Intelligent Mineral Prospectivity Mapping Constrained by Deposit Scale in the Zhaishang⁃Liba Mining District, West Qinling Orogen, China

Wang Bin; Gao Yongbao; Liu Xiangdong; Zhu Zhihao; Hou Cong; Bai Zhaohua; Wang Huaitao; Jing Linhai

doi:10.3799/dqkx.2026.082

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Intelligent Mineral Prospectivity Mapping Constrained by Deposit Scale in the Zhaishang⁃Liba Mining District, West Qinling Orogen, China

更新时间：2026-10-15

- Intelligent Mineral Prospectivity Mapping Constrained by Deposit Scale in the Zhaishang⁃Liba Mining District, West Qinling Orogen, China
- Earth Science Vol. 51, Issue 9, Pages: 3513-3529(2026)
- 作者机构：
  
  1.中国地质调查局西安矿产资源调查中心，陕西西安 710100
  2.中国地质调查局金矿勘查技术创新中心，陕西西安 710100
  3.甘肃省地质调查院，甘肃兰州 730000
  4.中国地质大学（北京）人工智能学院，北京 100083
- 作者简介：
- 基金信息：
- DOI：10.3799/dqkx.2026.082
  CLC： P612
- Received：26 December 2025，
  
  Published：30 September 2026
- 稿件说明：
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王斌，高永宝，刘向东，朱志昊，侯聪，白兆华，王怀涛，荆林海，2026.基于矿床规模约束的西秦岭寨上‒李坝矿集区智能矿产预测.地球科学，51（9）：3513-3529.

Wang Bin，Gao Yongbao，Liu Xiangdong，Zhu Zhihao，Hou Cong，Bai Zhaohua，Wang Huaitao，Jing Linhai，2026.Intelligent Mineral Prospectivity Mapping Constrained by Deposit Scale in the Zhaishang⁃Liba Mining District， West Qinling Orogen， China.Earth Science，51（9）：3513-3529.
王斌，高永宝，刘向东，朱志昊，侯聪，白兆华，王怀涛，荆林海，2026.基于矿床规模约束的西秦岭寨上‒李坝矿集区智能矿产预测.地球科学，51（9）：3513-3529. DOI： 10.3799/dqkx.2026.082.

Wang Bin，Gao Yongbao，Liu Xiangdong，Zhu Zhihao，Hou Cong，Bai Zhaohua，Wang Huaitao，Jing Linhai，2026.Intelligent Mineral Prospectivity Mapping Constrained by Deposit Scale in the Zhaishang⁃Liba Mining District， West Qinling Orogen， China.Earth Science，51（9）：3513-3529. DOI： 10.3799/dqkx.2026.082.

摘要

西秦岭寨上‒李坝矿集区是我国新一轮找矿突破战略行动重点勘查区之一.受浅地表厚层覆盖影响，传统找矿方法面临严峻挑战.基于大数据与人工智能的矿产预测为识别覆盖区弱矿化异常提供新思路.然而，现有模型在正样本构建环节，鲜少兼顾矿床规模与成矿能量场的空间差异性，制约了预测模型的精度与可靠性.为此，本文创新提出一种基于“资源量‒面积”动态约束的正样本构建方法.综合地质、地球化学及遥感等多源找矿数据，提取了17个预测变量，构建了随机森林预测模型，并结合SHAP方法进行地质可解释性分析.该预测模型表现出优异的泛化与分类性能，在概率阈值0.52下，仅以8%的预测面积即捕获了92%的已知金矿床；SHAP分析揭示，Au、Sn、Cu、Zn、断层密度、Sb、As异常以及岩体缓冲等变量在预测中发挥主导作用；预测高概率区在空间上呈现显著受控于多期次断裂叠加的“线状”与环绕“五朵金花”复式岩体的“环状”展布模式，深刻反映了该区金成矿作用严格受“构造‒岩浆”系统时空耦合控制的成矿动力学机制.依据预测结果优选了6处深部找矿靶区，其中P1靶区经深部钻孔（ZK4-1）验证成功探获最高品位达1.24 g/t的金矿化体.研究表明，基于矿床规模约束的智能矿产预测体系在覆盖区具有显著的科学指示意义与应用前景.

Abstract

The Zhaishang-Liba mining district in the West Qinling Orogen is one of the key exploration areas for the National Strategic Action for Mineral Exploration Breakthrough in China. Due to the thick shallow surface cover

traditional exploration methods face severe challenges. Mineral prospectivity mapping driven by big data and artificial intelligence provides a new perspective for identifying weak mineralization anomalies in covered terrains. However

during the generation of positive samples

existing models rarely consider the spatial heterogeneity of deposit scales and metallogenic energy fields

which restricts the accuracy and reliability of the predictive models. Therefore

this paper proposes an innovative positive sample construction method dynamically constrained by the “resource-area” relationship. Integrating multi-source exploration data

including geological

geochemical

and remote sensing data

17 predictor variables were extracted to construct a Random Forest (RF) predictive model

coupled with the SHAP (SHapley Additive exPlanations) method for geological interpretability analysis. The predictive model demonstrates excellent generalization and classification performance. Under a probability threshold of 0.52

it successfully captures 92% of the known gold deposits within merely 8% of the predicted area. SHAP analysis reveals that variables such as Au

fault density

and proximity to intrusions play dominant roles in the prediction. The predicted high-probability areas spatially exhibit a “linear” distribution pattern significantly controlled by the superposition of multi-stage faults

and an “annular” pattern surrounding the “Five Golden Flowers” composite intrusions

profoundly reflecting the metallogenic dynamic mechanism that gold mineralization in this area is strictly controlled by the spatiotemporal coupling of the “tectonic-magmatic” system. Based on the prediction results

6 deep exploration targets were delineated. Notably

Target P1 was verified by a deep drill hole (ZK4-1)

which successfully intercepted a gold mineralization body with a maximum grade of 1.24 g/t. The study demonstrates that the intelligent mineral prospectivity mapping framework constrained by deposit scale holds significant scientific indicative value and application prospects in covered areas.

关键词

Keywords

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