Three-Dimensional Prospectivity Modeling of Jinshan Ag-Au Deposit, Southern China by Weights-of-Evidence

Abstract

To comprehensively utilize the valuable geological map, exploration profile, borehole, and geochemical logging data and the knowledge on the formation of the Jinshan Ag-Au deposit for forecasting the exploration targets of concealed ore bodies, three-dimensional Mineral Prospectivity Modeling (MPM) of the deposit has been conducted using the weights-of-evidence (WofE) method. Conditional independence between evidence layers was tested, and the outline results using the prediction-volume (P-V) and Student’s t-statistic methods for delineating favorable mineralization areas from continuous posterior probability map were critically compared. Four exploration targets delineated ultimately by the Student’s t-statistic method for the discovery of minable ore bodies in each of the target areas were discussed in detail. The main conclusions include: (1) three-dimensional modeling of a deposit using multi-source reconnaissance data is useful for MPM in interpreting their relationships with known ore bodies; (2) WofE modeling can be used as a straightforward tool for integrating deposit model and reconnaissance data in MPM; (3) the Student’s t-statistic method is more applicable in binarizing the continuous prospectivity map for exploration targeting than the P-V approach; and (4) two target areas within high potential to find undiscovered ore bodies were diagnosed to guide future near-mine exploration activities of the Jinshan deposit.

Keywords

three-dimensional modeling / mineral prospectivity mapping / exploration targeting / weights-of-evidence / C-V fractal model / Jinshan Ag-Au deposit / mineral deposits / economic geology

References

[1] Abedi M, Norouzi G H. Integration of Various Geophysical Data with Geological and Geochemical Data to Determine Additional Drilling for Copper Exploration. Journal of Applied Geophysics, 2012, 83: 35-45.

[2] Afzal P, Ahari H D, Omran N R. et al.. Delineation of Gold Mineralized Zones Using Concentration-Volume Fractal Model in Qolqoleh Gold Deposit, NW Iran. Ore Geology Reviews, 2013, 55: 125-133.

[3] Afzal P, Alghalandis Y F, Khakzad A. et al.. Delineation of Mineralization Zones in Porphyry Cu Deposits by Fractal Concentration-Volume Modeling. Journal of Geochemical Exploration, 2011, 108(3): 220-232.

[4] Agterberg F P. Automatic Contouring of Geological Maps to Detect Target Areas for Mineral Exploration. Journal of the International Association for Mathematical Geology, 1974, 6(4): 373-395.

[5] Agterberg F P. Computer Programs for Mineral Exploration. Science (New York, NY), 1989, 245(4913): 76-81.

[6] Agterberg F P, Bonham-Carter G F. Measuring the Performance of Mineral-Potential Maps. Natural Resources Research, 2005, 14(1): 1-17.

[7] Agterberg F P, Bonham-Carter G F, Cheng Q M. et al.Davis J C, Herzfeld U C. et al.. Weights of Evidence Modeling and Weighted Logistic Regression for Mineral Potential Mapping. Computers in Geology, 25 Years of Progress, 1993OxfordOxford University Press

[8] Agterberg F P, Bonham-Carter G F, Wright D FGaál G, Merriam D F. Statistical Pattern Integration for Mineral Exploration. Computer Applications in Resource Estimation, 1990OxfordPergamon Press

[9] Agterberg F P, Cheng Q M. Conditional Independence Test for Weights-of-Evidence Modeling. Natural Resources Research, 2002, 11(4): 249-255.

[10] Agterberg F. A Modified Weights-of-Evidence Method for Regional Mineral Resource Estimation. Natural Resources Research, 2011, 20(2): 95-101.

[11] Apel M. From 3D Geomodelling Systems towards 3D Geoscience Information Systems: Data Model, Query Functionality, and Data Management. Computers & Geosciences, 2006, 32(2): 222-229.

[12] Bonham-Carter G FGeographic Information Systems for Geoscientists: Modeling with GIS, Computer Methods in the Geosciences, 1994New YorkPergamon

[13] Cai M H, Zhan M G, Peng S B. et al.. Study of Mesozoic Metallogenic Geological Setting and Dynamic Mechanism in Yunkai Area. Mineral Deposits, 2002, 21(3): 264-269(in Chinese with English Abstract)

[14] Carranza E J M. Weights of Evidence Modeling of Mineral Potential: A Case Study Using Small Number of Prospects, Abra, Philippines. Natural Resources Research, 2004, 13(3): 173-187.

[15] Carranza E J M. Editorial: Geocomputation of Mineral Exploration Targets. Computers & Geosciences, 2011, 37(12): 1907-1916.

[16] Carranza E J M, Laborte A G. Random Forest Predictive Modeling of Mineral Prospectivity with Small Number of Prospects and Data with Missing Values in Abra (Philippines). Computers & Geosciences, 2015, 74: 60-70.

[17] Chen Y L, Wu W. A Prospecting Cost-Benefit Strategy for Mineral Potential Mapping Based on ROC Curve Analysis. Ore Geology Reviews, 2016, 74: 26-38.

[18] Cheng Q M. BoostWofE: A New Sequential Weights of Evidence Model Reducing the Effect of Conditional Dependency. Mathematical Geosciences, 2015, 47(5): 591-621.

[19] Cheng Q M, Agterberg F P. Fuzzy Weights of Evidence Method and Its Application in Mineral Potential Mapping. Natural Resources Research, 1999, 8(1): 27-35.

[20] Cheng Q M, Agterberg F P, Ballantyne S B. The Separation of Geochemical Anomalies from Background by Fractal Methods. Journal of Geochemical Exploration, 1994, 51(2): 109-130.

[21] Chung C FRamani R V. An Application of Discriminant Analysis for the Evaluation of Mineral Potential. Application of Computer Methods in the MineralIndustry, Proceedings of the 14th APCOM Symposium, 1977New YorkSociety of Mining Engineers of American Institute of Mining, Metallurgical, and Petroleum Engineers

[22] de Kemp E A. 3-D Visualization of Structural Field Data: Examples from the Archean Caopatina Formation, Abitibi Greenstone Belt, Québec, Canada. Computers & Geosciences, 2000, 26(5): 509-530.

[23] de Kemp E A, Monecke T, Sheshpari M. et al.. 3D GIS as a Support for Mineral Discovery. Geochemistry-Exploration Environment Analysis, 2011, 11(2): 117-128.

[24] de Kemp E A, Sprague K B. Interpretive Tools for 3-D Structural Geological Modeling Part I: Bézier-Based Curves, Ribbons and Grip Frames. Geoinformatica, 2003, 7(1): 55-71.

[25] Deng H, Zheng Y, Chen J. et al.. Deep Learning-Based 3D Prediction Model for the Dayingezhuang Gold Deposit, Shandong Province. Acta Geoscientica Sinica, 2020, 41(2): 157-165(in Chinese with English Abstract)

[26] Deng M F. A Conditional Dependence Adjusted Weights of Evidence Model. Natural Resources Research, 2009, 18(4): 249-258.

[27] DeWolfe Y M, Gibson H L, Richardson D. 3D Reconstruction of Volcanic and Ore-Forming Environments of a Giant VMS System: A Case Study from the Kidd Creek Mine, Canada. Ore Geology Reviews, 2018, 101: 532-555.

[28] Ding R X, Yu P P, Hu G M. et al.. Thermochornology of Pangxidong Fault Zone in Southern Section of Qin-Hang Metallogenic Belt. Earth Science, 2018, 43(6): 1830-1838(in Chinese with English Abstract)

[29] Ding R X, Zou H P, Lao M J. et al.. Indosinian Activity Records of Ductile Shear Zones in Southern Segment of Qin-Hang Combined Belt: A Case Study of Fangcheng-Lingshan Fault Zone. Earth Science Frontiers, 2015, 22(2): 79-85(in Chinese with English Abstract)

[30] Ford A, Miller J M, Mol A G. A Comparative Analysis of Weights of Evidence, Evidential Belief Functions, and Fuzzy Logic for Mineral Potential Mapping Using Incomplete Data at the Scale of Investigation. Natural Resources Research, 2016, 25(1): 19-33.

[31] Fu G M, Lü Q T, Yan J Y. et al.. 3D Mineral Prospectivity Modeling Based on Machine Learning: A Case Study of the Zhuxi Tungsten Deposit in Northeastern Jiangxi Province, South China. Ore Geology Reviews, 2021, 131: 104010.

[32] Geng W H, Li B P. Metallogenic Regularity and Exploring Indicator for Altered Rock Type Au-Ag Deposits in Southeastern Guangxi, China. Mineral Resources and Geology, 1993, 7(3): 183-187(in Chinese with English Abstract)

[33] Gholampour O, Hezarkhani A, Maghsoudi A. et al.. Application of Sequential Gaussian Simulation and Concentration-Volume Fractal Model to Delineate Alterations in Hypogene Zone of Miduk Porphyry Copper Deposit, SE Iran. Journal of African Earth Sciences, 2019, 150: 389-400.

[34] Harris D, Zurcher L, Stanley M. et al.. A Comparative Analysis of Favorability Mappings by Weights of Evidence, Probabilistic Neural Networks, Discriminant Analysis, and Logistic Regression. Natural Resources Research, 2003, 12(4): 241-255.

[35] Huang X, Zheng Y, Yu P P. et al.. Mass Transfer during Alteration and Ore-Forming Geological Process of the Pangxidong-Jinshan Ag-Au Ore-Field in the Yunkai Area. Geochimica, 2021, 50(4): 365-380(in Chinese with English Abstract)

[36] Jiao Q Q, Wang L X, Deng T. et al.. Origin of the Ore-Forming Fluids and Metals of the Hetai Goldfield in Guangdong Province of South China: Constraints from C-H-O-S-Pb-He-Ar Isotopes. Ore Geology Reviews, 2017, 88: 674-689.

[37] Karaman M, Kumral M, Yildirim D K. et al.. Delineation of the Porphyry-Skarn Mineralized Zones (NW Turkey) Using Concentration-Volume Fractal Model. Geochemistry, 2021, 81(4): 125802.

[38] Kianoush P, Mohammadi G, Hosseini S A. et al.. Compressional and Shear Interval Velocity Modeling to Determine Formation Pressures in an Oilfield of SW Iran. Journal of Mining and Environment, 2022, 13(3): 851-871

[39] Kreuzer O P, Yousefi M, Nykänen V. Introduction to the Special Issue on Spatial Modelling and Analysis of Ore-Forming Processes in Mineral Exploration Targeting. Ore Geology Reviews, 2020, 119: 103391.

[40] Lawley C J M, Tschirhart V, Smith J W. et al.. Prospectivity Modelling of Canadian Magmatic Ni (±Cu±Co±PGE) Sulphide Mineral Systems. Ore Geology Reviews, 2021, 132: 103985.

[41] Lee C, Oh H J, Cho S J. et al.. Three-Dimensional Prospectivity Mapping of Skarn-Type Mineralization in the Southern Taebaek Area, Korea. Geosciences Journal, 2019, 23(2): 327-339.

[42] Li H, Li X H, Yuan F. et al.. Knowledge-Driven Based Three-Dimensional Prospectivity Modeling of Fe-Cu Skarn Deposits: A Case Study of the Fanchang Volcanic Basin, Anhui Province, Eastern China. Ore Geology Reviews, 2022, 149: 105065.

[43] Li N, Song X L, Li C B. et al.. 3D Geological Modeling for Mineral System Approach to GIS-Based Prospectivity Analysis: Case Study of an MVT Pb-Zn Deposit. Natural Resources Research, 2019, 28(3): 995-1019.

[44] Li R X, Wang G W, Carranza E J M. GeoCube: A 3D Mineral Resources Quantitative Prediction and Assessment System. Computers & Geosciences, 2016, 89: 161-173.

[45] Li S, Chen J P, Xiang J. Applications of Deep Convolutional Neural Networks in Prospecting Prediction Based on Two-Dimensional Geological Big Data. Neural Computing and Applications, 2020, 32(7): 2037-2053.

[46] Li X H, Yuan F, Zhang M M. et al.. Three-Dimensional Mineral Prospectivity Modeling for Targeting of Concealed Mineralization within the Zhonggu Iron Orefield, Ningwu Basin, China. Ore Geology Reviews, 2015, 71: 633-654.

[47] Li X H, Yuan F, Zhang M M. et al.. 3D Computational Simulation-Based Mineral Prospectivity Modeling for Exploration for Concealed Fe-Cu Skarn-Type Mineralization within the Yueshan Orefield, Anqing District, Anhui Province, China. Ore Geology Reviews, 2019, 105: 1-17.

[48] Li Z X, Li X H, Zhou H W. et al.. Grenvillian Continental Collision in South China: New SHRIMP U-Pb Zircon Results and Implications for the Configuration of Rodinia. Geology, 2002, 30(2): 163-166. in Chinese with English Abstract)

[49] Lin Z W, Zhou Y Z, Qin Y. et al.. Ore-Controlling Structure Analysis of Pangxidong-Jinshan Silver-Gold Orefield, Southern Qin-Hang Belt: Implications for Further Exploration. Mineral Deposits, 2017, 36(4): 866-878(in Chinese with English Abstract)

[50] Lindsay M D, Jessell M W, Ailleres L. et al.. Geodiversity: Exploration of 3D Geological Model Space. Tectonophysics, 2013, 594: 27-37.

[51] Liu T F. Geological Characteristics and Concentration Regularity of Jinshan Au-Ag Deposit, Guangxi Autonomous Region, Guangxi. Gold, 1990, 11(8): 1-7(in Chinese with English Abstract)

[52] Liu Y, Zhou K F, Zhang N N. et al.. Maximum Entropy Modeling for Orogenic Gold Prospectivity Mapping in the Tangbale-Hatu Belt, Western Junggar, China. Ore Geology Reviews, 2018, 100: 133-147.

[53] Lü W C, Liu X Y, Chen Q. et al.. Geochemical Characteristics of REE of Pangxidong Electrum Deposit of Guangdong Province. Metal Mine, 2014, 3: 108-110(in Chinese with English Abstract)

[54] Mahdizadeh M, Afzal P, Eftekhari M. et al.. Geomechanical Zonation Using Multivariate Fractal Modeling in Chadormalu Iron Mine, Central Iran. Bulletin of Engineering Geology and the Environment, 2022, 81(1): 59.

[55] Mallet J L. Discrete Smooth Interpolation in Geometric Modelling. Computer-Aided Design, 1992, 24(4): 178-191.

[56] Mao J W, Cheng Y B, Chen M H. et al.. Major Types and Time-Space Distribution of Mesozoic Ore Deposits in South China and Their Geodynamic Settings. Mineralium Deposita, 2013, 48(3): 267-294.

[57] Mao X C, Ren J, Liu Z K. et al.. Three-Dimensional Prospectivity Modeling of the Jiaojia-Type Gold Deposit, Jiaodong Peninsula, Eastern China: A Case Study of the Dayingezhuang Deposit. Journal of Geochemical Exploration, 2019, 203: 27-44.

[58] McMillan M, Haber E, Peters B. et al.. Mineral Prospectivity Mapping Using a VNet Convolutional Neural Network. The Leading Edge, 2021, 40(2): 99-105.

[59] Mirzaie M, Afzal P, Adib A. et al.. Detection of Zones Based on Ore and Gangue Using Fractal and Multivariate Analysis in Chah Gaz Iron Ore Deposit, Central Iran. Journal of Mining and Environment, 2020, 11(2): 453-466

[60] Mohammadpour M, Bahroudi A, Abedi M. Three Dimensional Mineral Prospectivity Modeling by Evidential Belief Functions, a Case Study from Kahang Porphyry Cu Deposit. Journal of African Earth Sciences, 2021, 174: 104098.

[61] Nielsen S, Partington G, Franey D. et al.. 3D Mineral Potential Modelling of Gold Distribution at the Tampia Gold Deposit. Ore Geology Reviews, 2019, 109: 276-289.

[62] Nykänen V, Karinen T, Niiranen T. et al.. Modelling the Gold Potential of Central Lapland, Northern Finland. Geological Survey of Finland, Special Paper, 2011, 49: 71-82

[63] Nykänen V, Lahti I, Niiranen T. et al.. 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 Geology Reviews, 2015, 71: 853-860.

[64] Olierook H K, Scalzo R, Kohn D. et al.. Bayesian Geological and Geophysical Data Fusion for the Construction and Uncertainty Quantification of 3D Geological Models. Geoscience Frontiers, 2021, 12: 479-493.

[65] Paganelli F, Richards J P, Grunsky E C. Integration of Structural, Gravity, and Magnetic Data Using the Weights of Evidence Method as a Tool for Kimberlite Exploration in the Buffalo Head Hills, Northern Central Alberta, Canada. Natural Resources Research, 2002, 11(3): 219-236.

[66] Pan G C, Harris D P. Estimating a Favorability Equation for the Integration of Geodata and Selection of Mineral Exploration Targets. Mathematical Geology, 1992, 24(2): 177-202.

[67] Pan J Y, Zhang Q, Zhang B G. et al.. Metallogenic Regularity of Gold and Silver Deposits in Western Guangdong. Mineral Deposits, 1996, 15(3): 66-75(in Chinese with English Abstract)

[68] Parsa M, Carranza E J M, Ahmadi B. Deep GMDH Neural Networks for Predictive Mapping of Mineral Prospectivity in Terrains Hosting Few but Large Mineral Deposits. Natural Resources Research, 2022, 31(1): 37-50.

[69] Payne C E, Cunningham F, Peters K J. et al.. From 2D to 3D: Prospectivity Modelling in the Taupo Volcanic Zone, New Zealand. Ore Geology Reviews, 2015, 71: 558-577.

[70] Perrouty S, Lindsay M D, Jessell M W. et al.. 3D Modeling of the Ashanti Belt, Southwest Ghana: Evidence for a Litho-Stratigraphic Control on Gold Occurrences within the Birimian Sefwi Group. Ore Geology Reviews, 2014, 63: 252-264.

[71] Pirajno F, Bagas L. Gold and Silver Metallogeny of the South China Fold Belt: A Consequence of Multiple Mineralizing Events?. Ore Geology Reviews, 2002, 20(3/4): 109-126.

[72] Porwal A K, Kreuzer O P. Introduction to the Special Issue: Mineral Prospectivity Analysis and Quantitative Resource Estimation. Ore Geology Reviews, 2010, 38(3): 121-127.

[73] Porwal A, Carranza E J M, Hale M. Knowledge-Driven and Data-Driven Fuzzy Models for Predictive Mineral Potential Mapping. Natural Resources Research, 2003, 12(1): 1-25.

[74] Porwal A, Carranza E J M, Hale M. A Hybrid Fuzzy Weights-of-Evidence Model for Mineral Potential Mapping. Natural Resources Research, 2006, 15(1): 1-14.

[75] Qian J P, Xie B W, Chen H Y. et al.. Analysis of Ore-Controlling Structure and Prospecting of Tectono-Geochemistry in Jinshan Au-Ag Mining Area, Guangxi. Geoscience, 2011, 25(3): 531-544(in Chinese with English Abstract)

[76] Raines G L. Evaluation of Weights of Evidence to Predict Epithermal-Gold Deposits in the Great Basin of the Western United States. Natural Resources Research, 1999, 8(4): 257-276.

[77] Schaeben H. A Mathematical View of Weights-of-Evidence, Conditional Independence, and Logistic Regression in Terms of Markov Random Fields. Mathematical Geosciences, 2014, 46(6): 691-709.

[78] Singer D A, Kouda R. Application of a Feedforward Neural Network in the Search for Kuroko Deposits in the Hokuroku District, Japan. Mathematical Geology, 1996, 28(8): 1017-1023.

[79] Souza Filho C R, Sawatzky D L, Raines G L. et al.Spatial Data Modeler 5 (ArcSDM 5): ArcGIS Geoprocessing Tools for Spatial Data Modelling Using Weights of Evidence, Logistic Regression, Fuzzy Logic and Neural Networks, 2017

[80] Sprague K, de Kemp E, Wong W. et al.. Spatial Targeting Using Queries in a 3-D GIS Environment with Application to Mineral Exploration. Computers & Geosciences, 2006, 32(3): 396-418.

[81] Sun H S, Cao X Z, Zhang K. Characteristics of Ore-Controlling Faults and Rules of Ore-Controlling Faults in Pangxidong Ag(Au) Deposit, Northwestern Guangdong. Conributions to Geology and Mineral Resources Research, 2005, 20(3): 161-165(in Chinese with English Abstract)

[82] Sun T, Wu K X, Chen L K. et al.. Joint Application of Fractal Analysis and Weights-of-Evidence Method for Revealing the Geological Controls on Regional-Scale Tungsten Mineralization in Southern Jiangxi Province, China. Minerals, 2017, 7: 243.

[83] Taylor B E, de Kemp E, Grunsky E. et al.. Three-Dimensional Visualization of the Archean Horne and Quemont Au-Bearing Volcanogenic Massive Sulfide Hydrothermal Systems, Blake River Group, Quebec. Economic Geology, 2014, 109(1): 183-203.

[84] The Sixth Geological Team of Guangxi (TSGTG)Topographic and Geological Map of Jinshan Ag-Au Mining Area in Bobai County, Guangxi, 1984

[85] Thiart C, Bonham-Carter G F, Agterberg F P. Conditional Independence in Weights-of-Rvidence: Application of an Improved Test. Proceedings of the 2003 Annual IAMG conference, 2003CD-ROM

[86] Thiart C, Bonham-Carter G F, Agterberg F P. et al.Harris J R. et al.. An Application of the New Omnibus Test for Conditional Independence in Weights of Evidence Modelling. GIS Applications in the Earth Sciences, 2005TorontoGeological Association of Canada Special Publication

[87] Wang G W, Carranza E J M, Zuo R G. et al.. Mapping of District-Scale Potential Targets Using Fractal Models. Journal of Geochemical Exploration, 2012, 122: 34-46.

[88] Wang G W, Li R X, Carranza E J M. et al.. 3D Geological Modeling for Prediction of Subsurface Mo Targets in the Luanchuan District, China. Ore Geology Reviews, 2015, 71: 592-610.

[89] Wang H N, Yang J W, Chen H Q. Geochemical Studies of the Pangxidong Silver Deposit in Guangdong Province. Mineral Deposits, 1992, 11(2): 179-187(in Chinese with English Abstract)

[90] Wang W L, Zhao J, Cheng Q M. et al.. Tectonic-Geochemical Exploration Modeling for Characterizing Geo-Anomalies in Southeastern Yunnan District, China. Journal of Geochemical Exploration, 2012, 122: 71-80.

[91] Wang Y, Chen J P, Jia D H. Three-Dimensional Mineral Potential Mapping for Reducing Multiplicity and Uncertainty: Kaerqueka Polymetallic Deposit, QingHai Province, China. Natural Resources Research, 2020, 29(1): 365-393.

[92] Wang Z W, Zhou Y ZGeochemistry Character of Pangxidong-Jinshan Silver-Gold Deposit and Its Mineral Resource Evaluation Yunkai Area, South China, Beijing, 2002(in Chinese with English Abstract)

[93] Wang Z W, Zhou Y Z. Geological Characteristics and Genesis of the Pangxidong-Jinshan Ag-Au Deopsit in Yunkai Terrain, South China. Geotectonic et Metallogenia, 2002, 26(2): 193-198(in Chinese with English Abstract)

[94] Wang Z Y, Wang J C, Yin Y Q. et al.. Metallogenic Law and Model of Au-Ag Deposits in Southeastern Guangxi. Mineral Resources and Geolgy, 1995, 9(4): 257-262(in Chinese with English Abstract)

[95] Xia J L, Huang G C, Ding L X. et al.. Zircon U-Pb Dating, Petrogenesis and Tectonic Background of the Early Paleozoic Nintan Gneisis Granitic Pluton, in the Yunkai Terran. Earth Science, 2018, 43(7): 2276-2293(in Chinese with English Abstract)

[96] Xiang J, Xiao K Y, Carranza E J M. et al.. 3D Mineral Prospectivity Mapping with Random Forests: A Case Study of Tongling, Anhui, China. Natural Resources Research, 2020, 29(1): 395-414.

[97] Xiao F, Chen J G, Agterberg F. et al.. Element Behavior Analysis and Its Implications for Geochemical Anomaly Identification: A Case Study for Porphyry Cu-Mo Deposits in Eastern Tianshan, China. Journal of Geochemical Exploration, 2014, 145: 1-11.

[98] Xiao F, Chen J G, Hou W S. et al.. Identification and Extraction of Ag-Au Mineralization Associated Geochemical Anomaly in Pangxitong District, Southern Part of the Qinzhou-Hangzhou Metallogenic Belt, China. Acta Petrologica Sinica, 2017, 33(3): 779-790(in Chinese with English Abstract)

[99] Xiao F, Chen J G, Hou W S. et al.. A Spatially Weighted Singularity Mapping Method Applied to Identify Epithermal Ag and Pb-Zn Polymetallic Mineralization Associated Geochemical Anomaly in Northwest Zhejiang, China. Journal of Geochemical Exploration, 2018, 189: 122-137.

[100] Xiao F, Chen J G, Zhang Z Y. et al.. Singularity Mapping and Spatially Weighted Principal Component Analysis to Identify Geochemical Anomalies Associated with Ag and Pb-Zn Polymetallic Mineralization in Northwest Zhejiang, China. Journal of Geochemical Exploration, 2012, 122: 90-100.

[101] Xiao F, Chen W L, Wang J. et al.. A Hybrid Logistic Regression: Gene Expression Programming Model and Its Application to Mineral Prospectivity Mapping. Natural Resources Research, 2022, 31(4): 2041-2064.

[102] Xiao F, Wang K Q, Hou W S. et al.. Identifying Geochemical Anomaly through Spatially Anisotropic Singularity Mapping: A Case Study from Silver-Gold Deposit in Pangxidong District, SE China. Journal of Geochemical Exploration, 2020, 210: 106453.

[103] Xiao F, Wang K Q, Hou W S. et al.. Prospectivity Mapping for Porphyry Cu-Mo Mineralization in the Eastern Tianshan, Xinjiang, Northwestern China. Natural Resources Research, 2020, 29(1): 89-113.

[104] Xiao F, Wang Y, Zhou Y Z. Determining Thresholds of Arsenic and Mercury in Stream Sediment for Mapping Natural Toxic Element Anomaly Using Data-Driven Models: A Comparative Study on Probability Plots and Fractal Methods. Arabian Journal of Geosciences, 2020, 13(18): 915.

[105] Xiao K Y, Li N, Porwal A. et al.. GIS-Based 3D Prospectivity Mapping: A Case Study of Jiama Copper-Polymetallic Deposit in Tibet, China. Ore Geology Reviews, 2015, 71: 611-632.

[106] Xu D M, Lin Z Y, Long W G. et al.. Research History and Current Situation of Qinzhou-Hangzhou Metallogenic Belt, South China. Geology and Mineral Resources of South China, 2012, 28(4): 277-289(in Chinese with English Abstract)

[107] Yang M G, Mei Y W. Charateristics of Geology and Metallization in the Qinzhou-Hangzhou Paleoplate Juncture. Geology and Mineral Resources of South China, 1997, 3: 52-59(in Chinese with English Abstract)

[108] Yin B J, Zuo R G, Sun S Q. Mineral Prospectivity Mapping Using Deep Self-Attention Model. Natural Resources Research, 2023, 32(1): 37-56.

[109] Yousefi M, Carranza E J M. Fuzzification of Continuous-Value Spatial Evidence for Mineral Prospectivity Mapping. Computers & Geosciences, 2015, 74: 97-109.

[110] Yousefi M, Carranza E J M. Prediction-Area (P-A) Plot and C-A Fractal Analysis to Classify and Evaluate Evidential Maps for Mineral Prospectivity Modeling. Computers & Geosciences, 2015, 79: 69-81.

[111] Yu X T, Xiao F, Zhou Y Z. et al.. Application of Hierarchical Clustering, Singularity Mapping, and Kohonen Neural Network to Identify Ag-Au-Pb-Zn Polymetallic Mineralization Associated Geochemical Anomaly in Pangxidong District. Journal of Geochemical Exploration, 2019, 203: 87-95.

[112] Yu Z B, Liu B L, Xie M A. et al.. 3D Mineral Prospectivity Mapping of Zaozigou Gold Deposit, West Qinling, China: Deep Learning-Based Mineral Prediction. Minerals, 2022, 12(11): 1382.

[113] Yuan F, Li X H, Zhang M M. et al.. Three-Dimensional Weights of Evidence-Based Prospectivity Modeling: A Case Study of the Baixiangshan Mining Area, Ningwu Basin, Middle and Lower Yangtze Metallogenic Belt, China. Journal of Geochemical Exploration, 2014, 145: 82-97.

[114] Zeng C Y, Ding R X, Li H Z. et al.. Analysis of X-Ray Fluorescence Spectroscopy and Plasma Mass Spectrometry of Pangxidong Composite Granitoid Pluton and Its Implications for Magmatic Differentiation. Spectroscopy and Spectral Analysis, 2015, 35(11): 3187-3191(in Chinese with English Abstract)

[115] Zhang D J, Agterberg F, Cheng Q M. et al.. A Comparison of Modified Fuzzy Weights of Evidence, Fuzzy Weights of Evidence, and Logistic Regression for Mapping Mineral Prospectivity. Mathematical Geosciences, 2014, 46(7): 869-885.

[116] Zhang K J, Cai J X. NE-SW-Trending Hepu-Hetai Dextral Shear Zone in Southern China: Penetration of the Yunkai Promontory of South China into Indochina. Journal of Structural Geology, 2009, 31(7): 737-748.

[117] Zhang Q P, Chen J P, Xu H. et al.. Three-Dimensional Mineral Prospectivity Mapping by XGBoost Modeling: A Case Study of the Lannigou Gold Deposit, China. Natural Resources Research, 2022, 31(3): 1135-1156.

[118] Zhang S, Carranza E J M, Wei H T. et al.. Data-Driven Mineral Prospectivity Mapping by Joint Application of Unsupervised Convolutional Auto-Encoder Network and Supervised Convolutional Neural Network. Natural Resources Research, 2021, 30(2): 1011-1031.

[119] Zhang Z J, Zuo R G, Xiong Y H. 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. Science China Earth Sciences, 2016, 59(3): 556-572.

[120] Zheng W, Mao J W, Pirajno F. et al.. Geochronology and Geochemistry of the Shilu Cu-Mo Deposit in the Yunkai Area, Guangdong Province, South China and Its Implication. Ore Geology Reviews, 2015, 67: 382-398.

[121] Zheng Y, Zhou Y Z, Wang Y J. et al.. A Fluid Inclusion Study of the Hetai Goldfield in the Qinzhou Bay-Hangzhou Bay Metallogenic Belt, South China. Ore Geology Reviews, 2016, 73: 346-353.

[122] Zhou M F, Yan D P, Kennedy A K. et al.. SHRIMP U-Pb Zircon Geochronological and Geochemical Evidence for Neoproterozoic Arc-Magmatism along the Western Margin of the Yangtze Block, South China. Earth and Planetary Science Letters, 2002, 196(1/2): 51-67.

[123] Zhou Y Z, Li X Y, Zheng Y. et al.. Geological Settings and Metallogenesis of Qinzhou Bay-Hangzhou Bay Orogenic Juncture Belt, South China. Acta Petrologica Sinica, 2017, 33(3): 667-681(in Chinese with English Abstract)

[124] Zhou Y Z, Zheng Y, Zeng C Y. et al.. On the Understanding of Qinzhou Bay-Hangzhou Bay Metallogenic Belt, South China. Earth Science Frontiers, 2015, 22(2): 1-6(in Chinese with English Abstract)

[125] Zuo R G, Carranza E J M. Support Vector Machine: A Tool for Mapping Mineral Prospectivity. Computers & Geosciences, 2011, 37(12): 1967-1975.

[126] Zuo R G, Cheng Q M, Agterberg F P. et al.. Application of Singularity Mapping Technique to Identify Local Anomalies Using Stream Sediment Geochemical Data, a Case Study from Gangdese, Tibet, Western China. Journal of Geochemical Exploration, 2009, 101(3): 225-235.

[127] Zuo R G, Luo Z J, Xiong Y H. et al.. A Geologically Constrained Variational Autoencoder for Mineral Prospectivity Mapping. Natural Resources Research, 2022, 31(3): 1121-1133.

[128] Zuo R G, Xu Y. Graph Deep Learning Model for Mapping Mineral Prospectivity. Mathematical Geosciences, 2023, 55(1): 1-21.

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