基于机器学习的隐式三维地质建模：以牟乳成矿带腊子沟金矿为例

王统荣; 纪旭波; 王江波; 刘洋; 邵玉宝; 王勇军; 黄鑫; 高涛; 姜鹏; 单江涛; 谭俊; 赵志新

doi:10.3799/dqkx.2025.048

Volume 50 Issue 8

Aug. 2025

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Article Navigation > Earth Science > 2025 > 50(8): 3167-3181

Wang Tongrong, Ji Xubo, Wang Jiangbo, Liu Yang, Shao Yubao, Wang Yongjun, Huang Xin, Gao Tao, Jiang Peng, Shan Jiangtao, Tan Jun, Zhao Zhixin, 2025. Implicit 3D Geological Modeling Based on Machine Learning: A Case Study of Lazigou Gold Deposit in Muping-Rushan Metallogenic Belt. Earth Science, 50(8): 3167-3181. doi: 10.3799/dqkx.2025.048

Citation:

Wang Tongrong, Ji Xubo, Wang Jiangbo, Liu Yang, Shao Yubao, Wang Yongjun, Huang Xin, Gao Tao, Jiang Peng, Shan Jiangtao, Tan Jun, Zhao Zhixin, 2025. Implicit 3D Geological Modeling Based on Machine Learning: A Case Study of Lazigou Gold Deposit in Muping-Rushan Metallogenic Belt. Earth Science, 50(8): 3167-3181. doi: 10.3799/dqkx.2025.048

Citation:

Wang Tongrong, Ji Xubo, Wang Jiangbo, Liu Yang, Shao Yubao, Wang Yongjun, Huang Xin, Gao Tao, Jiang Peng, Shan Jiangtao, Tan Jun, Zhao Zhixin, 2025. Implicit 3D Geological Modeling Based on Machine Learning: A Case Study of Lazigou Gold Deposit in Muping-Rushan Metallogenic Belt. Earth Science, 50(8): 3167-3181. doi: 10.3799/dqkx.2025.048

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Implicit 3D Geological Modeling Based on Machine Learning: A Case Study of Lazigou Gold Deposit in Muping-Rushan Metallogenic Belt

doi: 10.3799/dqkx.2025.048

Wang Tongrong^{1
,
,},
Ji Xubo²,
Wang Jiangbo²,
Liu Yang¹,
Shao Yubao³,
Wang Yongjun^{1, 3},
Huang Xin^{3, 4},
Gao Tao²,
Jiang Peng²,
Shan Jiangtao²,
Tan Jun¹,
Zhao Zhixin^{1
,
,
,}

1.
School of Earth Resources, China University of Geosciences, Wuhan 430074, China
2.
Shandong Hengbang Smelting Co., Ltd., Yantai 264109, China
3.
Shandong Research Institute of Coal Geology Planning and Exploration, Jinan 250104, China
4.
College of Earth Sciences, Jilin University, Changchun 130061, China

Received Date: 2025-02-23
Publish Date: 2025-08-25

Abstract

Abstract

This study aims to develop a methodology for implicit 3D geological modeling under the constraint of limited exploration data availability. Taking Lazigougold deposit as an example, we generate virtual grooving by distance weighting method based on the original grooving sampling data, and use the original grooving data to train and evaluate three machine learning models, namely K-nearest neighbor, random forest and gradient elevator, and select the random forest model with the best prediction performance to discriminate and predict the lithology of virtual grooving. Virtual encryption of groove data is realized by machine learning method, which provides a large number of sample data for implicit 3D modeling. On this basis, the orebody model and Au element grade numerical model of Lazigou gold mine were constructed in implicit modeling software using the original groove and virtual groove data. Five prospecting targets have been delimited, which have been proved reliable by engineering. The implicit 3D modeling based on machine learning can make full use of the known data to predict the unknown region and provide sufficient samples for the implicit 3D modeling, which is conducive to the construction of a higher precision geological model under the existing exploration engineering conditions, and then provide a basis for the deep edge prospecting prediction.
- Lazigou gold deposit,
- implicit 3D modeling,
- machine learning,
- random forest,
- 3D mineral prediction

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References(62)

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