基于混合注意力机制的矿田尺度三维岩性建模:以焦家金矿田为例

doi:10.3799/dqkx.2025.286

基于混合注意力机制的矿田尺度三维岩性建模:以焦家金矿田为例

doi: 10.3799/dqkx.2025.286

李风^1,2,3,
王功文⁴,
卢紫阳^1,2,3,
付超⁵,
刘烊⁶,
东玉龙⁶,
龚天一¹,
张智强^1,2,3, ,

1. 河北地质大学地球科学学院, 河北石家庄 050031;
2. 河北地质大学河北省战略性关键矿产研究协同创新中心, 河北石家庄 050031;
3. 河北地质大学河北省战略性关键矿产资源重点实验室, 河北石家庄 050031;
4. 中国地质大学(北京) 地球科学与资源学院, 北京 100083;
5. 中国地质调查局天津地质调查中心, 天津 300170;
6. 中国地质调查局地球物理调查中心, 河北廊坊 065000

基金项目:

科技部国家科技重大专项重点金矿集区四维建模与数字孪生的靶区优选项目（2024ZD1001900）

国家自然科学基金项目（42402301）

详细信息

作者简介:
李风（2004—）,男，主要从事资源环境大数据工程方向研究。E-mail: feng_li_geo@126.com ORCID：0009-0002-1979-8203

通讯作者:
张智强（1993—），男，副教授，主要从事三维地质建模和矿产资源定量预测研究。E-mail: zq_zhang_geo@126.com

中图分类号: P612；P628
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- 被引次数: 0
出版历程
- 收稿日期: 2025-11-11
- 网络出版日期: 2025-12-29

Three-dimensional (3D) lithological modeling via hybrid attentional mechanism deep learning model: A case study of Jiaojia gold field

Feng Li^1,2,3,
Gongwen Wang⁴,
Ziyang Lu^1,2,3,
Chao Fu⁵,
Yang Liu⁶,
Yulong Dong⁶,
Tianyi Gong¹,
Zhiqiang Zhang^{1,2,3
, ,}

1. Hebei GEO University, Shijiazhuang 050031, China;
2. Hebei Province Collaborative Innovation Center for Strategic key hyperparameter Mineral Research, Hebei GEO University, Shijiazhuang 050031, China;
3. Hebei Key Laboratory of Strategic Critical Mineral Resources, Hebei GEO University, Shijiazhuang 050031, China;
4. School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China;
5. Tianjin Center, China Geological Survey, Tianjin 300170, China;
6. China Geological Survey Geophysical Survey Center, Langfang 065000, China

摘要

摘要: 矿田尺度深部地质结构“透明化”是矿产勘查与成矿预测的核心，三维岩性建模是实现这一目标的关键技术。然而，当前矿田尺度三维岩性建模主要依赖效率较低的显式建模方法，难以满足多阶段矿产勘查和矿山实时生产的需求，因此亟需研发高精度、高效率的三维岩性隐式建模方法。针对上述问题，本研究以三维卷积神经网络（3D Convolutional Neural Network, 3D CNN）为基础，融合卷积注意力机制（Convolutional Block Attention Module, CBAM）与自注意力机制（Self-Attention Module, SAM）构建混合注意力机制深度学习算法（Hybrid Attentional Mechanism Deep Learning Model, HAM），并基于该算法挖掘多源地质—地球物理数据中的深层次特征，确定建模所需地质体边界，实现既能捕捉局部上下文、又能表征全局上下文的三维岩性隐式建模方法。为验证HAM算法有效性，本研究选择胶东半岛焦家金矿田作为研究区，开展对比实验与消融实验。结果表明，相较于随机森林（Random forest, RF）和3D CNN 等基线算法，本次研究提出的HAM算法在三维岩性建模的宏平均准确率、精确率、召回率、宏平均F1-score和混淆矩阵上表现出显著优势，对推动深部找矿和矿山生产具有重要意义。
- 三维岩性隐式建模 /
- 三维卷积神经网络 /
- 卷积注意力机制 /
- 自注意力机制 /
- 焦家金矿田
Abstract: Enhancing the transparency of deep geological structures at the ore-field scale is critical for subsurface mineral exploration and prospectivity modeling. Three-dimensional (3D) lithological modeling serves as a critical technology for this objective. However, existing ore-field–scale modeling workflows rely on explicit modeling approaches with relatively low efficiency, which struggle to meet the demands of multi-stage mineral exploration and real-time mining. Consequently, high-precision and high-efficiency implicit 3D lithological modeling methods are therefore urgently needed. To address this issue, this study constructs the 3D Convolutional Neural Network (3D CNN) and integrates the Convolutional Block Attention Module (CBAM) and the Self-Attention Module (SAM) to construct a Hybrid Attentional Mechanism deep learning model (HAM). Based on this algorithm, deep representations within multi-source geological and geophysical data are mined to determine the boundaries of geological bodies required for modeling, thereby achieving a 3D lithological implicit modeling method capable of capturing both local details and long-range dependencies. To validate the effectiveness of the proposed hybrid attentional mechanism model, the Jiaojia gold field in the Jiaodong Peninsula was selected as the study area, and comparative and ablation experiments were conducted. Relative to baseline models—Random Forest (RF) and a vanilla 3D-CNN, HAM markedly improves the macro-averaged accuracy, precision, recall, macro-averaged F1 score and confusion matrix of ore-field–scale implicit 3D lithological modeling, with direct implications for subsurface mineral exploration and mining operations.
- 3D lithological implicit modeling /
- Three-dimensional Convolutional Neural Network /
- Convolutional Block Attention Module /
- Self-Attention Module /
- Jiaojia gold field

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基于混合注意力机制的矿田尺度三维岩性建模:以焦家金矿田为例

doi: 10.3799/dqkx.2025.286

作者简介:
李风（2004—）,男，主要从事资源环境大数据工程方向研究。E-mail: feng_li_geo@126.com ORCID：0009-0002-1979-8203

通讯作者:
张智强（1993—），男，副教授，主要从事三维地质建模和矿产资源定量预测研究。E-mail: zq_zhang_geo@126.com

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Three-dimensional (3D) lithological modeling via hybrid attentional mechanism deep learning model: A case study of Jiaojia gold field

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留言板

基于混合注意力机制的矿田尺度三维岩性建模:以焦家金矿田为例

doi: 10.3799/dqkx.2025.286

作者简介: 李风（2004—）,男，主要从事资源环境大数据工程方向研究。E-mail: feng_li_geo@126.com ORCID：0009-0002-1979-8203

通讯作者: 张智强（1993—），男，副教授，主要从事三维地质建模和矿产资源定量预测研究。E-mail: zq_zhang_geo@126.com

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Three-dimensional (3D) lithological modeling via hybrid attentional mechanism deep learning model: A case study of Jiaojia gold field

计量

出版历程

目录

作者简介:
李风（2004—）,男，主要从事资源环境大数据工程方向研究。E-mail: feng_li_geo@126.com ORCID：0009-0002-1979-8203

通讯作者:
张智强（1993—），男，副教授，主要从事三维地质建模和矿产资源定量预测研究。E-mail: zq_zhang_geo@126.com