Advances in intelligent extraction and identification of metallogenic information for mineral prospectivity mapping

XIAO Keyan; WANG Zhengyao; LIU Bingli; LI Cheng; CAO Changjie; KONG Yunhui; WANG Yao

doi:10.12090/j.issn.1006-6616.2026098

Volume 32 Issue 5

Oct. 2026

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XIAO K Y，WANG Z Y，LIU B L，et al.，2026. Advances in intelligent extraction and identification of metallogenic information for mineral prospectivity mapping[J]. Journal of Geomechanics，32（5）：1284−1297 doi: 10.12090/j.issn.1006-6616.2026098

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Advances in intelligent extraction and identification of metallogenic information for mineral prospectivity mapping

doi: 10.12090/j.issn.1006-6616.2026098

XIAO Keyan^{1, 2, 3, 4
,},
WANG Zhengyao^{1, 2, 3
,
,},
LIU Bingli^{1, 2
,},
LI Cheng^{1, 2
,},
CAO Changjie^{1, 2
,},
KONG Yunhui^{1, 2
,},
WANG Yao^{1, 2, 3
,}

1.
School of Mathematical Sciences, Chengdu University of Technology, Chengdu 610059, Sichuan, China
2.
Geomathematics Key Laboratory of Sichuan Province, Chengdu 610059, Sichuan, China
3.
Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China
4.
State Key Laboratory of Deep Earth and Mineral Exploration, Beijing 100037, China

Funds: This research was financially supported by the Science and Technology Department of Xinjiang Uygur Autonomous Region (Grant No. 2024B03009-3) and the Science and Technology Department of Sichuan Province (Grant Nos. 2024NSFSC0009, 2026NSFSCZY0043, and 2026NSFSC0238).

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Author Bio:
肖克炎，男，二级研究员，博士生导师，俄罗斯科学院与工程院外籍院士。2023年获第十八次李四光地质科学奖科研奖。曾任成矿远景区划研究室总工程师、主任及矿产资源研究所副总地质师等职。长期从事数学地质、矿产资源评价、成矿预测、深部矿产资源预测及矿产资源大数据智能评价研究。长期以来，围绕矿产资源预测评价理论与技术开展系统研究，建立了成矿系列综合地质信息预测理论方法和“找矿模型—三维建模—定量预测”深部矿产资源预测理论方法体系，研发了区域矿产资源预测评价方法平台（MRAS）及“探矿者”软件系统。近年来进一步开展大数据、人工智能与地质知识融合的智能矿产预测研究，研究方向由数字化矿产预测逐步拓展至大数据智能评价和知识—数据联合驱动的智能找矿。已出版专著16部，发表论文300余篇。先后获国家科学技术进步奖二等奖3项、省部级科学技术奖一等奖4项、二等奖2项。享受国务院政府特殊津贴，并获自然资源部科技创新人才等荣誉称号
Received: 2026-07-15
Revised: 2026-08-31
Accepted: 2026-09-11

Available Online: 2026-09-24

Published: 2026-10-28

Abstract

Abstract

Objective The exploration of deep, concealed, and covered mineral resources is increasingly affected by scale mismatches, background interference, and ambiguous geoscientific responses. This study aims to clarify the hierarchical relationships and methodological pathways through which metallogenic knowledge can be transformed into computable and verifiable predictive information. Methods Structural geological, geochemical, geophysical remote-sensing, and three-dimensional spatial information were systematically reviewed. The review covered spatial quantification, statistical analysis, conventional machine learning, deep learning, geological-knowledge constraints, and spatial validation. A hierarchical framework of "raw data–geological objects and anomalous responses–metallogenic information–spatial representation–predictive variables–quantitative prediction–validation" was established, and different methods were compared in terms of data organization, applicable scale, sample design, interpretability, and validation strategy. Results Six classes of mineral system information were synthesized, including material sources, migration pathways, favorable sites for mineralization, precipitation and enrichment, preservation conditions, and known mineralization. Two principal spatial representations were summarized, namely two-dimensional thematic maps/evidence layers and three-dimensional attribute volumes. Six technical components were identified: information spatialization, anomaly and multivariate-association extraction, conventional machine-learning recognition, deep spatial representation, geological knowledge constraints, and spatial or engineering validation. Both convolutional neural networks (CNNs) and graph neural networks/graph convolutional networks (GNNs/GCNs) belong to the field of deep learning, but are suited to different data structures: CNNs are generally better suited to regular grids, including two-dimensional rasters and three-dimensional voxels, whereas GNNs/GCNs are more suitable for irregular graph-structured data. Random sample splitting may cause spatial leakage, while spatial-block, leave-area-out, cross-district, and engineering validation are more appropriate for evaluating model transferability. Conclusions Metallogenic information is computable evidence produced by quantifying metallogenic knowledge. Its reliability depends primarily on geological semantics, scale compatibility, data quality, sample representativeness, and independent validation rather than algorithmic complexity. Intelligent extraction and identification of metallogenic information should therefore remain constrained by mineral-system concepts, deposit models, and prospecting models, with future emphasis on multisource integration, coordinated two- and three-dimensional representation, uncertainty characterization, and interpretable learning.
- metallogenic information,
- intelligent extraction and identification,
- metallogenic model,
- predictive variable,
- mineral prospectivity mapping

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