Geodata Science-Based Mineral Prospectivity Mapping: A Review

This paper introduces the concept of geodata science-based mineral prospectivity mapping (GSMPM), which is based on analyzing the spatial associations between geological prospecting big data (GPBD) and locations of known mineralization. Geodata science reveals the inter-correlations between GPBD and mineralization, converts GPBD into mappable criteria, and combines multiple mappable criteria into a mineral potential map. A workflow of the GSMPM is proposed and compared with the traditional workflow of mineral prospectivity mapping. More specifically, each component in such a workflow is explained in detail to demonstrate how geodata science serves mineral prospectivity mapping by deriving geoinformation from geoscience data, generating geo-knowledge from geoinformation, and allowing spatial decision-making by integrating geoinformation and geo-knowledge on the formation of mineral deposits. This review also presents several research directions for GSMPM in the future.

[1]  Peter Naur,et al.  Concise survey of computer methods , 1974 .

[2]  F. Agterberg,et al.  Regression models for estimating mineral resources from geological map data , 1980 .

[3]  D. Watson A refinement of inverse distance weighted interpolation , 1985 .

[4]  F. Agterberg Computer programs for mineral exploration. , 1989, Science.

[5]  F. Agterberg,et al.  Weights of evidence modelling: a new approach to mapping mineral potential , 1990 .

[6]  C. Mann Uncertainty in geology , 1993 .

[7]  G. Bonham-Carter Geographic Information Systems for Geoscientists: Modelling with GIS , 1995 .

[8]  D. Singer,et al.  Application of a feedforward neural network in the search for Kuroko deposits in the Hokuroku district, Japan , 1996 .

[9]  D. Singer,et al.  Classification of mineral deposits into types using mineralogy with a probabilistic neural network , 1997 .

[10]  C. Knox-Robinson,et al.  Towards a holistic exploration strategy: Using Geographic Information Systems as a tool to enhance exploration , 1997 .

[11]  D. Singer,et al.  A Comparison of the Weights-of-Evidence Method and Probabilistic Neural Networks , 1999 .

[12]  Q. Cheng,et al.  Fuzzy Weights of Evidence Method and Its Application in Mineral Potential Mapping , 1999 .

[13]  E. Carranza,et al.  Evidential belief functions for data-driven geologically constrained mapping of gold potential, Baguio district, Philippines , 2003 .

[14]  Nitesh V. Chawla,et al.  SMOTEBoost: Improving Prediction of the Minority Class in Boosting , 2003, PKDD.

[15]  Emmanuel John M. Carranza,et al.  Artificial Neural Networks for Mineral-Potential Mapping: A Case Study from Aravalli Province, Western India , 2003 .

[16]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[17]  J. Fodor,et al.  Evaluation of Uncertainties and Risks in Geology , 2004 .

[18]  Nitesh V. Chawla,et al.  Editorial: special issue on learning from imbalanced data sets , 2004, SKDD.

[19]  A. Rencz APPLICATION OF FUZZY SET THEORY TO INTEGRATED MINERAL EXPLORATION , 2004 .

[20]  E. Carranza,et al.  Application of Data-Driven Evidential Belief Functions to Prospectivity Mapping for Aquamarine-Bearing Pegmatites, Lundazi District, Zambia , 2005 .

[21]  Zhi-Hua Zhou,et al.  Ieee Transactions on Knowledge and Data Engineering 1 Training Cost-sensitive Neural Networks with Methods Addressing the Class Imbalance Problem , 2022 .

[22]  E. Carranza,et al.  A Hybrid Fuzzy Weights-of-Evidence Model for Mineral Potential Mapping , 2006 .

[23]  J. Harris,et al.  Gold prospectivity maps of the Red Lake greenstone belt: application of GIS technology , 2006 .

[24]  Dan Zhu,et al.  Late Permian Emeishan Flood Basalts in Southwestern China , 2007 .

[25]  Q. Cheng Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China , 2007 .

[26]  Jie-shou Zhu The Structural Characteristics of Lithosphere in the Continent of Eurasia and Its Marginal Seas , 2007 .

[27]  Guochun Zhao When did plate tectonics begin on the North China Craton? Insights from metamorphism , 2007 .

[28]  O. Kreuzer,et al.  Linking Mineral Deposit Models to Quantitative Risk Analysis and Decision-Making in Exploration , 2008 .

[29]  E. Carranza,et al.  Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping , 2008 .

[30]  T. McCuaig,et al.  Translating the mineral systems approach into an effective exploration targeting system , 2010 .

[31]  Taghi M. Khoshgoftaar,et al.  RUSBoost: A Hybrid Approach to Alleviating Class Imbalance , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[32]  W. D. Menzie,et al.  Quantitative Mineral Resource Assessments: An Integrated Approach , 2010 .

[33]  Renguang Zuo,et al.  Support vector machine: A tool for mapping mineral prospectivity , 2011, Comput. Geosci..

[34]  Claudio Carpineto,et al.  A Survey of Automatic Query Expansion in Information Retrieval , 2012, CSUR.

[35]  E. Carranza Geochemical Anomaly and Mineral Prospectivity Mapping in Gis , 2012 .

[36]  Mohammed Bennamoun,et al.  Ontology learning from text: A look back and into the future , 2012, CSUR.

[37]  Viktor Mayer-Schnberger,et al.  Big Data: A Revolution That Will Transform How We Live, Work, and Think , 2013 .

[38]  S. Bates,et al.  Safety, Pharmacokinetic, and Functional Effects of the Nogo-A Monoclonal Antibody in Amyotrophic Lateral Sclerosis: A Randomized, First-In-Human Clinical Trial , 2014, PloS one.

[39]  Victor F. Rodriguez-Galiano,et al.  Predictive modelling of gold potential with the integration of multisource information based on random forest: a case study on the Rodalquilar area, Southern Spain , 2014, Int. J. Geogr. Inf. Sci..

[40]  C. Ré,et al.  A Machine Reading System for Assembling Synthetic Paleontological Databases , 2014, PloS one.

[41]  K. Kelley,et al.  Building Exploration Capability for the 21st Century , 2014 .

[42]  Yongliang Chen Mineral potential mapping with a restricted Boltzmann machine , 2015 .

[43]  R. Zuo,et al.  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 , 2016, Science China Earth Sciences.

[44]  J. Harris,et al.  Data- and knowledge-driven mineral prospectivity maps for Canada's North , 2015 .

[45]  V. Nykänen,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 , 2015 .

[46]  E. Carranza,et al.  Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of Random Forests algorithm , 2015 .

[47]  E. Carranza,et al.  Introduction to the Special Issue: GIS-based mineral potential modelling and geological data analyses for mineral exploration , 2015 .

[48]  V. Rodriguez-Galiano,et al.  Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines , 2015 .

[49]  E. Carranza,et al.  Evaluation of uncertainty in mineral prospectivity mapping due to missing evidence: A case study with skarn-type Fe deposits in Southwestern Fujian Province, China , 2015 .

[50]  J. Harris,et al.  Comparison of the Data-Driven Random Forests Model and a Knowledge-Driven Method for Mineral Prospectivity Mapping: A Case Study for Gold Deposits Around the Huritz Group and Nueltin Suite, Nunavut, Canada , 2016, Natural Resources Research.

[51]  Eric Gossett,et al.  Big Data: A Revolution That Will Transform How We Live, Work, and Think , 2015 .

[52]  Emmanuel John M. Carranza,et al.  Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines) , 2015, Comput. Geosci..

[53]  E. Carranza,et al.  Data-Driven Predictive Modeling of Mineral Prospectivity Using Random Forests: A Case Study in Catanduanes Island (Philippines) , 2016, Natural Resources Research.

[54]  Yu Zhang,et al.  A review on text mining , 2015, 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS).

[55]  R. Zuo,et al.  Mapping mineral prospectivity for Cu polymetallic mineralization in southwest Fujian Province, China , 2016 .

[56]  Huadong Guo,et al.  Big Earth Data from space: a new engine for Earth science , 2016 .

[57]  R. Zuo,et al.  Big Data Analytics of Identifying Geochemical Anomalies Supported by Machine Learning Methods , 2017, Natural Resources Research.

[58]  T. McCuaig,et al.  The mineral systems concept: The key to exploration targeting , 2017 .

[59]  R. Zuo Machine Learning of Mineralization-Related Geochemical Anomalies: A Review of Potential Methods , 2017, Natural Resources Research.

[60]  Emmanuel John M. Carranza,et al.  Natural Resources Research Publications on Geochemical Anomaly and Mineral Potential Mapping, and Introduction to the Special Issue of Papers in These Fields , 2017, Natural Resources Research.

[61]  Yongliang Chen,et al.  Mapping mineral prospectivity using an extreme learning machine regression , 2017 .

[62]  Li Shi,et al.  Prospecting Information Extraction by Text Mining Based on Convolutional Neural Networks–A Case Study of the Lala Copper Deposit, China , 2018, IEEE Access.

[63]  E. Carranza,et al.  Mapping mineral prospectivity through big data analytics and a deep learning algorithm , 2018, Ore Geology Reviews.

[64]  Jianguo Chen,et al.  Information extraction and knowledge graph construction from geoscience literature , 2018, Comput. Geosci..

[65]  E. Holden,et al.  GeoDocA – Fast analysis of geological content in mineral exploration reports: A text mining approach , 2019, Ore Geology Reviews.

[66]  Shi Li,et al.  Applications of deep convolutional neural networks in prospecting prediction based on two-dimensional geological big data , 2019, Neural Computing and Applications.

[67]  Jian Wang,et al.  Mapping Mineral Prospectivity via Semi-supervised Random Forest , 2019, Natural Resources Research.

[68]  Jian Wang,et al.  Deep learning and its application in geochemical mapping , 2019, Earth-Science Reviews.

[69]  A. Ford,et al.  Translating expressions of intrusion-related mineral systems into mappable spatial proxies for mineral potential mapping: Case studies from the Southern New England Orogen, Australia , 2019, Ore Geology Reviews.

[70]  Joachim Denzler,et al.  Deep learning and process understanding for data-driven Earth system science , 2019, Nature.

[71]  M. Yousefi,et al.  Exploration information systems – A proposal for the future use of GIS in mineral exploration targeting , 2019, Ore Geology Reviews.

[72]  M. Yousefi,et al.  Introduction to the special issue on spatial modelling and analysis of ore-forming processes in mineral exploration targeting , 2020 .

[73]  R. Zuo,et al.  Geodata science and geochemical mapping , 2020 .

[74]  D. Groves,et al.  Towards producing mineral resource-potential maps within a mineral systems framework, with emphasis on Australian orogenic gold systems , 2020 .

[75]  Fei Chen,et al.  Data-Driven Predictive Modelling of Mineral Prospectivity Using Machine Learning and Deep Learning Methods: A Case Study from Southern Jiangxi Province, China , 2020, Minerals.

[76]  D. Groves,et al.  A scale-integrated exploration model for orogenic gold deposits based on a mineral system approach , 2020, Geoscience Frontiers.

[77]  R. Zuo,et al.  Effects of Random Negative Training Samples on Mineral Prospectivity Mapping , 2020, Natural Resources Research.

[78]  J. Caers,et al.  A Monte Carlo-based framework for risk-return analysis in mineral prospectivity mapping , 2020 .