A positive and unlabeled learning algorithm for mineral prospectivity mapping
Tài liệu tham khảo
Agterberg, 1999, Logistic Regression and Weights of Evidence Modeling in Mineral Exploration, 483
Agterberg, 2005, Measuring the performance of mineral-potential maps, Nat. Resour. Res., 14, 1, 10.1007/s11053-005-4674-0
Ahmadi, 2016, Applying a sophisticated approach to predict CO2 solubility in brines: application to CO2 sequestration, Int. J. Low Carbon Technol., 11, 325, 10.1093/ijlct/ctu034
Ahmadi, 2012, New approach for prediction of asphaltene precipitation due to natural depletion by using evolutionary algorithm concept, Fuel, 102, 716, 10.1016/j.fuel.2012.05.050
Ahmadi, 2013, Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir, Appl. Soft Comput., 13, 1085, 10.1016/j.asoc.2012.10.009
Ahmadi, 2014, Evolving predictive model to determine condensate-to-gas ratio in retrograded condensate gas reservoirs, Fuel, 124, 241, 10.1016/j.fuel.2014.01.073
Ahmadi, 2014, Connectionist model predicts the porosity and permeability of petroleum reservoirs by means of petro-physical logs: application of artificial intelligence, J. Petrol. Sci. Eng., 123, 183, 10.1016/j.petrol.2014.08.026
Ahmadi, 2014, Robust intelligent tool for estimating dew point pressure in retrograded condensate gas reservoirs: application of particle swarm optimization, J. Petrol. Sci. Eng., 123, 7, 10.1016/j.petrol.2014.05.023
Ahmadi, 2015, A rigorous model to predict the amount of dissolved calcium carbonate concentration throughout oil field brines: side effect of pressure and temperature, Fuel, 139, 154, 10.1016/j.fuel.2014.08.044
Anantrasirichai, 2018, Application of machine learning to classification of volcanic deformation in routinely generated InSAR data, J. Geophys. Res.: Solid Earth, 123, 6592, 10.1029/2018JB015911
Bekker, 2018, Estimating the Class Prior in Positive and Unlabeled Data through Decision Tree Induction, 2712
Bekker, 2020, Learning from positive and unlabeled data: a survey, Mach. Learn., 109, 719, 10.1007/s10994-020-05877-5
Blanchard, 2010, Semi-supervised novelty detection, J. Mach. Learn. Res., 11, 2973
Bonham-Carter, 1994, 1
Carranza, 2008
Carranza, 2015, Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: application of random forests algorithm, Ore Geol. Rev., 71, 777, 10.1016/j.oregeorev.2014.08.010
Carranza, 2015, Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines), Comput. Geosci., 74, 60, 10.1016/j.cageo.2014.10.004
Castelli, 1996, The relative value of labeled and unlabeled samples in pattern recognition with an unknown mixing parameter, IEEE Trans. Inf. Theor., 42, 2102, 10.1109/18.556600
Chaudhari, 2012, Learning from Positive and Unlabelled Examples Using Maximum Margin Clustering, 465
Chen, 2015, Mineral potential mapping with a restricted Boltzmann machine, Ore Geol. Rev., 71, 749, 10.1016/j.oregeorev.2014.08.012
Chen, 2017, Mapping mineral prospectivity by using one-class support vector machine to identify multivariate geological anomalies from digital geological survey data, Aust. J. Earth Sci., 64, 639, 10.1080/08120099.2017.1328705
Chen, 2019, Isolation forest as an alternative data-driven mineral prospectivity mapping method with a higher data-processing efficiency, Nat. Resour. Res., 28, 31, 10.1007/s11053-018-9375-6
Chen, 2016, Effect of training strategy for positive and unlabelled learning classification: test on Landsat imagery, Remote Sensing Letters, 7, 1063, 10.1080/2150704X.2016.1217437
Cheng, 2007, Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China, Ore Geol. Rev., 32, 314, 10.1016/j.oregeorev.2006.10.002
Dagdelenler, 2016, Modification of seed cell sampling strategy for landslide susceptibility mapping: an application from the Eastern part of the Gallipoli Peninsula (Canakkale, Turkey), Bull. Eng. Geol. Environ., 75, 575, 10.1007/s10064-015-0759-0
Dundar, 2004, A cost-effective semisupervised classifier approach with kernels, IEEE Trans. Geosci. Rem. Sens., 42, 264, 10.1109/TGRS.2003.817815
Elkan, 2008, Learning Classifiers from Only Positive and Unlabeled Data, 213
Fabbri, 2008, On blind tests and spatial prediction models, Nat. Resour. Res., 17, 107, 10.1007/s11053-008-9072-y
Fawcett, 2006, An introduction to ROC analysis, Pattern Recogn. Lett., 27, 861, 10.1016/j.patrec.2005.10.010
Fung, 2005, Text classification without negative examples revisit, IEEE Trans. Knowl. Data Eng., 18, 6, 10.1109/TKDE.2006.16
Ge, 1981, Geological characteristics of the Makeng iron deposit of marine volcano-sedimentary origin, Acta Geol. Sin., 3, 47
Guo, 2011, Predicting potential distributions of geographic events using one-class data: concepts and methods, Int. J. Geogr. Inf. Sci., 25, 1697, 10.1080/13658816.2010.546360
Han, 1983, Geological and geochemical features of submarine volcanic hydrothermal-sedimentary mineralization of Makeng iron deposit, Fujian province, Bull. Inst. Miner. Deposits, Chin. Acad. Geol. Sci., 7, 1
Karpatne, 2018, Machine learning for the geosciences: challenges and opportunities, IEEE Trans. Knowl. Data Eng., 31, 1544, 10.1109/TKDE.2018.2861006
Ke, 2018, Global and local learning from positive and unlabeled examples, Appl. Intell., 48, 2373, 10.1007/s10489-017-1076-z
Khan, 2014, One-class classification: taxonomy of study and review of techniques, Knowl. Eng. Rev., 29, 345, 10.1017/S026988891300043X
Kothari, 2003, Learning from labeled and unlabeled data using a minimal number of queries, IEEE Trans. Neural Network., 14, 1496, 10.1109/TNN.2003.820446
Kuhn, 2018, Lithologic mapping using Random Forests applied to geophysical and remote-sensing data: a demonstration study from the Eastern Goldfields of Australia, Geophysics, 83, B183, 10.1190/geo2017-0590.1
Lai, 2014, Petrogeochemical features and zircon LA-ICP-MS U-Pb ages of granite in the Pantian iron ore deposit, Fujian province and their relationship with mineralization, Acta Petrol. Sin., 30, 1780
Li, 2010, A positive and unlabeled learning algorithm for one-class classification of remote-sensing data, IEEE Trans. Geosci. Rem. Sens., 49, 717, 10.1109/TGRS.2010.2058578
Li, 2020, Convolutional neural network and transfer learning based mineral prospectivity modeling for geochemical exploration of Au mineralization within the Guandian–Zhangbaling area, Anhui Province, China, Appl. Geochem., 122, 104747, 10.1016/j.apgeochem.2020.104747
Li, 2020, Random-drop data augmentation of deep convolutional neural network for mineral prospectivity mapping, Nat. Resour. Res.
Lin, 2011
Liu, 2003, Building text classifiers using positive and unlabeled examples, 179
Liu, 2002, vol.2, 387
Liu, 2008, Partially Supervised Classification: Based on Weighted Unlabeled Samples Support Vector Machine, 1216
Manevitz, 2001, One-class SVMs for document classification, J. Mach. Learn. Res., 2, 139
Moosavi, 2019, ANN-based prediction of laboratory-scale performance of CO2-foam flooding for improving oil recovery, Nat. Resour. Res., 28, 1619, 10.1007/s11053-019-09459-8
Mosavi, 2018, Flood prediction using machine learning models: literature review, Water, 10, 1536, 10.3390/w10111536
Mulder, 2011, The use of remote sensing in soil and terrain mapping—a review, Geoderma, 162, 1, 10.1016/j.geoderma.2010.12.018
Nefeslioglu, 2008, An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps, Eng. Geol., 97, 171, 10.1016/j.enggeo.2008.01.004
Negnevitsky, 2002, 394
Nigam, 2000, Text classification from labeled and unlabeled documents using EM, Mach. Learn., 39, 103, 10.1023/A:1007692713085
Nykänen, 2015, Receiver operating characteristics (ROC) as validation tool for prospectivity models—a magmatic Ni–Cu case study from the Central Lapland Greenstone Belt, Northern Finland, Ore Geol. Rev., 71, 853, 10.1016/j.oregeorev.2014.09.007
Porwal, 2006, Bayesian network classifiers for mineral potential mapping, Comput. Geosci., 32, 1, 10.1016/j.cageo.2005.03.018
Pourghasemi, 2013, Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran, Journal of Earth System Science, 122, 349, 10.1007/s12040-013-0282-2
Press, 2008, Earth science and society, Nature, 451, 301, 10.1038/nature06595
Reid, 2010, Earth system science for global sustainability: grand challenges, Science, 330, 916, 10.1126/science.1196263
Rodriguez-Galiano, 2014, Predictive modelling of gold potential with the integration of multisource information based on random forest: a case study on the Rodalquilar area, Southern Spain, Int. J. Geogr. Inf. Sci., 28, 1336, 10.1080/13658816.2014.885527
Rouet‐Leduc, 2017, Machine learning predicts laboratory earthquakes, Geophys. Res. Lett., 44, 9276, 10.1002/2017GL074677
Rumelhart, 1986, Learning representations by back-propagating errors, Nature, 323, 533, 10.1038/323533a0
Sen, 2012, On sampling strategies for small and continuous data with the modeling of genetic programming and adaptive neuro-fuzzy inference system, J. Intell. Fuzzy Syst., 23, 297, 10.3233/IFS-2012-0521
Sezer, 2014, An assessment on producing synthetic samples by fuzzy C-means for limited number of data in prediction models, Appl. Soft Comput., 24, 126, 10.1016/j.asoc.2014.06.056
Singer, 1996, Application of a feed-forward neural network in the search for Kuruko deposits in the Hokuroku district, Japan, Math. Geol., 28, 1017, 10.1007/BF02068587
Song, 2008, SVM-based data editing for enhanced one-class classification of remotely sensed imagery, Geosci. Rem. Sens. Lett. IEEE, 5, 189, 10.1109/LGRS.2008.916832
Sun, 2020, Data-driven predictive modelling of mineral prospectivity using machine learning and deep learning methods: a case study from southern jiangxi province, China, Minerals, 10, 102, 10.3390/min10020102
Swets, 1988, Measuring the accuracy of diagnostic systems, Science, 240, 1285, 10.1126/science.3287615
Tax, 1999, Support vector domain description, Pattern Recogn. Lett., 20, 1191, 10.1016/S0167-8655(99)00087-2
Tran, 2004, 310
Wang, 2015, vol.44, 450
Wang, 2020, Mapping mineral prospectivity via semi-supervised random forest, Nat. Resour. Res., 29, 189, 10.1007/s11053-019-09510-8
Xie, 1997, Geochemical mapping in China, J. Geochem. Explor., 60, 99, 10.1016/S0375-6742(97)00029-0
Xie, 2018, Evaluation of machine learning methods for formation lithology identification: a comparison of tuning processes and model performances, J. Petrol. Sci. Eng., 160, 182, 10.1016/j.petrol.2017.10.028
Xiong, 2016, Recognition of geochemical anomalies using a deep autoencoder network, Computers & Geosciences, 86, 75, 10.1016/j.cageo.2015.10.006
Xiong, 2017, Effects of misclassification costs on mapping mineral prospectivity, Ore Geol. Rev., 82, 1, 10.1016/j.oregeorev.2016.11.014
Xiong, 2018, GIS-based rare events logistic regression for mineral prospectivity mapping, Comput. Geosci., 111, 18, 10.1016/j.cageo.2017.10.005
Xiong, 2018, Mapping mineral prospectivity through big data analytics and a deep learning algorithm, Ore Geol. Rev., 102, 811, 10.1016/j.oregeorev.2018.10.006
Yang, 2008, SHRIMP zircon U–Pb dating of quartz porphyry from Zhongjia tin–polymetallic deposit in Longyan area, Fujian Province, and its geological significance, Miner. Deposits, 27, 329
Yu, 2005, Single-class classification with mapping convergence, Mach. Learn., 61, 49, 10.1007/s10994-005-1122-7
Zhang, 2014, Sr–Nd–Pb isotope systematics of magnetite: implications for the genesis of Makeng Fe deposit, southern China, Ore Geol. Rev., 57, 53, 10.1016/j.oregeorev.2013.09.009
Zhang, 2012, LA-ICP-MS zircon U-Pb ages and Hf isotopic compositions of dayang granite from longyan, fujian province, Geoscience, 26, 434
Zhang, 2012, Geochronology of diagenesis and mineralization of the Luoyang iron deposit in Zhangping city, Fujian province and its geological significance. Earth Science, J. China Univ. Geosci., 37, 1217
Zhang, 2015, Geological features and formation processes of the Makeng Fe deposit, China, Resour. Geol., 65, 266, 10.1111/rge.12070
Zhang, 2015, The mineralization age of the Makeng Fe deposit, South China: implications from U-Pb and Sm-Nd geochronology, Int. J. Earth Sci., 104, 663, 10.1007/s00531-014-1096-4
Zhang, 2016, A comparative study of fuzzy weights of evidence and random forests for mapping mineral prospectivity for skarn-type Fe deposits in the southwestern Fujian metallogenic belt, China, Sci. China Earth Sci., 59, 556, 10.1007/s11430-015-5178-3
Zhao, 2019, Assessment of urban flood susceptibility using semi-supervised machine learning model, Sci. Total Environ., 659, 940, 10.1016/j.scitotenv.2018.12.217
Zhou, 2004, 321
Zuo, 2018, Selection of an elemental association related to mineralization using spatial analysis, J. Geochem. Explor., 184, 150, 10.1016/j.gexplo.2017.10.020
Zuo, 2020, Geodata science-based mineral prospectivity mapping: a review, Nat. Resour. Res., 29, 3415, 10.1007/s11053-020-09700-9
Zuo, 2011, Support vector machine: a tool for mapping mineral prospectivity, Comput. Geosci., 37, 1967, 10.1016/j.cageo.2010.09.014
Zuo, 2020, Effects of random negative training samples on mineral prospectivity mapping, Nat. Resour. Res., 29, 3443, 10.1007/s11053-020-09668-6
Zuo, 2020, Geodata science and geochemical mapping, J. Geochem. Explor., 209, 10.1016/j.gexplo.2019.106431
Zuo, 2019, Deep learning and its application in geochemical mapping, Earth-science reviews, 192, 1, 10.1016/j.earscirev.2019.02.023
Zuo, 2015, Evaluation of uncertainty in mineral prospectivity mapping due to missing evidence: a case study with skarn-type Fe deposits in Southwestern Fujian Province, China, Ore Geol. Rev., 71, 502, 10.1016/j.oregeorev.2014.09.024