A new paradigm for mineral resource prediction based on human intelligence-artificial intelligence Integration

doi:10.13745/j.esf.sf.2025.7.20

Abstract

Abstract:

Mineral resources serve as a critical material basis supporting socio-economic development, with their formation and distribution governed by the complex interactions between deep Earth processes and surface environmental conditions. With the ever-growing global demand for mineral resources, traditional mineral resource prediction methods face significant challenges in their application to covered regions, deeply buried deposits, and non-traditional exploration regions. In recent years, the rapid development of big data and artificial intelligence (AI) technologies has provided significant opportunities for mineral resource research, offering transformative tools for mineral prediction and assessment. This paper systematically reviews the theoretical evolution of mineral prediction and explores a new AI- and big data-powered paradigm, which includes an expanded concept of “ore deposits”, multi-system integrated modeling involving the Earth system, metallogenic system, exploration system, and prediction-evaluation system, intelligent integration of geological survey data and long-tail scientific data, and the deep integration of human intelligence (HI) and artificial intelligence (AI). Based on several representative case studies from recent research projects completed by the author’s team, including integrated mineral prediction in covered regions, quantitative prediction of deep mineral resources, and the construction of a global porphyry copper deposit knowledge graph, this paper demonstrates the innovative application of nonlinear theory and AI techniques in addressing key scientific issues related to mineral prediction. Building on this, the paper anticipates that future data-driven and intelligence-integrated research paradigms will fundamentally transform the paradigm of mineral prediction approaches, significantly enhancing their accuracy and efficiency. This shift will accelerate the transformation of Earth science research from traditional, experience-based practices to intelligent and quantitative methodologies, providing essential theoretical and technological support for the next generation of strategic breakthroughs in mineral exploration.

Key words: big data, large language model (LLM), artificial intelligence, mineral resource prediction, non-linear theory, knowledge graph, mineral exploration in deep and covered areas

CLC Number:

CHENG Qiuming. A new paradigm for mineral resource prediction based on human intelligence-artificial intelligence Integration[J]. Earth Science Frontiers, 2025, 32(4): 1-19.

Figures/Tables 14

Fig.1 Historical development of the discipline of quantitative mineral resource prediction

Fig.2 Schematic diagram illustrating key challenges in mineral exploration in covered regions or deep mineral resource prediction:Weak anomaly extraction (left); Decomposition of complexly mixed and superimposed data into backgrounds and anomalies (middle); Integration and synthesis of incomplete or missing multi-source metallogenic information (right)

Fig.3 Framework of nonlinear theories and methods in mineral resource prediction

Fig.4 An integrated model combining geochemical and gravity-magnetic anomalies predicts a “semi-concealed” Mo-polymetallic metallogenic belt in the Xiaodaqingshan-Hongniangshan area. Five evenly spaced intermediate-acid intrusive bodies (including concealed ones) and potential mineralization concentration zones are delineated, encompassing the Caosiyao giant Mo deposit, Quanzigou medium-sized Mo deposit, and undiscovered prospective targets.

Fig.5 Critical scientific questions, key technologies, and targeting objectives related to deep mineral resource prediction and assessment

Fig.6 Study areas showing tectonic settings, mineralization processes, and ore district distribution (Upper) and schematic cross-section of typical deposit types and metallogenic geotectonic background (Lower)

Fig.7 Framework showing relationship diagram among research contents

Fig.8 Schematic diagram illustrating the principles and models of singularity, generalized self-similarity, and fractal spectra. The singularity index Δα used to extract weak signals caused by deep sources (left); the generalized self-similarity of mineralization structures supports interpolation or extrapolation of deep ore bodies (middle); the variation patterns of mineralization series and ore deposit spectra with depth as a basis for deep mineral prediction (right).

Fig.9 Construction of a self-similar structural model for the Jinchuan Cu-Ni sulfide deposit. (A) Simplified map showing the tectonic and magmatic rock distribution in the Jinchuan mining area. (B) The thrusting and overthrusting tectonics result in a 3D self-similar pattern of intrusion-structure relationships. (C) A geological cross-section showing a known ore body exhibiting a pinch-and-swell pattern; at the pinch-out end, a new ore body was discovered.

Fig.10 Distribution map of Mesozoic to Cenozoic porphyry copper deposits along the Circum-Pacific and Tethyan metallogenic belts (Data source: USGS)

Fig.11 Knowledge system of Mathematical Geosciences and its integration into the knowledge base for mineral prediction model development

Fig.12 Knowledge graph and analytical system for porphyry copper deposits

Fig.13 Query results from the knowledge graph of porphyry copper deposits, illustrating the relationship between Cu and S elements in the mineralization process. Circular symbols represent nodes (geological terms), the arrowed links indicate relationships, and the size of a circle represents the frequency of the node’s occurrence.

Fig.14 Architecture of the intelligent mineral resource prediction platform: integration of multifunctional intelligent agents, mineral resources prediction platform, data and computational resource database, knowledge graph and large language models, and large predictive computing models

References 56

[1]	AGTERBERG F P. Computer programs for mineral exploration[J]. Science, 1989, 245(4913): 76-81. PMID
[2]	SINGER D A. Basic concepts in three-part quantitative assessments of undiscovered mineral resources[J]. Nonrenewable Resources, 1993, 2(2): 69-81.
[3]	BONHAM-CARTER G F. Geographic information systems for geoscientists: modelling with GIS[M]. Kidlington: Pergamon, 1994: 1-398.
[4]	CARRANZA E J M. Geochemical anomaly and mineral prospectivity mapping in GIS[M]. Amsterdam: Elsevier, 2009: 1-351.
[5]	李建威, 赵新福, 邓晓东, 等. 新中国成立以来中国矿床学研究若干重要进展[J]. 中国科学: 地球科学, 2019, 49(11): 1720-1771.
[6]	马哲, 魏江桥, 王安建, 等. 矿产资源全球治理要素理论框架构建[J]. 地球学报, 2023, 44(2): 271-278.
[7]	CHEN Y J. Orogenic-type deposits and their metallogenic model and exploration potential[J]. Geology in China, 2006, 33(6): 1181-1196.
[8]	HOU Z Q, YANG Z M, LU Y J, et al. A genetic linkage between subduction- and collision-related porphyry Cu deposits in continental collision zones[J]. Geology, 2015, 43(3): 247-250.
[9]	DENG J, WANG Q F. Gold mineralization in China: metallogenic provinces, deposit types and tectonic framework[J]. Gondwana Research, 2016, 36(10): 219-274.
[10]	ZHU R X, ZHANG H F, ZHU G, et al. Craton destruction and related resources[J]. International Journal of Earth Sciences, 2017, 106(7): 2233-2257.
[11]	HU R Z, FU S L, HUANG Y, et al. The giant South China mesozoic low-temperature metallogenic domain: reviews and a new geodynamic model[J]. Journal of Asian Earth Sciences, 2017, 137: 9-34.
[12]	赵鹏大. “三联式”资源定量预测与评价: 数字找矿理论与实践探讨[J]. 地球科学: 中国地质大学学报, 2002, 27(5): 482-489.
[13]	翟裕生. 地球系统、成矿系统到勘查系统[J]. 地学前缘, 2007, 14(1): 172-181.
[14]	陈毓川, 裴荣富, 王登红. 三论矿床的成矿系列问题[J]. 地质学报, 2006, 80(10): 1501-1508
[15]	王世称. 综合信息矿产预测理论与方法[M]. 北京: 科学出版社, 2000: 1-343.
[16]	CHENG Q M. Non-linear theory and power-law models for information integration and mineral resources quantitative assessments[J]. Mathematical Geology, 2008, 40(5): 503-532.
[17]	CHENG Q M, OBERHÄNSLI R E, ZHAO M L. A new international initiative for facilitating data-driven Earth science transformation[J]. Geological Society Special Publication, 2020, 499(1): 225-240.
[18]	STEPHENSON M H, CHENG Q M, WANG C S, et al. Progress towards the establishment of the IUGS Deep-time Digital Earth (DDE) programme[J]. Episodes, 2020, 43(4): 1057-1062.
[19]	AGTERBERG F. Geomathematics: theoretical foundations, applications and future developments[M]. Cham, Switzerland: Springer, 2014.
[20]	周永章, 黎培兴, 王树功, 等. 矿床大数据及智能矿床模型研究背景与进展[J]. 矿物岩石地球化学通报, 2017, 36(2): 327-331.
[21]	周永章, 陈铄, 张旗, 等. 大数据与数学地球科学研究进展:大数据与数学地球科学专题代序[J]. 岩石学报, 2018, 34(2): 255-263.
[22]	肖克炎. “深部综合信息矿产资源预测评价”专辑特邀主编寄语[J]. 地球学报, 2020, 41(2): 130-134.
[23]	肖克炎. 成矿系列理论与找矿预测应用研究:“矿床成矿系列综合信息找矿预测”专辑特邀主编寄语[J]. 地球学报, 2023, 44(5): 748-752.
[24]	LIU Y, CARRANZA E J M, XIA Q L. Developments in quantitative assessment and modeling of mineral resource potential: an overview[J]. Natural Resources Research, 2022, 31(4): 1825-1840.
[25]	WANG W L, XIE S Y, GRUNSKY E J M, et al. Introduction to the thematic collection: applications of innovations in geochemical data analysis[J]. Geochemistry: Exploration, Environment, Analysis, 2022, 23(1): geochem2022-058.
[26]	CHEN G X, CHENG Q M, STEVE P. Special issue: data-driven discovery in geosciences: opportunities and challenges[J]. Mathematical Geosciences, 2023, 55(3): 287-293.
[27]	SAGAR B S, CHENG Q M, MCKINLEY J, et al. Encyclopedia of mathematical geosciences[M]. Cham: Springer, 2023: 1705.
[28]	XIAO F, CHENG Q M, AGTERBERG F. Fractals in geology and geochemistry[C]// Fractal and fractional. Basel: MDPI, 2025.
[29]	MATHERON G. Principles of geostatistics[J]. Economic Geology, 1963, 58(8): 1246-1266.
[30]	AGTERBERG F P. A probability index for detecting favourable geological environments[J]. Canadian Institute of Mining and Metallurgy, 1971, 10: 82-91.
[31]	赵鹏大. 成矿定量预测与深部找矿[J]. 地学前缘, 2007, 14(5): 1-10.
[32]	HARRIS D P. Mineral resources appraisal: mineral endowment, resources, and potential supply: concepts, methods and cases[M]. Oxford: Oxford University Press, 1984: 445.
[33]	CARGILL S M, MEYER R F, PICKLYK D D, et al. Summary of resource assessment methods resulting from the International Geological Correlation Program Project 98[J]. Mathematical Geology, 1977, 9(3): 211-220.
[34]	HART P E, DUDA R O, EINAUDI M T. Prospector: a computer-based consultation system for mineral exploration[J]. Mathematical Geology, 1978, 10(5): 589-610.
[35]	AGTERBERG F P, BONHAM-CARTER G F. Statistical applications in the earth sciences[C]// Geological survey of Canada. Ottawa: Canadian Government Publishing Centre, 1988: 89-9.
[36]	CHENG Q M, AGTERBERG F P. Fuzzy weights of evidence method and its application in mineral potential mapping[J]. Natural Resources Research, 1999, 8(1): 27-35.
[37]	MALLET J L. Discrete modeling for natural objects[J]. Mathematical Geology, 1997, 29(2): 199-219.
[38]	CHENG Q M, BONHAM-CARTER G F, RAINES G L. GeoDAS: a new GIS system for spatial analysis of geochemical data sets for mineral exploration and environmental assessment[C]// Proceedings of the 20th International Geochemical Exploration Symposium (IGES). Santiago de Chile: Association of Applied Geochemists, 2001: 42-43.
[39]	CHENG Q M, ZHANG S. Fuzzy weights of evidence method implemented in GeoDAS GIS for information extraction and integration for prediction of point events[C]// Proceedings of the 2002 IEEE International Geoscience and Remote Sensing Symposium. Toronto, ON, Canada: IEEE, 2002: 2933-2935.
[40]	CHENG Q M. Singularity theory and methods for mapping geochemical anomalies caused by buried sources and for predicting undiscovered mineral deposits in covered areas[J]. Journal of Geochemical Exploration, 2012, 122: 55-70.
[41]	REICHSTEIN M, CAMPS-VALLS G, STEVENS B, et al. Deep learning and process understanding for data-driven Earth system science[J]. Nature, 2019, 566(7743): 195-204.
[42]	MA X G. Knowledge graph construction and application in geosciences: a review[J]. Computers & Geosciences, 2022, 161: 105082.
[43]	ZHANG Y F, LI J X, WANG Z Y, et al. Geospatial large language model trained with a simulated environment for generating tool-use chains autonomously[J]. International Journal of Applied Earth Observation and Geoinformation, 2025, 136: 104312.
[44]	CHENG Q M. Integration of deep-time digital data for mapping clusters of porphyry copper mineral deposits[J]. Acta Geologica Sinica (English Edition), 2019, 93(Suppl 3): 8-10.
[45]	WANG C S, HAZEN R M, CHENG Q M, et al. The Deep-Time Digital Earth program: data-driven discovery in geosciences[J]. National Science Review, 2021, 8(9): 156-166.
[46]	ZHOU C H, WANG H, WANG C S, et al. Geoscience knowledge graph in the big data era[J]. Science China Earth Sciences, 2021, 64(7): 1105-1114.
[47]	ZHANG Z J, KUSKY T, YANG X K, et al. A paradigm shift in Precambrian research driven by big data[J]. Precambrian Research, 2023, 399: 107235.
[48]	王学求. 勘查地球化学近十年进展[J]. 矿物岩石地球化学通报, 2013, 32(2):190-197.
[49]	COHEN D R, BOWELL R J. Exploration geochemistry[M]// HOLLANDH D, TUREKIANK K. Treatise on geochemistry. 2nd ed. Amsterdam: Elsevier, 2014: 623-650.
[50]	CHEN H Y, ZHANG J L. What is the future road for mineral exploration in the 21st century?[J]. Journal of Earth Science, 2022, 33(5): 1328-1329.
[51]	CHENG Q. Singularity theory and methods for mapping geochemical anomalies caused by buried sources and for predicting undiscovered mineral deposits in covered areas[J]. Journal of Geochemical Exploration, 2012, 122: 55-70.
[52]	CHENG Q. Vertical distribution of elements in regolith over mineral deposits and implications for mapping geochemical weak anomalies in covered areas[J]. Geochemistry: Exploration, Environment, Analysis, 2014, 14(3): 277-289.
[53]	CHENG Q. Multifractal interpolation method for spatial data with singularities[J]. Journal of the Southern African Institute of Mining and Metallurgy, 2015, 115(3): 235-240.
[54]	XIAO F, LIN W P, CHENG Q M. Ab-initio calculations and molecular dynamics simulations of In, Ag, and Cu replacing Zn in sphalerite: implications for critical metal mineralization[J]. Ore Geology Reviews, 2023, 163: 105699.
[55]	XIAO F, WANG K Q, CHENG Q M. Porphyry magma cooling and crystallization control of mineralization: insights from the dynamic numerical modeling[J]. Ore Geology Reviews, 2024, 166: 105956.
[56]	ZUO R G, YANG F F, CHENG Q M., et al. A novel data-knowledge dual-driven model coupling artificial intelligence with a mineral systems approach for mineral prospectivity mapping[J]. Geology, 2025, 53(3): 284-288.