7fa35-dbf7-42eb-911e-291d3b99a035",[],[1212],{"id":18,"sortIndex":19,"affiliation":1213,"properties":18},{"id":1214,"createTime":1215,"updateTime":1216,"relativeEntities":1217,"slug":1218,"properties":1219,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"b7198a65-4b71-47f0-9f24-6b742c67462e","2023-12-19T22:06:10.007+00:00","2024-10-16T03:33:48.852+00:00",[],"Department-of-Mining-Engineering-University-of-Chile-Santiago-Chile",{"title":1220},{"VI":1221},"Department of Mining Engineering, University of Chile, Santiago, Chile",{"openalex":1223,"orcid":1225,"title":1227},{"VOID":1224},"A5001540398",{"VOID":1226},"https:\u002F\u002Forcid.org\u002F0000-0003-0396-5038",{"EN":1228},"Xavier Emery",{"id":1230,"sortIndex":219,"researcher":18,"roles":1231,"affiliations":1232,"properties":1241},"3f5fd435-25ac-4911-b0a9-78f54348e96e",[],[1233],{"id":18,"sortIndex":19,"affiliation":1234,"properties":18},{"id":1235,"createTime":1236,"updateTime":1236,"relativeEntities":1237,"slug":18,"properties":1238,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"39ee31bb-91c8-4fda-85a8-3b4cfbb61cc8","2023-12-30T00:33:01.327+00:00",[],{"title":1239},{"VI":1240},"Centre de Géosciences, MINES ParisTech, Fontainebleau, France",{"openalex":1242,"orcid":1244,"title":1246},{"VOID":1243},"A5062190063",{"VOID":1245},"https:\u002F\u002Forcid.org\u002F0000-0002-8609-8963",{"EN":1247},"Christian Lantuéjoul",{"url":18,"publisher":1249,"properties":18},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1250,"slug":10,"properties":1251,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":1254,"manageAffiliations":1255,"indexDatabases":1256,"url":18,"thumbnailPath":18,"statistic":1271,"gsStatistic":18,"type":172,"analyzePriority":18},[],{"issn":1252,"title":1253},{"VOID":13},{"VOID":15},[],[],[1257,1264],{"id":64,"indexDatabase":1258,"url":79,"indexYears":18,"academicFieldIds":1263,"indexDatabaseRanking":18},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":1259,"label":1260,"description":1261,"key":75,"publicationTags":1262,"standard":18},[],{"EN":71,"VI":71},{"VI":73,"EN":74},[77,78],[81,82],{"id":84,"indexDatabase":1265,"url":97,"indexYears":98,"academicFieldIds":1270,"indexDatabaseRanking":102},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":1266,"label":1267,"description":1268,"key":94,"publicationTags":1269,"standard":18},[],{"EN":91,"VI":91},{"EN":91,"VI":93},[96],[100,101],{"impactFactor":19,"impactFactorByYear":1272,"i10Index":115,"i10IndexLast5Year":48,"totalPublication":116,"totalPublicationByYear":1273,"totalCitation":134,"totalCitationByYear":1274,"totalCitationPerPublication":152,"totalCitationPerPublicationByYear":1275,"hindexLast5Year":127,"hindex":127},{"2012":105,"2013":106,"2014":107,"2015":108,"2016":109,"2017":110,"2018":111,"2019":112,"2020":111,"2021":111,"2022":113,"2023":114},{"1997":118,"2007":119,"2008":120,"2009":121,"2010":122,"2011":123,"2012":124,"2013":125,"2014":126,"2015":121,"2016":127,"2017":128,"2018":123,"2019":129,"2020":124,"2021":130,"2022":131,"2023":132,"2024":133},{"1997":136,"2008":137,"2009":138,"2010":139,"2011":140,"2012":141,"2013":142,"2014":137,"2015":143,"2016":144,"2017":145,"2018":143,"2019":146,"2020":147,"2021":148,"2022":149,"2023":150,"2024":151},{"1997":154,"2008":155,"2009":156,"2010":157,"2011":158,"2012":159,"2013":160,"2014":161,"2015":162,"2016":163,"2017":164,"2018":165,"2019":166,"2020":167,"2021":168,"2022":169,"2023":170,"2024":171},{"total":804,"publishYear":18,"statisticByYear":1277},{"2013":219,"2017":118,"2018":219,"2021":118},"2011-11-01","ERROR_IN_ANALYZE_CITATION",[1281,1284,1288,1292,1296,1299,1303,1307,1310,1313,1316,1319,1322,1325,1328],{"id":18,"text":1282,"url":18,"identifiers":1283},"Adler RJ, Taylor JE (2007) Random fields and geometry. Springer, New York",{},{"id":18,"text":1285,"url":18,"identifiers":1286},"Chilès JP, Delfiner P (1999) Geostatistics: modeling spatial uncertainty. Wiley, New York",{"doi":1287},"10.1002\u002F9780470316993",{"id":18,"text":1289,"url":18,"identifiers":1290},"Emery X (2010) On the existence of mosaic and indicator random fields with spherical, circular and triangular variograms. Math Geosci 42(8):969–984",{"doi":1291},"10.1007\u002Fs11004-010-9282-9",{"id":18,"text":1293,"url":18,"identifiers":1294},"Jeulin D (2000) Random texture models for material structures. Stat Comput 10(2):121–132",{"doi":1295},"10.1023\u002FA:1008942325749",{"id":18,"text":1297,"url":18,"identifiers":1298},"Krantz SG, Parks HR (2002) The implicit function theorem: history, theory and applications. Birkhaüser, Boston",{},{"id":18,"text":1300,"url":18,"identifiers":1301},"Lantuéjoul C (2002) Geostatistical simulation: models and algorithms. Springer, Berlin",{"doi":1302},"10.1007\u002F978-3-662-04808-5",{"id":18,"text":1304,"url":18,"identifiers":1305},"Mandelbrot B (2004) Fractal and chaos: the Mandelbrot set and beyond. Springer, New York",{"doi":1306},"10.1007\u002F978-1-4757-4017-2",{"id":18,"text":1308,"url":18,"identifiers":1309},"Matheron G (1967) Eléments pour une théorie des milieux poreux (Elements for a theory of porous media). Masson, Paris",{},{"id":18,"text":1311,"url":18,"identifiers":1312},"Matheron G (1971) The theory of regionalized variables and its applications. Ecole Nationale Supérieure des Mines de Paris, Fontainebleau",{},{"id":18,"text":1314,"url":18,"identifiers":1315},"Matheron G (1975) Random sets and integral geometry. Wiley, New York",{},{"id":18,"text":1317,"url":18,"identifiers":1318},"Molchanov I (2005) Theory of random sets. Springer, London",{},{"id":18,"text":1320,"url":18,"identifiers":1321},"Munkres J (2000) Topology, 2nd edn. Prentice Hall, Upper Saddle River",{},{"id":18,"text":1323,"url":18,"identifiers":1324},"Omre H, Tjelmeland H (1997) Petroleum geostatistics. In: Baafi EY, Schofield NA (eds) Geostatistics Wollongong’96. Kluwer, Dordrecht, pp 41–52",{},{"id":18,"text":1326,"url":18,"identifiers":1327},"Serra J (1982) Image analysis and mathematical morphology. Academic Press, London",{},{"id":18,"text":1329,"url":18,"identifiers":1330},"Stoyan D, Kendall WS, Mecke J (1995) Stochastic geometry and its applications. Wiley, New York",{}]