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The present study carries out a statistical investigation of the relationship between the seismicity and some water level parameters of the Kariba Reservoir for the period between 1960 and 2020. This study is aimed at understanding the role played by reservoir annual water fluctuations and other reservoir water parameters in inducing seismic events. The study further aims to assess the risk level of the mid-Zambezi basin. The study area, mid-Zambezi basin, presents a particular interest due to the frequency of seismic events in the basin. In an effort to understand the effect of the fluctuation of the water parameters, cross correlation methods and statistical modelling using extreme value models were used. The model parameters were estimated using the method of moments (MM) and maximum likelihood estimation (MLE) method. The models in this study were assessed using Anderson-Darling, Kolmogorov-Smirnov and chi-squared goodness-of-fit tests. The best point process probability model was selected using the Akaike’s information criterion (AIC). Time series ARIMA models were used to forecast reservoir water parameters and the analysis concludes with a dam failure risk assessment. The study revealed that there is a correlation between reservoir water level and seismic moment magnitude, as well as between maximum amplitude of reservoir level changes and number of seismic events. The seismic moment magnitude rate in mid-Zambezi basin was also shown to be related to annual Kariba water level. The study also demonstrated that the annual reservoir water level rate and the duration in days for which the maximum water level is maintained, do not contribute significantly in inducing seismic events. The study also showed that seismic occurrence response to large changes in Kariba Dam water level is within several kilometers around Kariba Reservoir and is not instantaneous. The response time is nearly 61 days, within which response to gravitational loading of the Kariba Reservoir occurs. This could promote pore water pressure diffusion from the Kariba Reservoir to mid-Zambezi basin. Following the International Commission On Large Dams (ICOLD) guidelines on risk assessment, the study further identifies six significant variables, current state of Kariba Dam and seismological observations. The risk factors were then evaluated, giving a total points of 40 which suggested that the mid-Zambezi basin can be classified as extremely risky with risk level IV according to ICOLD risk classification. Literature of this nature is scarce in mid-Zambezi basin and several other basins in the world, therefore this study and the findings therein will play an important role to the body of knowledge in applications of statistics to seismic events.",{"EN":115},"Statistical investigation of reservoir-induced seismic events in mid-Zambezi basin and risk assessment of seismically triggered Kariba Dam failure",{"VOID":117},"[\"1457891843938369737\"]",{"VOID":119},"10.1007\u002Fs10950-022-10101-z","PUBLICATION","VERIFIED","2024-04-28T04:55:42.244+00:00","Auto 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Google Maps [online] Available through:http:\u002F\u002Fwww.isc.ac.uk\u002Fisc-ehb\u002Fsearch\u002Fcatalogue\u002Finteractive\u002F. Accessed 20 Dec 2021","http:\u002F\u002Fwww.isc.ac.uk\u002Fisc-ehb\u002Fsearch\u002Fcatalogue\u002Finteractive\u002F",{},{"id":20,"text":312,"url":313,"identifiers":314},"Gough DI, Gough WI (1970) Load-induced Earthquakes at Lake Kariba-II. Geophys J Roy Astron Soc 21:79–101. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1365-246X.1970.tb01768.x","https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1365-246x.1970.tb01768.x",{"mag":315,"openalex":316,"doi":317},"2156897469","W2156897469","10.1111\u002Fj.1365-246x.1970.tb01768.x",{"id":20,"text":319,"url":320,"identifiers":321},"Gough DI, Gough WI (1976) Time dependence and trigger mechanisms for the Kariba (Rhodesia) earthquakes. Eng Geology 10(2-4):211–217. ISSN 0013-7952 https:\u002F\u002Fdoi.org\u002F10.1016\u002F0013-7952(76)90021-1","https:\u002F\u002Fdoi.org\u002F10.1016\u002F0013-7952(76)90021-1",{"mag":322,"issn":323,"openalex":324,"doi":325},"2030301015","0013-7952","W2030301015","10.1016\u002F0013-7952(76)90021-1",{"id":20,"text":327,"url":328,"identifiers":329},"Gupta HK (2002) A review of recent studies of triggered earthquakes by artificial water reservoirs with special emphasis on earthquakes in Koyna, India. Earth Sci Rev 58(3-4):279–310. 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Geophys J Int 120(3):567–576. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1365-246X1995.tb01839.x","http:\u002F\u002Fdx.doi.org\u002F10.1111\u002Fj.1365-246x.1995.tb01839.x",{"doi":358},"10.1111\u002Fj.1365-246x.1995.tb01839.x",{"id":20,"text":360,"url":361,"identifiers":362},"Institute of Risk Management South Africa (2015) Risk research report of Kariba Dam. https:\u002F\u002Fwww.irmsa.org.za\u002Fblogpost\u002F1163465\u002F191101\u002FPossible-collapse-of-the-Kariba-dam-wall. Accessed 20 Apr 2021","https:\u002F\u002Fwww.irmsa.org.za\u002Fblogpost\u002F1163465\u002F191101\u002FPossible-collapse-of-the-Kariba-dam-wall",{},{"id":20,"text":364,"url":20,"identifiers":365},"International Commission on Large Dams (ICOLD) (1989) Selecting seismic parameters for large dams guidelines Bulletin 72 International Commission on Large Dams. France, Paris",{},{"id":20,"text":367,"url":368,"identifiers":369},"International Seismological Centre (2021) ISC-EHB dataset. https:\u002F\u002Fdoi.org\u002F10.31905\u002FPY08W6S3","https:\u002F\u002Fdoi.org\u002F10.31905\u002FPY08W6S3",{"doi":370},"10.31905\u002FPY08W6S3",{"id":20,"text":372,"url":20,"identifiers":373},"Masukwedza IGT (2016) Seismic observation and seismicity of Zimbabwe. Global Seismology Course, Meteorological Services Department of Zimbabwe",{},{"id":335,"text":375,"url":337,"identifiers":376},"Richter CF (1935) An instrumental earthquake magnitude scale. Bull Seismol Soc Am 25(1) 1–32. ISSN 0037-1106",{"doi":339},{"id":20,"text":378,"url":379,"identifiers":380},"Pavlou K (2019) Relationship between observed seismicity and water level fluctuations in polyphyto dam area, North Greece. Journal of Geography, Environment and Earth Science International 21(2):1–10. https:\u002F\u002Fdoi.org\u002F10.9734\u002FJGEESI\u002F2019\u002Fv21i230122","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjgeesi\u002F2019\u002Fv21i230122",{"mag":381,"openalex":382,"doi":383},"2947385375","W2947385375","10.9734\u002Fjgeesi\u002F2019\u002Fv21i230122",{"id":385,"text":386,"url":387,"identifiers":388},"cee013b6-7962-41ad-9d4f-ce778cf6379f","Storchak DA, Harris J, Brown L et al (2017) Rebuild of the bulletin of the international seismological centre (ISC), part 1: 1964–1979. Geosci Lett 4:32. https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs40562-017-0098-z","https:\u002F\u002Fgeoscienceletters.springeropen.com\u002Farticles\u002F10.1186\u002Fs40562-017-0098-z",{"doi":389},"10.1186\u002Fs40562-017-0098-z",{"id":20,"text":391,"url":392,"identifiers":393},"Willemann R, Storchak D (2001) Data collection at the international seismological centre. Seismol Res Lett 72. https:\u002F\u002Fdoi.org\u002F10.1785\u002Fgssrl.72.4.440","https:\u002F\u002Fdoi.org\u002F10.1785\u002Fgssrl.72.4.440",{"mag":394,"openalex":395,"doi":396},"2043324167","W2043324167","10.1785\u002Fgssrl.72.4.440",{"id":20,"text":398,"url":399,"identifiers":400},"Zambezi River Authority (2021) https:\u002F\u002Fwww.zambezira.org\u002Fmedia-centre\u002Fphoto-gallery\u002Fdam-photos. Accessed 05 Nov 2021","https:\u002F\u002Fwww.zambezira.org\u002Fmedia-centre\u002Fphoto-gallery\u002Fdam-photos",{},{"id":20,"text":402,"url":403,"identifiers":404},"Zambezi River Authority (2022) https:\u002F\u002Fwww.zambezira.org\u002Fhydro-electric-schemes\u002Fkariba-hes\u002Fkariba-hes-technical-data. Accessed 05 March 2022","https:\u002F\u002Fwww.zambezira.org\u002Fhydro-electric-schemes\u002Fkariba-hes\u002Fkariba-hes-technical-data",{},{"id":20,"text":406,"url":407,"identifiers":408},"Zeng X, Wang D, Wu J (2015) Evaluating the three methods of goodness of fit test for frequency analysis. J Risk Anal Crisis Response 5(3):178. https:\u002F\u002Fdoi.org\u002F10.2991\u002Fjrarc.2015.5.3.5","https:\u002F\u002Fdoi.org\u002F10.2991\u002Fjrarc.2015.5.3.5",{"mag":409,"openalex":410,"doi":411},"2191026410","W2191026410","10.2991\u002Fjrarc.2015.5.3.5",{"id":413,"text":414,"url":415,"identifiers":416},"80bbf2e3-bf24-4812-aef1-76675e29e709","Zhang L, Li J, Wei G, Liao W, Wang Q, Xiang C (2017) Analysis of the relationship between water level fluctuation and seismicity in the Three Gorges Reservoir (China). Geodesy Geodyn 8(2):96–102. ISSN 1674-9847. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.geog.2017.02.004","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1674984716301252",{"doi":417},"10.1016\u002Fj.geog.2017.02.004",false,{"id":420,"createTime":421,"updateTime":422,"relativeEntities":423,"slug":424,"properties":425,"entityType":120,"verifyStatus":121,"verifyTime":436,"verifyNote":123,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":437,"fullTextUrl":20,"authors":438,"publicationType":176,"publisherRelationship":524,"citationCount":21,"citationInfo":576,"publishDate":579,"publishYear":577,"citationAnalyzeStatus":19,"lastCitationAnalyze":580,"indexDatabases":581,"openAccess":20,"references":20,"isForceReanalyzing":418},"1c7544c0-f240-48f3-808f-e2cfb2b620c5","2024-02-10T07:22:59.792+00:00","2026-07-17T09:31:36.762+00:00",[],"Comparison-of-seismicity-declustering-methods-using-a-probabilistic-measure-of-clustering",{"abstract":426,"title":428,"gsPaper":430,"references":432,"doi":434},{"EN":427},"We present a new measure of earthquake clustering and explore its use for comparing the performance of three different declustering methods. The advantage of this new clustering measure over existing techniques is that it can be used for non-Poissonian background seismicity and, in particular, to compare the results of declustering algorithms where different background models are used. We use our approach to study inter-event times between successive earthquakes using earthquake catalog data from Japan and southern California. A measure of the extent of clustering is introduced by comparing the inter-event time distributions of the background seismicity to that of the whole observed seismicity. Theoretical aspects of the clustering measure are then discussed with respect to the Poissonian and Weibull models for the background inter-event time distribution. In the case of a Poissonian background, the obtained clustering measure shows a decrease followed by an increase, defining a V-shaped trend, which can be explained by the presence of short- and long-range correlation in the inter-event time series. Three previously proposed declustering methods (i.e., the methods of Gardner and Knopoff, Reasenberg, and Zhuang et al.) are used to obtain an approximation of the residual “background” inter-event time distribution in order to apply our clustering measure to real seismicity. The clustering measure is then estimated for different values of magnitude cutoffs and time periods, taking into account the completeness of each catalog. Plots of the clustering measure are presented as clustering attenuation curves (CACs), showing how the correlation decreases when inter-event times increase. The CACs demonstrate strong clustering at short inter-event time ranges and weak clustering at long time ranges. When the algorithm of Gardner and Knopoff is used, the CACs show strong correlation with a weak background at the short inter-event time ranges. The fit of the CACs using the Poissonian background model is successful at short and intermediate inter-event time ranges, but deviates at long ranges. The observed deviation shows that the residual catalog obtained after declustering remains non-Poissonian at long time ranges. The apparent background fraction can be estimated directly from the CAC fit. The CACs using the algorithms of Reasenberg and Zhuang et al. show a relatively similar behavior, with a time correlation decreasing more rapidly than the CACs of Gardner and Knopoff for shorter time ranges. This study offers a novel approach for the study of different types of clustering produced as a result of various hypotheses used to account for different backgrounds.",{"EN":429},"Comparison of seismicity declustering methods using a probabilistic measure of clustering",{"VOID":431},"[\"1680413239237593756\"]",{"VOID":433},"Baddeley A, Turner R, Moller J, Hazelton M (2005) Residual analysis for spatial point processes. J R Stat Soc Ser B Stat Methodol 67:617–666\nBaddeley A, Moller J, Pakes AG (2008) Properties of residuals for spatial point processes. 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Proc Natl Acad Sci U S A 97(22):11880–11884\nLawson AB (1993) A deviance residual for heterogeneous spatial point processes. Biometrics 49:889–897\nLuen B, Stark PB (2012) Poisson tests of declustered catalogs. Geophys J Int 189(1):691–700\nMarsan D, Lengline O (2008) Extending earthquakes’ reach through cascading. Science 319:1076. doi:10.1126\u002Fscience.1148783\nMatthews MV, Ellsworth WL, Reasenberg PA (2002) A Brownian model for recurrent earthquakes. Bull Seismol Soc Am 92(6):2233–2250\nMolchan G (2005) Interevent time distribution in seismicity: a theoretical approach. Pure Appl Geophys 162:1135–1150. doi:10.1007\u002Fs00024-004-2664-5\nNanjo KZ, Ishibe T, Tsuruoka H, Schorlemmer D, Ishigaki Y, Hirata N (2010) Analysis of completeness magnitude and seismic network coverage of Japan. Bull Seismol Soc Am 100(6):3261–3268\nNaylor M, Main IG, Touati S (2009) Quantifying uncertainty in mean earthquake inter-event times for a finite sample. 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Bull Seismol Soc Am 97(5):1679–1687",{"VOID":435},"10.1007\u002Fs10950-013-9371-6","2024-05-10T05:54:54.438+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10950-013-9371-6",[439,463,478,493,510],{"id":440,"sortIndex":21,"researcher":20,"roles":441,"affiliations":442,"properties":460,"displayName":462,"givenName":20,"familyName":20},"235260ba-7b10-4b20-85e2-87447e8b02a5",[129],[443,451],{"id":444,"sortIndex":21,"affiliation":445,"properties":20},"2706714d-e9c3-4511-9efa-703a4e2680da",{"id":444,"createTime":20,"updateTime":20,"relativeEntities":446,"slug":20,"properties":447,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":450,"statistic":20},[],{"title":448},{"VI":449},"Earthquake Research Institute, University of Tokyo, Bunkyo-ku, 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clustering is an essential part of almost any statistical analysis of spatial and temporal properties of seismic activity. The nature of earthquake clusters and subsequent declustering of earthquake catalogues plays a crucial role in determining the magnitude-dependent earthquake return period and its respective spatial variation for probabilistic seismic hazard assessment. This study introduces the Smart Cluster Method (SCM), a new methodology to identify earthquake clusters, which uses an adaptive point process for spatio-temporal cluster identification. It utilises the magnitude-dependent spatio-temporal earthquake density to adjust the search properties, subsequently analyses the identified clusters to determine directional variation and adjusts its search space with respect to directional properties. In the case of rapid subsequent ruptures like the 1992 Landers sequence or the 2010–2011 Darfield-Christchurch sequence, a reclassification procedure is applied to disassemble subsequent ruptures using near-field searches, nearest neighbour classification and temporal splitting. The method is capable of identifying and classifying earthquake clusters in space and time. It has been tested and validated using earthquake data from California and New Zealand. A total of more than 1500 clusters have been found in both regions since 1980 with M\n                        \n                  m\n                  i\n                  n\n                 = 2.0. Utilising the knowledge of cluster classification, the method has been adjusted to provide an earthquake declustering algorithm, which has been compared to existing methods. Its performance is comparable to established methodologies. The analysis of earthquake clustering statistics lead to various new and updated correlation functions, e.g. for ratios between mainshock and strongest aftershock and general aftershock activity metrics.",{"EN":592},"The smart cluster method",{"VOID":594},"[\"514651545322165249\"]",{"VOID":596},"10.1007\u002Fs10950-017-9646-4","2024-05-07T21:39:20.621+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10950-017-9646-4",[600,617,632],{"id":601,"sortIndex":21,"researcher":20,"roles":602,"affiliations":603,"properties":612,"displayName":614,"givenName":20,"familyName":20},"e7fab65f-e2f8-4f5b-a437-e1d53c5cd33f",[129],[604],{"id":605,"sortIndex":21,"affiliation":606,"properties":20},"c8d28edc-93ba-4bdd-8879-57efa6e9386d",{"id":605,"createTime":20,"updateTime":20,"relativeEntities":607,"slug":20,"properties":608,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":611,"statistic":20},[],{"title":609},{"VI":610},"Geophysical Institute, Karlsruhe Institute of Technology, Karlsruhe, Germany",[],{"title":613,"gsAuthor":615},{"VI":614},"Andreas M. Schaefer",{"VOID":616},"[\"a8XHdaAAAAAJ\"]",{"id":618,"sortIndex":96,"researcher":20,"roles":619,"affiliations":620,"properties":627,"displayName":629,"givenName":20,"familyName":20},"966cab14-af79-48af-b6e0-a1b7da44339b",[129],[621],{"id":605,"sortIndex":21,"affiliation":622,"properties":20},{"id":605,"createTime":20,"updateTime":20,"relativeEntities":623,"slug":20,"properties":624,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":626,"statistic":20},[],{"title":625},{"VI":610},[],{"title":628,"gsAuthor":630},{"VI":629},"James E. Daniell",{"VOID":631},"[\"ujXiALMAAAAJ\"]",{"id":633,"sortIndex":97,"researcher":20,"roles":634,"affiliations":635,"properties":642,"displayName":644,"givenName":20,"familyName":20},"9327c8b3-79c7-4eed-b9b4-2f5365c077ec",[129],[636],{"id":605,"sortIndex":21,"affiliation":637,"properties":20},{"id":605,"createTime":20,"updateTime":20,"relativeEntities":638,"slug":20,"properties":639,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":641,"statistic":20},[],{"title":640},{"VI":610},[],{"title":643,"gsAuthor":645},{"VI":644},"Friedemann Wenzel",{"VOID":646},"[\"TMo_qEsAAAAJ\"]",{"url":598,"publisher":648,"properties":694},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":649,"slug":10,"properties":650,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":654,"manageAffiliations":663,"indexDatabases":674,"url":87,"thumbnailPath":20,"statistic":689,"gsStatistic":20,"type":100,"analyzePriority":20},[],{"issn":651,"title":652,"eissn":653},{"VOID":13},{"EN":15},{"VOID":17},[655,659],{"id":24,"createTime":20,"updateTime":20,"relativeEntities":656,"label":657,"description":658,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":27},{},{"id":30,"createTime":20,"updateTime":20,"relativeEntities":660,"label":661,"description":662,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":33},{},[664,669],{"id":37,"createTime":20,"updateTime":20,"relativeEntities":665,"slug":20,"properties":666,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":668,"statistic":20},[],{"title":667},{"EN":41},[43],{"id":45,"createTime":20,"updateTime":20,"relativeEntities":670,"slug":20,"properties":671,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":673,"statistic":20},[],{"title":672},{"EN":49},[],[675,682],{"id":53,"indexDatabase":676,"url":66,"indexYears":20,"academicFieldIds":681,"indexDatabaseRanking":20},{"id":55,"createTime":20,"updateTime":20,"relativeEntities":677,"label":678,"description":679,"key":62,"publicationTags":680,"standard":20},[],{"EN":58,"VI":58},{"EN":60,"VI":61},[64,65],[68],{"id":70,"indexDatabase":683,"url":81,"indexYears":82,"academicFieldIds":688,"indexDatabaseRanking":86},{"id":72,"createTime":20,"updateTime":20,"relativeEntities":684,"label":685,"description":686,"key":78,"publicationTags":687,"standard":20},[],{"EN":75,"VI":75},{"EN":75,"VI":77},[80],[84,85],{"impactFactor":21,"impactFactorByYear":690,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":90,"totalPublicationByYear":691,"totalCitation":21,"totalCitationByYear":692,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":693,"hindexLast5Year":21,"hindex":21},{},{"1997":92,"1998":93,"1999":94,"2000":92,"2001":95,"2002":94,"2003":96,"2005":96,"2006":96,"2007":96,"2008":96,"2012":97,"2013":96,"2016":97,"2018":97,"2020":96,"2022":93},{},{},{"pages":695,"volume":697},{"VOID":696},"965-985",{"VOID":698},"21",{"total":21,"publishYear":700,"statisticByYear":701},2017,{},"2017-03-17","2026-07-17T05:00:12.653+00:00",[86,64],[706,709,712,715,721,724,727,730,733,736,739,742,748,751,754,757,760,766,769,772,775,778,784,787,790,793,796,799,802,805,808,811,814,817,823,826,829,832,835,838,841,844,847,850,853,856,859,862,865,868],{"id":20,"text":707,"url":20,"identifiers":708},"Abrahamson N (2006) Seismic hazard assessment: problems with current practice and future developments. 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J Geophys Res Solid Earth 111 (B5):1–12",{"doi":339},{"id":335,"text":848,"url":337,"identifiers":849},"Vidale JE, Boyle KL, Shearer PM (2006) Crustal earthquake bursts in California and Japan: their patterns and relation to volcanoes. Geophys Res Lett 33(20):1–5",{"doi":339},{"id":335,"text":851,"url":337,"identifiers":852},"Wells DL, Coppersmith KJ (1994) New empirical relationships among magnitude, rupture length, rupture width, rupture area, and surface displacement. Bull Seismol Soc Am 84(4):974–1002",{"doi":339},{"id":335,"text":854,"url":337,"identifiers":855},"Werner MJ, Helmstetter A, Jackson D, Kagan Y (2011) High-resolution long-term and short-term earthquake forecasts for California. Bull Seism Soc Am 101:1630–1648",{"doi":339},{"id":335,"text":857,"url":337,"identifiers":858},"Wiemer S, Wyss M (2000) Minimum magnitude of complete reporting in earthquake catalogs: examples from Alaska, the western United States, and Japan. Bull Seism Soc Am 90:859–869",{"doi":339},{"id":335,"text":860,"url":337,"identifiers":861},"Zaliapin I, Ben-Zion Y (2013) Earthquake clusters in southern California i: identification and stability. J Geophys Res Solid Earth 118:2847–2864",{"doi":339},{"id":335,"text":863,"url":337,"identifiers":864},"Zaliapin I, Ben-Zion Y (2016) Discriminating characteristics of tectonic and human-induced seismicity. Bull Seismol Soc Am 106(3):846–859",{"doi":339},{"id":20,"text":866,"url":20,"identifiers":867},"Zhuang J (2011) Next-day earthquake forecasts by using the etas model. Earth Planet Space 63:207–216",{},{"id":335,"text":869,"url":337,"identifiers":870},"Zhuang J, Ogata Y, Vere-Jones D (2002) Stochastic declustering of space-time earthquake occurrences. J Am Stat Assoc 97(458):369–380",{"doi":339},{"id":872,"createTime":873,"updateTime":874,"relativeEntities":875,"slug":876,"properties":877,"entityType":120,"verifyStatus":121,"verifyTime":888,"verifyNote":123,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":889,"fullTextUrl":20,"authors":890,"publicationType":176,"publisherRelationship":974,"citationCount":21,"citationInfo":1026,"publishDate":1029,"publishYear":1027,"citationAnalyzeStatus":19,"lastCitationAnalyze":1030,"indexDatabases":1031,"openAccess":20,"references":20,"isForceReanalyzing":418},"a911e23c-f7b7-43ab-8102-9d6f0bd1e604","2024-01-08T09:33:54.421+00:00","2026-07-10T14:05:42.778+00:00",[],"Empirical-relations-for-conversion-of-surface-and-body-wave-magnitudes-to-moment-magnitudes-in-China-s-seas-and-adjacent-areas",{"abstract":878,"title":880,"gsPaper":882,"references":884,"doi":886},{"EN":879},"An earthquake catalog based on a unified magnitude scale is an important prerequisite for analyses of seismic activities and seismic hazards. To unify the magnitude scales of earthquakes in China’s seas and neighboring regions, we developed conversion relationships between surface- and body-wave magnitudes and the global centroid moment tensor (GCMT) and National Research Institute for Earth Science and Disaster Resilience (NIED) moment magnitudes for shallow and intermediate- and deep-focus earthquakes in China’s seas and adjacent areas. We employed data collected by the Chinese Earthquake Network Center, GCMT, and NIED for earthquakes of magnitude 4.5 and higher in China’s seas and neighboring regions from 1976 to 2018. We used the linear least squares regression method and the orthogonal regression method to fit the seismic data under different magnitude ranges and different focal depth ranges. The results provided empirical formulas to unify magnitude scales for the earthquakes in China’s seas and neighboring regions and also provided an important basis for seismic catalog integrity analyses and seismic activity studies for this area. The results are significant for seismic hazard analyses, seismic zoning, and mid- and long-term forecast analyses of seismic activities in the area.",{"EN":881},"Empirical relations for conversion of surface- and body-wave magnitudes to moment magnitudes in China’s seas and adjacent areas",{"VOID":883},"[\"16176607890608961698\"]",{"VOID":885},"Bormann P, Liu RF, Ren X, Gutdeutsch R, Kaiser D, Castellaro S (2007) Chinese national network magnitudes, their relation to NEIC magnitudes, and recommendations for new IASPEI magnitude standards. Bull Seismol Soc Am 97(1B):114–127. https:\u002F\u002Fdoi.org\u002F10.1785\u002F0120060078\nCarroll RJ, Ruppert D (1996) The use and misuse of orthogonal regression in linear errors-in-variables models. Am Stat 50(1):1–6. https:\u002F\u002Fdoi.org\u002F10.1080\u002F00031305.1996.10473533\nCastellaro S, Bormann P (2007) Performance of different regression procedures on the magnitude conversion problem. Bull Seismol Soc Am 97(4):1167–1175. https:\u002F\u002Fdoi.org\u002F10.1785\u002F0120060102\nCastellaro S, Mulargia F, Kagan YY (2006) Regression problems for magnitudes. Geophys J Int 165(3):913–930. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1365-246X.2006.02955.x\nCavallini F, Rebez A (1996) Representing earthquake intensity-magnitude relationship with a nonlinear function. Bull Seismol Soc Am 86(1A):73–78\nChen YT, Liu RF (2004) Earthquake magnitude. Seismol Geomagn Obs Res 25(6):1–12 (in Chinese)\nChen YT, Wu ZL, Wang PD (2000) Digital seismology. Seismological Press, Beijing, pp 1–30 (in Chinese)\nCheng J, Rong YF, Magistrale H, Chen GH, Xu XW (2017) An Mw-based historical earthquake catalog for mainland China. 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Bull Seismol Soc Am 35(1):3–12\nHanks TC, Kanamori H (1979) A moment magnitude scale. J Geophys Res 84(B5):2348–2349. https:\u002F\u002Fdoi.org\u002F10.1029\u002FJB084iB05p02348\nHarvard University and Columbia University Lamont-Doherty Earth Observatory (2018) GCMT catalog. http:\u002F\u002Fwww.globalcmt.org\u002FCMTsearch.html\nKanamori H (1977) The energy release in great earthquake. J Geophys Res 82(20):2981–2987. https:\u002F\u002Fdoi.org\u002F10.1029\u002FJB082i020p02981\nKendall MG, Stuart A (1979) The advanced theory of statistics, vol 2, 4th edn. Griffin, London\nKubo A, Fukuyama E (2003) Stress field along the Ryukyu Arc and the Okinawa Trough inferred from moment tensors of shallow earthquakes. Earth Planet Sci Lett 210(1–2):305–316. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS0012-821X(03)00132-8\nLi XJ (2005a) Comparison of several linear regression methods. Meas Tech 8:52–54 (in Chinese)\nLi XJ (2005b) The discussion of the least squares linear regression in the X and Y directions. Meas Tech 1:50–52 (in Chinese)\nLi XJ, Gao MT (2017) Conversion of local and surface-wave magnitudes to moment magnitude for earthquakes in the Chinese mainland. 2017 Fall Meeting, AGU, New Orleans, USA\nLiu RF, Chen YT, Bormann P, Ren X, Hou JM, Zou LY, Yang H (2005) Comparison between earthquake magnitudes determined by China seismograph network and US seismograph network (I): body wave magnitude. Acta Seismol Sin 27:583–587 (in Chinese)\nLiu RF, Chen YT, Bormann P, Ren X, Hou JM, Zou LY, Yang H (2006) Comparison between earthquake magnitudes determined by China seismograph network and US seismograph network (II): surface wave magnitude. Acta Seismol Sin 28:1–7 (in Chinese)\nLiu RF, Chen YT, Ren X, Xu ZG, Sun L, Yang H, Liang JH, Ren KX (2007) Comparison between different earthquake magnitude determined by China seismograph network. Acta Seismol Sin 29(5):467–476 (in Chinese)\nLiu RF, Chen YT, Ren X, Xu ZG, Wang XX, Zou LH, Zhang LW (2015) Determination of magnitude. Seismological Press, Beijing (in Chinese)\nLiu RF, Wang LY, Yuan NR, Cheng HF, Han XJ (2017) Material of GB17740-2017: general ruler for earthquake magnitude. Seismological Press, Beijing, pp 40–41 (in Chinese)\nLiu RF, Chen YT, Xue F (2018) The measured magnitude should not be converted to each other. Seismol Geomagn Obs Res 39:1–9 (in Chinese)\nMadansky A (1959) The fitting of straight lines when both variables are subject to error. J Am Stat Assoc 54(285):173–205. https:\u002F\u002Fdoi.org\u002F10.1080\u002F01621459.1959.10501505\nNath SK, Mandal S, Adhikari MD, Maiti SK (2017) A unified earthquake catalogue for South Asia covering the period 1900–2014. Nat Hazards 85:1787–1810. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11069-016-2665-6\nScordilis EM (2006) Empirical global relations converting MS and mb to moment magnitude. J Seismol 10:225–236. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10950-006-9012-4\nSun YQ (2002) Two problems on least-squares fit. J Hanzhong Teach Coll 20:58–61 (in Chinese)\nTang CC, Zhu LP, Huang R (2016) Empirical Mw–ML, mb, and Ms conversions in Western China. Bull Seismol Soc Am 106(6):2614–2623. https:\u002F\u002Fdoi.org\u002F10.1785\u002F0120160148\nThingbaijam KKS, Chingtham P, Nath SK (2009) Seismicity in the northwest frontier province of Indian-Eurasian plate convergences. Seismol Res Lett 80:599–608. https:\u002F\u002Fdoi.org\u002F10.1785\u002Fgssrl.80.4.599\nWu HL, Wang SZ, Li SH (1992) Applications and analysis of directional selection of linear fitting. J Shanxi Norm Univ 20(3):72–75 (in Chinese)\nXie ZJ (2009) Statistical analysis on the seismic data of Chinese mainland and adjacent regions since 1990. M.D. thesis, Institute of Crustal Dynamics, China Earthquake Administration, Beijing (in Chinese)\nXie ZJ, Lu YJ, Peng YJ, Zhang LF (2012) Study on the empirical relations between surface wave magnitude and local earthquake magnitude in the China mainland and neighboring region. Institute of Crustal Dynamics, C.E.A. Beijing, (24):74–89 (in Chinese)\nYadav RBS, Bormann P, Rastogi BK, Das MC, Chopra S (2009) A homogeneous and complete earthquake catalog for Northeast India and the adjoining region. Seismol Res Lett 80(4):609–627. https:\u002F\u002Fdoi.org\u002F10.1785\u002Fgssrl.80.4.609\nYang JJ, Zhang YQ, Xie FR (2018) The evolutionary analysis of the stress field in the seismic focal zone of the great Tohoku-Oki earthquake (MW=9.0) in the Japan Trench subduction zone. 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ANFIS classifiers were used to detect seismic events using six inputs that defined the seismic events. Neuro-fuzzy coding was applied using the six extracted features as ANFIS inputs. Two types of events were defined: weak earthquakes and mining blasts. The data comprised 748 events (6289 signals) ranging from magnitude 1.1 to 4.6 recorded at 13 seismic stations between 2004 and 2009. We surveyed that there are almost 223 earthquakes with M ≤ 2.2 included in this database. Data sets from the south, east, and southeast of the city of Tehran were used to evaluate the best short period seismic discriminants, and features as inputs such as origin time of event, distance (source to station), latitude of epicenter, longitude of epicenter, magnitude, and spectral analysis (fc of the Pg wave) were used, increasing the rate of correct classification and decreasing the confusion rate between weak earthquakes and quarry blasts. The performance of the ANFIS model was evaluated for training and classification accuracy. 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BP, Shearer PM (2008) Spectral discrimination between quarry blasts and earthquakes in Southern California. Bull Seismol Soc Am 98:2073–2079",{"id":20,"text":1160,"url":20,"identifiers":20},"Amiri F, Lucas C, Yazdani N (2009) Anomaly detection using Neuro Fuzzy system. World Acad Sci Eng Technol 49:889–896",{"id":20,"text":1162,"url":20,"identifiers":20},"Berberian M (1976) Contribution to the seismotectonic of Iran, Part II. Rep. No. 39, Geological Survey of Iran",{"id":20,"text":1164,"url":20,"identifiers":20},"Brune JN (1970) Tectonic stress and the spectra of seismic shear wave from earthquakes. J Geophys Res 75:4,997–5,009",{"id":20,"text":1166,"url":20,"identifiers":20},"Dowla FU, Taylor SR, Anderson RW (1990) Seismic discrimination with artificial neural networks: preliminary results with regional spectral data. Bull Seismol Soc Am 80:1346–1373",{"id":20,"text":1168,"url":20,"identifiers":20},"Dubois D, Prade H (1998) An introduction to fuzzy systems. Clin Chim Acta 270:3–29. doi:10.1016\u002FS0009-8981(97)00232-5",{"id":20,"text":1170,"url":20,"identifiers":20},"Duda R, Hart P, Stork D (2001) Pattern classification. Wiley, New York, p 654",{"id":20,"text":1172,"url":20,"identifiers":20},"Dysart PS, Pulli JJ (1990) Regional seismic event classification at the NORESS array: seismological measurement and the used of trained neural network. Bull Seismol Soc Am 80:1910–1933",{"id":20,"text":1174,"url":20,"identifiers":20},"Gitterman Y, Shapira A (1993) Spectral discrimination of underwater explosions. Israel J Earth Sci 42:37–44",{"id":20,"text":1176,"url":20,"identifiers":20},"Gitterman Y, Pinsky V, Shapira A (1998) Spectral classification methods in monitoring small local events by the Israel seismic network. J Seismol 2:237–256",{"id":20,"text":1178,"url":20,"identifiers":20},"Havskov J, Ottemoller L (2005) SEISAN: the earthquake analysis software for Windows, Solaris, Linux and Mac OS X, Version 8.1, http:\u002F\u002Fwww.geociencias.unam.mx\u002F~gomez\u002FCurso_sismo\u002Fmanual_seisan_8.1.pdf",{"id":20,"text":1180,"url":20,"identifiers":20},"Jang R (1992) Self-learning fuzzy controllers based on temporal back propagation. IEEE Trans Neural Netw 3:714–723",{"id":20,"text":1182,"url":20,"identifiers":20},"Jang R (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685",{"id":20,"text":1184,"url":20,"identifiers":20},"Jang R, Sun C (1995) Neuro-fuzzy modeling and control. Proc IEEE 83(no. 3):378–406",{"id":20,"text":1186,"url":20,"identifiers":20},"Joswig M (1995) Automated classification of local earthquake data in the BUG small array. Geophys J Int 120:262–286",{"id":20,"text":1188,"url":20,"identifiers":20},"Kuncheva LI, Steimann F (1999) Fuzzy diagnosis. Artif Intell Med. doi:10.1016\u002FS0933-3657(98)00068-2",{"id":20,"text":1190,"url":20,"identifiers":20},"Kurian CP, George J, Bhat IJ, Aithal RS (2006) ANFIS model for the time series prediction of interior daylight illuminance. AIML J 6:35–40",{"id":20,"text":1192,"url":20,"identifiers":20},"Lee KC, Oh SB (1996) An intelligent approach to time series identification by a neural network-driven decision tree classifier. Decis Support Syst 17:183–197",{"id":20,"text":1194,"url":20,"identifiers":20},"Madariaga R (1976) Dynamics of an expanding circular fault. Bull Seismol Soc Am 66:639–666",{"id":20,"text":1196,"url":20,"identifiers":20},"Muller S, Garda P, Muller J, Crusem R, Canci Y (1999) Seismic events discrimination by neuro-fuzzy merging of signal and catalogue features. Phys Chem Earth 24(A):201–206",{"id":20,"text":1198,"url":20,"identifiers":20},"Musil M, Plesinger A (1996) Discrimination between local micro-earthquakes and quarry blast by multi-layer perceptrons and Kohonen maps. Bull Seismol Soc Am 80:1077–1090",{"id":20,"text":1200,"url":20,"identifiers":20},"Nauck D, Kurse R (1999) Obtaining interpretable fuzzy classification rules from medical data. Artif Intell Med 16:149–169. doi:10.1016\u002FS0933-3657(98)00070-0",{"id":20,"text":1202,"url":20,"identifiers":20},"Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27:1226–1238",{"id":20,"text":1204,"url":20,"identifiers":20},"Radeva ST, Radev D (2002) Fuzzy random vibration of hysteretic system subjected to earthquake. 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Rev Geophys 33:269–274",{"id":20,"text":1214,"url":20,"identifiers":20},"Vasheghani-Farahani J, Zaré M (2011) The southeastern Tehran earthquake of 17 October 2009 (M w = 4.0). Seismol Res Lett 82(3):404–412. doi:10.1785\u002Fgssrl.82.3.404",{"id":20,"text":1216,"url":20,"identifiers":20},"Wang J, Teng TL (1995) Artificial neural network-based seismic detector. Bull Seismol Soc Am 85:308–319",{"id":1218,"createTime":1219,"updateTime":1220,"relativeEntities":1221,"slug":1222,"properties":1223,"entityType":120,"verifyStatus":121,"verifyTime":1232,"verifyNote":123,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1233,"fullTextUrl":20,"authors":1234,"publicationType":176,"publisherRelationship":1275,"citationCount":21,"citationInfo":1326,"publishDate":1328,"publishYear":230,"citationAnalyzeStatus":19,"lastCitationAnalyze":1329,"indexDatabases":1330,"openAccess":20,"references":1331,"isForceReanalyzing":418},"037b008b-50b8-4374-801d-b853f8315dfd","2023-12-28T21:38:16.459+00:00","2026-05-12T11:29:39.939+00:00",[],"SWprocess-a-workflow-for-developing-robust-estimates-of-surface-wave-dispersion-uncertainty",{"abstract":1224,"title":1226,"gsPaper":1228,"doi":1230},{"EN":1225},"Non-invasive surface wave methods are increasingly being used as the primary technique for estimating a site’s small-strain shear wave velocity (Vs). Yet, in comparison to invasive methods, non-invasive surface wave methods suffer from highly variable standards of practice, with each company\u002Fgroup\u002Fanalyst estimating surface wave dispersion data, quantifying its uncertainty (or ignoring it in many cases), and performing inversions to obtain Vs profiles in their own unique manner. In response, this work presents a well-documented, production-tested, and easy-to-adopt workflow for developing estimates of experimental surface wave dispersion data with robust measures of uncertainty. This is a key step required for propagating dispersion uncertainty forward into the estimates of Vs derived from inversion. The paper focuses on the two most common applications of surface wave testing: the first, where only active-source testing has been performed, and the second, where both active-source and passive-wavefield testing has been performed. In both cases, clear guidance is provided on the steps to transform experimentally acquired waveforms into estimates of the site’s surface wave dispersion data and quantify its uncertainty. In particular, changes to surface wave data acquisition and processing are shown to affect the resulting experimental dispersion data, thereby highlighting their importance when quantifying uncertainty. In addition, this work is accompanied by an open-source Python package, swprocess, and associated Jupyter workflows to enable the reader to easily adopt the recommendations presented herein. It is hoped that these recommendations will lead to further discussions about developing standards of practice for surface wave data acquisition, processing, and inversion.",{"EN":1227},"SWprocess: a workflow for developing robust estimates of surface wave dispersion uncertainty",{"VOID":1229},"4376245159498418307",{"VOID":1231},"10.1007\u002Fs10950-021-10035-y","2024-04-24T08:25:44.744+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10950-021-10035-y",[1235,1255],{"id":1236,"sortIndex":21,"researcher":20,"roles":1237,"affiliations":1238,"properties":1250,"displayName":1252,"givenName":20,"familyName":20},"2a798b41-ff94-4ef7-ba90-dc1638f3e16b",[129],[1239],{"id":1240,"sortIndex":21,"affiliation":1241,"properties":1247},"817d1bb5-99c5-44ee-8612-22fcaf1a9a0f",{"id":1240,"createTime":20,"updateTime":20,"relativeEntities":1242,"slug":20,"properties":1243,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1246,"statistic":20},[],{"title":1244},{"VI":1245},"The University of Texas at Austin, Austin, United States",[],{"title":1248},{"VI":1249},"The University of Texas at Austin, Austin, USA",{"title":1251,"gsAuthor":1253},{"VI":1252},"Joseph P. Vantassel",{"VOID":1254},"tKGF968AAAAJ",{"id":1256,"sortIndex":96,"researcher":20,"roles":1257,"affiliations":1258,"properties":1270,"displayName":1272,"givenName":20,"familyName":20},"ee3891ce-77d8-4cc4-9f69-86f48eb7854f",[129],[1259],{"id":1260,"sortIndex":21,"affiliation":1261,"properties":1267},"bbac4c9f-25d9-4489-a41f-2aec156ec2da",{"id":1260,"createTime":20,"updateTime":20,"relativeEntities":1262,"slug":20,"properties":1263,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1266,"statistic":20},[],{"title":1264},{"VI":1265},"Utah State University, Logan, U.S.A.",[],{"title":1268},{"VI":1269},"Utah State University, Logan, USA",{"title":1271,"gsAuthor":1273},{"VI":1272},"Brady R. 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American Society of Civil Engineers, Atlanta, Georgia, United States, pp. 845–852. https:\u002F\u002Fdoi.org\u002F10.1061\u002F41183(418)89","https:\u002F\u002Fdoi.org\u002F10.1061\u002F41183(418)89",{"mag":1363,"openalex":1364,"doi":1365},"2321452276","W2321452276","10.1061\u002F41183(418)89",{"id":20,"text":1367,"url":1368,"identifiers":1369},"Cox BR, Wood CM, Teague DP (2014) Synthesis of the UTexas1 surface wave dataset blind-analysis study: inter-analyst dispersion and shear wave velocity uncertainty. In: Geo-Congress 2014 technical papers. Presented at the Geo-Congress 2014. American Society of Civil Engineers, Atlanta, Georgia, pp. 850–859. https:\u002F\u002Fdoi.org\u002F10.1061\u002F9780784413272.083","https:\u002F\u002Fdoi.org\u002F10.1061\u002F9780784413272.083",{"mag":1370,"openalex":1371,"doi":1372},"2083431067","W2083431067","10.1061\u002F9780784413272.083",{"id":20,"text":1374,"url":1375,"identifiers":1376},"Dikmen Ü, Arısoy MÖ, Akkaya İ (2010) Offset and linear spread geometry in the MASW method. 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Geophys J Int, ISSN: 0956-540X. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1365-246X.2009.04471.x",{"doi":1819},"10.1111\u002Fj.1365-246X.2009.04471.x",{"id":20,"text":1821,"url":20,"identifiers":1822},"Picozzi M, Oth A, Parolai S, Bindi D, De Landro G, Amoroso O (2017) Accurate estimation of seismic source parameters of induced seismicity by a combined approach of generalized inversion and genetic algorithm: application to the Geysers geothermal area, California. J Geophys Res Solid Earth 122:3916–3933",{"doi":1823},"10.1002\u002F2016JB013690",{"id":20,"text":1825,"url":20,"identifiers":1826},"Priolo E, Romanelli M, Plasencia Linares M, Garbin M, Peruzza L, Romano MA, Marotta P, Bernardi P, Moratto L, Zuliani D, Fabris P (2015) Seismic monitoring of an underground natural gas storage facility: the Collalto seismic network. Seismol Res Lett 86:109–123",{"doi":1827},"10.1785\u002F0220140087",{"id":20,"text":1829,"url":20,"identifiers":1830},"Rabinowitz N, Steinberg DM (1990) Optimal configuration of a seismographic network: a statistical approach. Bull Seismol Soc Am 80(1):187–196",{"doi":1831},"10.1785\u002FBSSA0800010187",{"id":20,"text":1833,"url":20,"identifiers":1834},"Raymer DG, Leslie HD (2011) Microseismic network design-estimating event detection. In 73rd EAGE Conference and Exhibition incorporating SPE EUROPEC 2011",{},{"id":20,"text":1836,"url":20,"identifiers":1837},"Rost S, Thomas C (2009) Improving seismic resolution through array processing techniques. Surv Geophys 30:271–299",{"doi":1838},"10.1007\u002Fs10712-009-9070-6",{"id":20,"text":1840,"url":20,"identifiers":1841},"Schorlemmer D, Woessner J (2008) Probability of detecting an earthquake. Bull Seismol Soc Am 98(5):2103–2117",{"doi":1842},"10.1785\u002F0120070105",{"id":20,"text":1844,"url":20,"identifiers":1845},"Schweitzer J, Fyen J, Mykkeltveit S, Kværna T, Bormann P (2002) Seismic arrays. IASPEI new manual of seismological observatory practice. pp 1–51",{},{"id":20,"text":1847,"url":20,"identifiers":1848},"Stabile TA, De Matteis R, Zollo A (2009) Method for rapid high-frequency seismogram calculation. Comput Geosci 35(2):409–418",{"doi":1849},"10.1016\u002Fj.cageo.2008.02.030",{"id":20,"text":1851,"url":20,"identifiers":1852},"Stabile TA, Iannaccone G, Zollo A, Lomax A, Ferulano MF, Vetri MLV, Barzaghi LP (2013) A comprehensive approach for evaluating network performance in surface and borehole seismic monitoring. Geophys J Int 192(2):793–806",{"doi":1853},"10.1093\u002Fgji\u002Fggs049",{"id":20,"text":1855,"url":20,"identifiers":1856},"Steck LK, Velasco AA, Cogbill AH, Patton HJ (2001) Improving regional seismic event location in China. Pure Appl Geophys 158:211–240",{"doi":1857},"10.1007\u002FPL00001157",{"id":20,"text":1859,"url":20,"identifiers":1860},"Uhrhammer RA (1980) Analysis of small seismographic station networks. Bull Seismol Soc Am 70(4):1369–1379",{"doi":1861},"10.1785\u002FBSSA0700041369",{"id":20,"text":1863,"url":20,"identifiers":1864},"Vassallo M, Bobbio A, Iannaccone G (2008) A comparison of sea-floor and on-land seismic ambient noise in the Campi Flegrei caldera, southern Italy. Bull Seismol Soc Am 98(6):2962–2974",{"doi":1865},"10.1785\u002F0120070152",{"id":20,"text":1867,"url":20,"identifiers":1868},"Venisti N, Calcagnile G, Pontevivo A, Panza GF (2005) Tomographic study of the Adriatic plate. Pure Appl Geophys 162(2):311–329",{"doi":1869},"10.1007\u002Fs00024-004-2602-6",{"id":20,"text":1871,"url":20,"identifiers":1872},"Zeiler C, Velasco AA (2009) Seismogram picking error from analyst review (SPEAR): single-analyst and institution analysis. Bull Seismol Soc Am 99(5):2759–2770",{"doi":1873},"10.1785\u002F0120080131",{"id":1875,"createTime":1876,"updateTime":1877,"relativeEntities":1878,"slug":1879,"properties":1880,"entityType":120,"verifyStatus":121,"verifyTime":1891,"verifyNote":123,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1892,"fullTextUrl":20,"authors":1893,"publicationType":176,"publisherRelationship":1956,"citationCount":20,"citationInfo":20,"publishDate":2008,"publishYear":2009,"citationAnalyzeStatus":19,"lastCitationAnalyze":2010,"indexDatabases":2011,"openAccess":20,"references":20,"isForceReanalyzing":418},"6e512f69-b4bd-40e1-80fa-438c01fe1840","2024-01-11T08:47:53.076+00:00","2026-04-03T19:09:40.018+00:00",[],"Crustal-structure-of-Northwest-Zagros-Kermanshah-and-Central-Iran-Yazd-and-Isfahan-using-teleseismic-Ps-converted-phases",{"abstract":1881,"title":1883,"gsPaper":1885,"references":1887,"doi":1889},{"EN":1882},"Receiver functions are widely employed to detect P-to-S converted waves and are especially useful to image seismic discontinuities in the crust. In this study we used the P receiver function technique to investigate the velocity structure of the crust beneath the Northwest Zagros and Central Iran and map out the lateral variation of the Moho boundary within this area. Our dataset includes teleseismic data (M\n                        b ≥ 5.5, epicentral distance from 30° to 95°) recorded at 12 three-component short-period stations of Kermanshah, Isfahan and Yazd telemetry seismic networks. Our results obtained from P receiver functions indicate clear Ps conversions at the Moho boundary. The Moho depths were firstly estimated from the delay time of the Moho converted phase relative to the direct P wave beneath each network. Then, we used the P receiver function inversion to find the properties of the Moho discontinuity such as depth and velocity contrast. Our results obtained from PRF are in good agreement with those obtained from the P receiver function modeling. We found an average Moho depth of about 42 km beneath the Northwest Zagros increasing toward the Sanandaj-Sirjan Metamorphic Zone and reaches 51 km, where two crusts (Zagros and Central Iran) are assumed to be superposed. The Moho depth decreases toward the Urmieh-Dokhtar Cenozoic volcanic belt and reaches 43 km beneath this area. We found a relatively flat Moho beneath the Central Iran where, the average crustal thickness is about 42 km. Our P receiver function modeling revealed a shear wave velocity of 3.6 km\u002Fs in the crust of Northwest Zagros and Central Iran increasing to 4.5 km\u002Fs beneath the Moho boundary. The average shear wave velocity in the crust of UDMA as SSZ is 3.6 km\u002Fs, which reaches to 4.0 km\u002Fs while in SSZ increases to 4.3 km\u002Fs beneath the Moho.",{"EN":1884},"Crustal structure of Northwest Zagros (Kermanshah) and Central Iran (Yazd and Isfahan) using teleseismic Ps converted phases",{"VOID":1886},"[\"11271467491758125368\"]",{"VOID":1888},"Afsari N (2008) Crustal structure and azimuthal shear wave velocity anisotropy in Northwest of Zagros (Kermanshah) and Central Iran (Yazd and Isfahan) using teleseismic Ps converted phases. PhD thesis, Science and Research Branch, Islamic Azad University (IAU)\nAfsari N, Sodoudi F, Gheitanchi MR, Kaviani A (2010) Moho depth variations and vp\u002Fvs ratio in Northwest of Zagros (Kermanshah Region) using teleseismic receiver functions. Geoscience 19(74):45–50\nAgard P, Omrani L, Jolivet, Mouthereau F (2005) Convergence history across Zagros (Iran): constrains from collisional and earlier deformation. Int J Earth Sci 94:401–419. doi:10.1007\u002Fs00531-005-0481-4\nAmmon CJ (1990) On the nonuniqueness of receiver function inversions. J Geophys Res 95:2504–2510\nAsudeh I (1982) Seismic structure of Iran from surface and body wave data. Geophys J R Astron Soc 71:715–730\nBerberian M (1995) Master blind thrust faults hidden under the Zagros folds: active basement tectonics and surface morphotectonics. Tectonophysics 241:193–224\nBerberian M, King GCP (1981) Towards a paleogeography and tectonic evolution of Iran. Can J Earth Sci 18:210–265\nDavoudzadeh M, Lammerer B, Weber-Diefenbach K (1997) Paleogeography, stratigraphy, and tectonics of the tertiary of Iran. N Jb Geol Palàont Abh 205:33–67\nDehgani G, Makris J (1984) The gravity field and crustal structure of Iran. N Jb Geol Palaont Abh 168:215–229\nDewey JW, Grantz A (1973) The Ghir earthquake of April 10, 1972 in the Zagros mountains of southern Iran; seismotectonic aspects and some results of a field reconnaissance. Bull Seismol Soc Am 63:2071–2090\nFalcon NL (1974) Southern Iran: Zagros mountains. Spec Pub Geol Soc Lond 4:199–211\nGiese P, Makris J, Akashe B, Röwer P, Letz H, Mostaanpour M (1984) The crustal structure in Southern Iran derived from seismic explosion data. N Jb Geol Palaeont Abh 168:230–243\nHaskell NA (1962) Crustal reflections of plane P and SV waves. J Geophys Res 67:4751–4767\nHatzfeld D, Tatar M, Priestley K, Ghafory-Ashtiany M (2003) Seismological constrains on the crustal structure beneath the Zagros mountain belt (Iran). Geophys J Int 155:403–410\nJackson J, Haines J, Holt W (1995) The accommodation of Arabia-Eurasia plate convergence in Iran. J Geophys Res 100:15205–15219\nKennett BLN, Engdahl ER (1991) Travel times for global earthquake location and phase identification. Geophys J Int 105:429–465\nKind R, Kosarve G, Peterson NV (1995) Receiver function at the stations of the German Regional Seismic Network (GRSN). Geophys J Int 121:191–202\nPaul A, Kaviani A, Hatzfeld D, Vegne J, Mokhtari M (2006) Seismological evidence for crustal- scale thrusting in the Zagros mountain belt (Iran). Geophys J Int 166:227–237. doi:10.1111\u002Fj.1365-24x.2006.02920.x\nPaul A, Hatzfeld D, Kaviani A, Tatar M, Pequegnat C (2010) Seismic imaging of the lithospheric structure of the Zagros mountain belt (Iran). Geol Soc London Special Publications 330:5–18. doi:10.1144\u002FSP330.2\nRamesh DS, Wakatsu HK, Watada S, Yuan X (2005) Receiver function images of the central Chugoku region in the Japanese islands using Hi-net data. Earth Planets Space 57(4):271–280\nRicou LE, Braud J, Brunn J (1977) Le Zagros Mem. h. ser. Soc Geol Fr 8:33–52\nSnyder DB, Barazangi M (1986) Deep crustal structure and flexture of the Arabian plate beneath the Zagros collisional mountain belt as inferred from gravity observation. Tectonics 5:361–373\nSodoudi F, Kind R, Priestly W, Hanka W, Wylegalla K, Stavrakakis G, Vafidis A, Harjes HP, Bohnhoff M (2006) Lithospheric structure of the Aegean obtained from P and S receiver functions. J Geophys Res 11:12307–12330\nSodoudi F, Yuan X, Kind R, Heit B, Sadidkhouy A (2009) Evidence for a missing crustal root and a thin lithosphere beneath the Central Alborz by receiver function studies. Geophys J Int 177(2):733–742\nStammler K (1993) Seismichandler-programmable multichannel data handler for interactive and automatic processing of seismological analyses. Comput Geosci 19:135–140\nStöcklin J (1968) Structural history and tectonics of Iran: a review. AAPG Bull 52:1229–1258\nStöcklin J (1974) Possible ancient continental margin in Iran. In: Burke C, Drake C (eds) Geology of continental margins. Springer, New York, pp 873–877\nStoneley R (1981) The geology of the Kuh-e Dalnesh area of Southern Iran, and its bearing on the evolution of Southern Tethys. J Geol Soc Lond 138:509–526\nTaghizadeh-Farahmand F, Sodoudi F, Afsari N, Ghassemi MR (2010) Lithospheric structure of NW Iran from P and S receiver functions. J Seismology 14:823–836. doi:10.1007\u002Fs10950-010-9199-2\nTalebian M, Jackson J (2004) A reappraisal of earthquake focal mechanisms and active shortening in the Zagros mountains of Iran. Geophys J Int 156:506–526\nTchalenko JS, Braud J (1974) Seismicity and structure of the Zagros (Iran): in the Main Recent Fault between 33o and 35o N. Philos Trans R Soc Lond 227:1–25\nVernant P, Niloforoushan F, Hatzfeld D, Abbassi MR, Vigny C, Masson F, Nankali H, Martinod J, Ashtiani A, Bayer R, Tavakoli F, Chery J (2004) Present- day crustal deformation and plate kinematics in the Middle East constrained by GPS measurements in Iran and northern Oman. Geophys J Int 157:381–398\nWessel P, Smith WHF (1998) New, improved version of Generic Mapping Tools Released. EOS Trans Am Geophys Union 79:579\nWoelbern I, Rümpker G, Schumann A, Muwanga A (2010) Crustal thinning beneath the Rwenzori region Albertine rift, Uganda, from receiver function analysis. Int J Earth Sci 99:1545–1557. doi:10.1007\u002Fs00531-009-0509-2\nZhu L (2000) Crustal structure across the San Andreas Fault, southern California from teleseismic converted waves. Earth Planet Sci Lett 179:183–190",{"VOID":1890},"10.1007\u002Fs10950-011-9227-x","2024-05-14T08:26:58.883+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10950-011-9227-x",[1894,1909,1924,1941],{"id":1895,"sortIndex":21,"researcher":20,"roles":1896,"affiliations":1897,"properties":1906,"displayName":1908,"givenName":20,"familyName":20},"89d0aa0b-45b2-4350-9acd-948ea75b7c69",[129],[1898],{"id":1899,"sortIndex":21,"affiliation":1900,"properties":20},"8d63b427-e08e-4a7a-a790-83b8f2ebfdce",{"id":1899,"createTime":20,"updateTime":20,"relativeEntities":1901,"slug":20,"properties":1902,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1905,"statistic":20},[],{"title":1903},{"VI":1904},"Department of Physics, Parand Branch, Islamic Azad University, Tehran, Iran",[],{"title":1907},{"VI":1908},"Narges Afsari",{"id":1910,"sortIndex":96,"researcher":20,"roles":1911,"affiliations":1912,"properties":1921,"displayName":1923,"givenName":20,"familyName":20},"916ea7db-91c4-4205-a8f6-f0f71c18eaf5",[129],[1913],{"id":1914,"sortIndex":21,"affiliation":1915,"properties":20},"106a2043-3a53-427a-88ea-fb6efa852939",{"id":1914,"createTime":20,"updateTime":20,"relativeEntities":1916,"slug":20,"properties":1917,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1920,"statistic":20},[],{"title":1918},{"VI":1919},"GFZ Research Center for Geosciences, Helmholtz Center Potsdam, Potsdam, Germany",[],{"title":1922},{"VI":1923},"Forogh Sodoudi",{"id":1925,"sortIndex":97,"researcher":20,"roles":1926,"affiliations":1927,"properties":1936,"displayName":1938,"givenName":20,"familyName":20},"0dd47303-c6a8-47b0-8605-513c728c78d8",[129],[1928],{"id":1929,"sortIndex":21,"affiliation":1930,"properties":20},"793b3a84-b241-40ba-b420-12e36c23f2df",{"id":1929,"createTime":20,"updateTime":20,"relativeEntities":1931,"slug":20,"properties":1932,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1935,"statistic":20},[],{"title":1933},{"VI":1934},"Department of Physics, Qom Branch, Islamic Azad University, Qom, Iran",[],{"title":1937,"gsAuthor":1939},{"VI":1938},"Fataneh Taghizadeh Farahmand",{"VOID":1940},"[\"vt32RLEAAAAJ\"]",{"id":1942,"sortIndex":93,"researcher":20,"roles":1943,"affiliations":1944,"properties":1953,"displayName":1955,"givenName":20,"familyName":20},"73321c5a-b9ab-476c-bfac-2b1f6bf678c0",[129],[1945],{"id":1946,"sortIndex":21,"affiliation":1947,"properties":20},"dc6808ec-008d-4be6-87e4-bcf748c37b42",{"id":1946,"createTime":20,"updateTime":20,"relativeEntities":1948,"slug":20,"properties":1949,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1952,"statistic":20},[],{"title":1950},{"VI":1951},"Research Institute for Earth Science, Geological Survey of Iran, Tehran, Iran",[],{"title":1954},{"VI":1955},"Mohammad Reza Ghassemi",{"url":1892,"publisher":1957,"properties":2003},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1958,"slug":10,"properties":1959,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":1963,"manageAffiliations":1972,"indexDatabases":1983,"url":87,"thumbnailPath":20,"statistic":1998,"gsStatistic":20,"type":100,"analyzePriority":20},[],{"issn":1960,"title":1961,"eissn":1962},{"VOID":13},{"EN":15},{"VOID":17},[1964,1968],{"id":24,"createTime":20,"updateTime":20,"relativeEntities":1965,"label":1966,"description":1967,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":27},{},{"id":30,"createTime":20,"updateTime":20,"relativeEntities":1969,"label":1970,"description":1971,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":33},{},[1973,1978],{"id":37,"createTime":20,"updateTime":20,"relativeEntities":1974,"slug":20,"properties":1975,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1977,"statistic":20},[],{"title":1976},{"EN":41},[43],{"id":45,"createTime":20,"updateTime":20,"relativeEntities":1979,"slug":20,"properties":1980,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1982,"statistic":20},[],{"title":1981},{"EN":49},[],[1984,1991],{"id":53,"indexDatabase":1985,"url":66,"indexYears":20,"academicFieldIds":1990,"indexDatabaseRanking":20},{"id":55,"createTime":20,"updateTime":20,"relativeEntities":1986,"label":1987,"description":1988,"key":62,"publicationTags":1989,"standard":20},[],{"EN":58,"VI":58},{"EN":60,"VI":61},[64,65],[68],{"id":70,"indexDatabase":1992,"url":81,"indexYears":82,"academicFieldIds":1997,"indexDatabaseRanking":86},{"id":72,"createTime":20,"updateTime":20,"relativeEntities":1993,"label":1994,"description":1995,"key":78,"publicationTags":1996,"standard":20},[],{"EN":75,"VI":75},{"EN":75,"VI":77},[80],[84,85],{"impactFactor":21,"impactFactorByYear":1999,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":90,"totalPublicationByYear":2000,"totalCitation":21,"totalCitationByYear":2001,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":2002,"hindexLast5Year":21,"hindex":21},{},{"1997":92,"1998":93,"1999":94,"2000":92,"2001":95,"2002":94,"2003":96,"2005":96,"2006":96,"2007":96,"2008":96,"2012":97,"2013":96,"2016":97,"2018":97,"2020":96,"2022":93},{},{},{"pages":2004,"volume":2006},{"VOID":2005},"341-353",{"VOID":2007},"15","2011-01-26",2011,"2026-04-03T19:09:40.017+00:00",[86,64],{"id":2013,"createTime":2014,"updateTime":2015,"relativeEntities":2016,"slug":2017,"properties":2018,"entityType":120,"verifyStatus":121,"verifyTime":2030,"verifyNote":123,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":2031,"fullTextUrl":20,"authors":2032,"publicationType":176,"publisherRelationship":2050,"citationCount":20,"citationInfo":20,"publishDate":2097,"publishYear":2098,"citationAnalyzeStatus":19,"lastCitationAnalyze":2099,"indexDatabases":2100,"openAccess":20,"references":20,"isForceReanalyzing":418},"ff0ef9b8-8278-40ac-80e1-97f141a966b0","2024-04-05T17:58:34.903+00:00","2026-03-18T05:26:54.805+00:00",[],"An-application-of-the-time-and-magnitude-predictable-model-to-long-term-earthquake-prediction-in-eastern-Anatolia",{"abstract":2019,"title":2021,"gsPaper":2023,"keywords":2025,"references":2026,"doi":2028},{"EN":2020},"In order to estimate the recurrence intervals for large earthquakes occurring in eastern Anatolia, this region enclosed within the coordinates of 36∘–42∘N, 35∘–45∘E has been separated into nine seismogenic sources on the basis of certain seismological and geomorphological criteria, and a regional time- and magnitude-predictable model has been applied for these sources. This model implies that the magnitude of the preceding main shock which is the largest earthquake during a seismic excitation in a seismogenic source governs the time of occurrence and the magnitude of the expected main shock in this source. The data belonging to both the instrumental period (M\n                        \n                  S\n                ≥ 5.5) until 2003 and the historical period (I\n                        0≥ 9.0 corresponding to M\n                        \n                  S\n                ≥ 7.0) before 1900 have been used in the analysis. The interevent time between successive main shocks with magnitude equal to or larger than a certain minimum magnitude threshold were considered in each of the nine source regions within the study area. These interevent times as well as the magnitudes of the main shocks have been used to determine the following relations:\n\n                  \n                    \n                  \n                  $$\\log T_t= 0.11M_{\\min} - 0.12M_p - 0.11\\log M_0 + 2.51$$\n                \n                        \n                  \n                    \n                  \n                  $$M_f= 0.89M_{\\min} - 0.24M_p + 0.31\\log M_0 - 4.99$$\n                \nfwawhere T\n                        \n                  t\n                 is the interevent time measured in years, M\n                        min is the surface wave magnitude of the smallest main shock considered, M\n                        \n                  p\n                 is the magnitude of the preceding main shock, M\n                        \n                  f\n                 is magnitude of the following main shock, and M\n                        0 is the released seismic moment per year in each source. Multiple correlation coefficient and standard deviation have been computed as 0.50 and 0.28, respectively for the first relation. The corresponding values for the second relation are 0.64 and 0.32, respectively. It was found that the magnitude of the following main shock M\n                        \n                  f\n                 does not depend on the preceding interevent time T\n                        \n                  t\n                . This case is an interesting property for earthquake prediction since it provides the ability to predict the time of occurrence of the next strong earthquake. On the other hand, a strong negative dependence of M\n                        \n                  f\n                 on M\n                        \n                  p\n                 was found. This result indicates that a large main shock is followed by a smaller magnitude one and vice versa. On the basis of the first one of the relations above and taking into account the occurrence time and magnitude of the last main shock, the probabilities of occurrence P(Δ t) of main shocks in each seismogenic source of the east Anatolia during the next 10, 20, 30, 40 and 50 years for earthquakes with magnitudes equal 6.0 and 7.0 were determined. The second of these relations has been used to estimate the magnitude of the expected main shock. According to the time- and magnitude-predictable model, it is expected that a strong and a large earthquake can occur in seismogenic Source 2 (Erzincan) with the highest probabilities of P\n                        10 = 66% (M\n                        \n                  f\n                 = 6.9 and T\n                        \n                  t\n                 = 12 years) and P\n                        10 = 44% (M\n                        \n                  f\n                 = 7.3 and T\n                        \n                  t\n                 = 24 years) during the future decade, respectively.",{"EN":2022},"An application of the time- and magnitude-predictable model to long-term earthquake prediction in eastern Anatolia",{"VOID":2024},"[\"14050591916045778768\"]",{"EN":1046},{"VOID":2027},"Acharya, H.K., 1979, A method to determine the duration of quisence in a seismic gap, Geophys. Res. Letters 6, 681–684.\nAlsan, E., Tezuçan, L. and Bath, M., 1975, An earthquake catalogue for Turkey for the interval 1913–1970, Report Kandilli Obs., Istanbul and Uppsala Univ., Sweden.\nAmbraseys, N.N. and Jackson, J.A., 1981, Earthquake hazard and vulnerability in the northeastern mediterranean, the Corinth earthquake sequence of February–March 1981, Disaster 5, 355–368.\nAyhan, E., Alsan, E., Sancaklı, N. and Üçer, S.B., 1987, An Earthquake Catalogue of Turkey and Surrounding Area (1881–1980), Bogazici University, Istanbul.\nBufe, C.G., Harsh, P.W. and Burford, R.O., 1977, Steady-state seismic slip: A precise recurrence model, Geophys. Res. Letters 4, 91–94.\nDewey, J.F., 1976, Seismicity of North Anatolia, Bull. Seism. Soc. Am. 66, 843–868.\nErgin, K., 1967, An Earthquake Catalogue of Turkey and Surrounding Area (from A.D. 11 to close of 1964), Istanbul Tech. Univ. Mining Faculty, Istanbul.\nEkström, G. and Dziewonski, A., 1988, Evidence of bias in estimations of earthquake size, Nature 332, 319–323.\nGutenberg, B. and Richter, C.F., 1944, Frequency of eathquakes in California, Bull. Seism. Soc. Am. 34, 185–188.\nGündogdu, O. and Altınok, Y., 1986, An Earthquake Data Set of Turkey and Surrounding Area 1900–1986, Istanbul Univ., Engineering Faculty, Dept. of Geophys., Istanbul.\nJackson, J. and McKenzie, D., 1988, The relationship between plate motion and seismic moment tensors, and the rate of active deformation in the Mediterranean and Middle East, Geophys. J. 93, 45–73.\nKanamori, H., 1977, The energy released in Great Earthquakes, J. Geophys. Res., 82, 2981–2987.\nKarakaisis, G.F., Kourouzidis, M.C. and Papazachos, B.C., 1991, Behaviour of seismic activity during a single seismic cycle, Int. Conf. Earthq. Pred. Strasbourg, 15–18 October 1991, 1, 47–54.\nKarakaisis, G.F., 1994a, Long-term earthquake prediction along the north and east anatolian fault zones based on the time and magnitude predictable model, Geophys. J. Int. 116, 198–204.\nKarakaisis, G.F., 2000, Effects of zonation on the results of the application of the regional time predictable seismicity model in greece and japan, Earth Planets Space 52, 221–228.\nKarnik, V., 1968, Seismicity of the European Area, D. Reidel Publ. Com., Dordreed, Holland.\nKenar, Ö., Osmansahin, İ. and Özer, M.F., 1996, Seismicity and tectonics of Eastern Anatolia, Bull. IISEE 30, 59–76.\nKetin, İ., 1977, General Geology, 1, İ.T.Ü. Faculty of Mining Publication, İstanbul.\nMcGuire, R.K., 1976, Fortran Computer Program for Seismic Risk Analysis, USGS open File Report, 76–67.\nMcKenzie, D., 1972, Active tectonics of the mediterranean region, Geophys. J. R. Astr. Soc. 30, 109–185.\nMolnar, P., 1979, Earthquake recurrence intervals and plate tectonics, Bull. 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Geophys. 149, 173–217.\nPapazachos, B.C., Karakaisis, G.F., Papadimitriou, E.E. and Papaioannou, Ch.A., 1997b, The Regional time and magnitude predictable model and its application to the alpine-himalayan belt, Tectonophys 271, 295–323.\nParsons, T., Toda, S. and Stein, R.S., 2000, Heightened odds of large earthquakes near Istanbul: An interaction-based probability calculation, Science 288, 661–665.\nPinar, N. and Lahn, E., 1952, Türkiye Earthquake Catalogue, Ministry of Public Works, 96.\nSayil, N. and Osmansahin, İ., 2003, Investigation of Seismicity of the Eastern Anatolia, Kocaeli 2003 Earthquake Symposium, 2003 November, İzmit, Türkiye, 580–589.\nShimazaki, K. and Nakata, T., 1980, Time predictable recurrence of great earthquakes, Geophys. Res. Letters 7, 279–282.\nSoysal, H., Sipahioglu, S., Kolçak, D. and Y. Altınok, 1981, Historical Earthquake Catalogue of Türkiye and Surrounding, Tübitak, TBAG 341, Ankara.\nSykes, L.R. and Quittmeyer, R.C., 1981, Repeat times of Great Earthquakes along simple plate boundaries, Maurice Ewing Series 4, 297–332.\nStein, R.S., Barka, A.A. and Dietrich, J.H., 1997, Progressive failure on the North Anatolian fault since 1939 by earthquake stress triggering, Geophys. J. Intern. 128, 594–604.\nWeisberg, S., 1980, Applied Linear Regression, Wiley, New York 1980, p. 283.\nWesnousky, S.G., Scholz, K. and Matsuda, T., 1984, Integration of geological and seismological data for analysis of seismic hazard: A case study of Japan, Bull. Seism. Soc. 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