基于集成学习模型与贝叶斯优化算法的成矿预测

doi:10.13745/j.esf.sf.2025.4.66

地学前缘 ›› 2025, Vol. 32 ›› Issue (4): 122-139.DOI: 10.13745/j.esf.sf.2025.4.66

基于集成学习模型与贝叶斯优化算法的成矿预测

孔春芳¹^,²^,³^,⁴(), 田倩¹, 刘健⁵, 蔡国荣¹^,⁵, 赵杰¹, 徐凯¹^,²^,³^,⁴^,^*()

1.中国地质大学(武汉) 计算机学院, 湖北武汉 430074
2.自然资源部基岩区矿产资源勘查工程技术创新中心, 贵州贵阳 550081
3.贵州省战略矿产智慧勘查全省重点实验室, 贵州贵阳 550081
4.智能地学信息处理湖北省重点实验室, 湖北武汉 430074
5.贵州省地质矿产勘查开发局一○三地质大队, 贵州铜仁 554300

收稿日期:2025-05-12 修回日期:2025-05-20 出版日期:2025-07-25 发布日期:2025-08-04
通信作者: *徐凯(1972—),男,博士,副教授,主要从事数据挖掘与知识发现、基于大数据的智能找矿、定量遥感与地学信息工程等方面的教学与研究工作。E-mail: xukai@cug.edu.cn
作者简介:孔春芳(1973—),女,博士,副教授,主要从事遥感与地理信息系统应用方面的教学与科研工作。E-mail: kongcf@cug.edu.cn
基金资助:
贵州省找矿突破战略行动重大协同创新项目(黔科合战略找矿[2022]ZD004);湖北省自然科学基金项目(2021CFB506);智能地学信息处理湖北省重点实验室开放基金项目(KLIGIP-2023-B08);贵州省基础研究计划(自然科学)(QKHJC-ZK[2023]G 194);贵州省重大科技成果转化项目(黔科合[2022]重点003);贵州省地质矿产勘查开发局项目(黔地矿科合[2021]3);贵州省地质矿产勘查开发局项目(黔地矿科合[2023]1);铜仁市科技局科研项目(铜市科研[2024]97号)

Metallogenic prediction based on ensemble learning models and Bayesian Optimization Algorithm

KONG Chunfang¹^,²^,³^,⁴(), TIAN Qian¹, LIU Jian⁵, CAI Guorong¹^,⁵, ZHAO Jie¹, XU Kai¹^,²^,³^,⁴^,^*()

1. School of Computer, China University of Geosciences (Wuhan), Wuhan 430074, China
2. Engineering Technology Innovation Center of Mineral Resource Explorations in Bedrock Zones, Ministry of Natural Resources, Guiyang 550081, China
3. Guizhou Key Laboratory for Strategic Mineral Intelligent Exploration, Guiyang 550081, China
4. Hubei Key Laboratory of Intelligent Geo-Information Processing, Wuhan 430074, China
5. Geology Team 103, Bureau of Geology and Mineral Exploration and Development, Tongren 554300, China

Received:2025-05-12 Revised:2025-05-20 Online:2025-07-25 Published:2025-08-04

摘要/Abstract

摘要：

全球进入隐伏矿体勘查时代,急需新的找矿预测方法。利用集成学习进行的数据驱动的成矿预测模型正在成为深部隐伏矿产勘探的有力工具。然而,基于集成学习的成矿预测模型面临着一些普遍的问题,特别是模型的参数调优。模型的参数调优是一个非常耗时的过程,需要繁琐的计算和足够的专家经验。本文提出了一种基于多源地学知识与贝叶斯优化算法的集成学习模型来解决上述问题。具体来说,首先,基于多源地学知识,构建锰矿成矿预测数据库;其次,基于自适应提升模型(Adaptive Boosting,AdaBoost)和随机森林(Random Forest,RF)模型,建立黔东北锰矿成矿预测模型;然后,采用贝叶斯优化算法(Bayesian Optimization,BO),通过5倍交叉验证的辅助,寻找BO-AdaBoost和BO-RF模型最合适的超参数组合;最后,利用精度、准确率、召回率、F₁分数、kappa系数、AUC值等参数及已有成果检测模型的性能。实验结果发现,BO-AdaBoost和BO-RF模型的AUC值都得到了显著的提高,表明BO是一个强大的优化工具,优化结果为集成学习模型的超参数设置提供了参考。同时,实验结果也表明:BO-AdaBoost模型(92.8%)比BO-RF模型(89.9%)具有更高的预测精度和地质泛化能力,在成矿预测方面具有巨大潜力。基于BO-AdaBoost模型的预测图为黔东北隐伏锰矿矿床的勘探提供了重要线索,并可以指导未来的矿产勘探与开发。

关键词: 集成学习, 自适应提升模型, 随机森林, 贝叶斯优化算法, 隐伏锰矿, 成矿预测

Abstract:

Exploration for hidden ore bodies is increasingly important and demands innovative prospecting methods. Data-driven metallogenic prediction models using ensemble learning are becoming powerful tools for deep mineral exploration. However, such models face challenges, particularly in time-consuming hyperparameter tuning requiring extensive computation and expertise. To address this, we propose a framework integrating multi-source geological knowledge with Bayesian Optimization (BO) for ensemble learning. Specifically, a manganese (Mn) metallogenic prediction database integrating multi-source geological knowledge is first constructed. Metallogenic prediction models for Mn ore in northeastern Guizhou are then established using Adaptive Boosting (AdaBoost) and Random Forest (RF). The hyperparameters of these base models are optimized using Bayesian Optimization (BO) via 5-fold cross-validation, resulting in the optimized BO-AdaBoost and BO-RF models. Model performance is evaluated using metrics including accuracy, precision, recall, F₁-score, kappa, and AUC values. Results show significant improvements in AUC for both BO-optimized models compared to their non-optimized counterparts, demonstrating BO’s effectiveness for ensemble learning hyperparameter tuning. Furthermore, the BO-AdaBoost model achieves higher prediction accuracy (92.8%) and generalization performance than the BO-RF model (89.9%), highlighting its strong potential for metallogenic prediction. The prospectivity map generated by the BO-AdaBoost model provides critical guidance for exploring deep-hidden Mn deposits in northeastern Guizhou and can direct future mineral exploration and development.

Key words: ensemble learning, Adaptive Boosting (AdaBoost), Random Forest (RF), Bayesian Optimization (BO), hidden manganese ore, metallogenic prediction

中图分类号:

孔春芳, 田倩, 刘健, 蔡国荣, 赵杰, 徐凯. 基于集成学习模型与贝叶斯优化算法的成矿预测[J]. 地学前缘, 2025, 32(4): 122-139.

KONG Chunfang, TIAN Qian, LIU Jian, CAI Guorong, ZHAO Jie, XU Kai. Metallogenic prediction based on ensemble learning models and Bayesian Optimization Algorithm[J]. Earth Science Frontiers, 2025, 32(4): 122-139.

图/表 19

图1 基于多源地学知识和贝叶斯优化集成学习方法的锰矿成矿预测流程

Fig.1 Flowchart of the Mn ore metallogenic prediction based on multi-source geological knowledge and Bayesian optimization ensemble learning method

图2 AdaBoost算法流程图

Fig.2 Flowchart of the AdaBoost algorithm

表1 AdaBoost算法步骤

Table 1 The steps of AdaBoost algorithm

步骤	步骤描述
1	标准化、归一化数据集,并按照7∶2∶1的比例分配训练集、测试集和验证集
2	为每一个样本赋予同样的权重,训练出第一个决策树DT₁,让DT₁对样本x_i进行分类得到预测值G₁(x_i),并依据公式 $∑ i = 1 m w k i I (G k (x i) ≠ y i)$ 计算误差率e₁
3	利用误差率e₁进行轮次迭代,依据公式α_k= $1 2 l g (1 - e k) e k$ 来更新分类器本轮次的自身权重α₁,随后结合e₁、α₁以及G₁(x_i),并根据公式(1)和(2)计算出下一轮次每个样本的权重w₂_i。接着依据样本集和样本权重,训练出第二个分类器DT₂,并以此类推
4	不断重复步骤3,共构造15个弱分类器和15个分类器权重α_k。然后使用累加投票法(公式(3))组合成强分类器

表1 AdaBoost算法步骤

Table 1 The steps of AdaBoost algorithm

步骤	步骤描述
1	标准化、归一化数据集,并按照7∶2∶1的比例分配训练集、测试集和验证集
2	为每一个样本赋予同样的权重,训练出第一个决策树DT₁,让DT₁对样本x_i进行分类得到预测值G₁(x_i),并依据公式 $∑ i = 1 m w k i I (G k (x i) ≠ y i)$ 计算误差率e₁
3	利用误差率e₁进行轮次迭代,依据公式α_k= $1 2 l g (1 - e k) e k$ 来更新分类器本轮次的自身权重α₁,随后结合e₁、α₁以及G₁(x_i),并根据公式(1)和(2)计算出下一轮次每个样本的权重w₂_i。接着依据样本集和样本权重,训练出第二个分类器DT₂,并以此类推
4	不断重复步骤3,共构造15个弱分类器和15个分类器权重α_k。然后使用累加投票法(公式(3))组合成强分类器

图3 研究区域地理位置图(a)与构造纲要略图(b)

Fig.3 Geographical location map (a) and structural outline (b) of the study area

图4 武陵次级裂谷构造格架及大塘坡锰矿的空间分析(据文献[1,38]修改) 1—实测与推测断层;2—同生断层;3—锰矿床分布范围;4—超大型锰矿;5—地堑;6—地垒;7—铜仁古裂谷。

Fig.4 Spatial distribution of Datangpo type Mn ore deposits and tectonic framework of Wuling secondary rift. Modified after [1,38].

图5 地质变量预测证据层包括地层(a)、断层缓冲区(b)和褶皱缓冲区(c)

Fig.5 The prediction evidence layer for geological variables includes stratum (a), fault buffer zone (b) and (c) fold buffer zone

图6 通过小波分析提取的向下延拓2 km(a)、6 km(b)、10 km(c)和20 km(d)的多尺度地球物理信息

Fig.6 Multiscale geophysical information of 2 km (a), 6 km (b), 10 km (c) and 20 km (d) down-extension extracted by wavelet analysis

图7 通过小波分析提取多尺度航磁ΔT的等值线(a)和ΔT化极等值线(b)

Fig.7 Multi-scale aeromagnetic ΔT contour (a) and polar (b) contour extracted by wavelet analysis

图8 黔东北地区Mn(a)、Cu-As-Co-Cr-Ni(b)、Pb-Zn-Fe-Si(c)和所有元素(d)的地球化学异常图

Fig.8 The geochemical anomaly of Mn (a) Cu-As-Co-Cr-Ni (b) Pb-Zn-Fe-Si (c) and all elements (d) in northeastern Guizhou

图9 基于遥感影像的锰矿化伴生矿物提取结果及异常区分布图(据文献[42]修改)

Fig.9 Extraction results and abnormal area distribution of Mn mineralization associated minerals based on remote sensing

图10 为训练机器学习模型而准备的手动标记的矿点与非矿点的空间分布

Fig.10 Spatial distribution of manually labeled mineral and non-mineral spots for training machine learning model

表2 贝叶斯优化随机森林模型的超参数的过程及结果

Table 2 Hyperparameters optimization process and result of BO-RF model

序号	精度	max_depth	max_features	min_samples_split	n_estimators
1	0.882	9	0.754	8	137
2	0.893	17	0.890	19	174
3	0.899	12	0.920	10	120
4	0.893	16	0.254	16	109
5	0.892	16	0.493	15	182
6	0.898	15	0.944	5	162
7	0.894	13	0.797	18	123
8	0.889	11	0.927	17	72
9	0.885	19	0.729	9	82
10	0.755	1	0.835	7	20
11	0.896	20	0.100	20	10
12	0.895	7	0.120	10	149
13	0.895	14	0.100	20	10
14	0.897	9	0.100	2	119
15	0.894	8	0.990	20	49
16	0.894	20	0.100	2	200
Best	0.899	12	0.920	10	120

表3 Geo-AdaBoost模型的贝叶斯超参数优化过程及结果

Table 3 Hyperparameters optimization process and result of BO-AdaBoost model

序号	精度	max_depth	learning_rate	min_samples_split	min_samples_leaf	n_estimators
1	0.911	5	0.322	19	15	137
2	0.903	9	0.951	7	18	174
3	0.908	5	0.87	4	18	146
4	0.911	8	0.804	13	4	109
5	0.914	8	0.718	11	9	182
6	0.891	8	0.196	5	19	162
7	0.916	7	0.875	8	2	123
8	0.907	6	0.839	11	18	72
9	0.903	10	0.421	18	14	82
10	0.904	1	0.262	15	17	120
11	0.912	1	0.378	3	1	113
12	0.903	2	0.496	12	6	21
13	0.928	7	0.516	3	2	196
14	0.900	6	0.318	13	20	10
15	0.908	2	0.312	17	20	200
16	0.907	2	0.572	13	2	14
Best	0.928	7	0.516	3	2	196

图11 4个模型的损失函数曲线图

Fig.11 Loss function plots for the four models

图12 4个模型的验证精度曲线图

Fig.12 Validation accuracy plots for the four models

图13 4个机器学习模型的ROC曲线与AUC值

Fig.13 ROC curves and AUC values for the four models

表4 4个集成学习模型的性能比较

Table 4 Comparison of performance for the four models

模型	精度	准确率	召回率	F₁分数	kappa	AUC
RF	0.888	0.900	0.883	0.885	0.773	0.883 2
BO-RF	0.899	0.907	0.897	0.896	0.805	0.891 6
AdaBoost	0.912	0.920	0.906	0.910	0.821	0.906 8
BO-AdaBoost	0.928	0.940	0.923	0.926	0.854	0.962 1

图14 使用RF(a)、BO-RF(b)、AdaBoost(c)和BO-AdaBoost(d)模型预测的Mn的成矿预测图与已知矿点叠加图

Fig.14 Overlay of prediction maps of Mn and known ore points using RF (a), AdaBoost (b), BO-RF (c) and BO-AdaBoost (d) models

表5 4个集成学习模型的预测结果占总面积百分比

Table 5 The percentage of the total area for the four models predictions results

模型	各区域面积占比/%
模型	极低区域	低区域	中等区域	高区域	极高区域
RF	62.13	21.47	6.77	4.88	4.75
BO-RF	63.99	19.16	8.13	4.62	4.10
AdaBoost	69.03	18.47	5.19	4.39	2.93
BO-AdaBoost	72.75	15.26	5.37	4.33	2.29

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基于集成学习模型与贝叶斯优化算法的成矿预测

Metallogenic prediction based on ensemble learning models and Bayesian Optimization Algorithm

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