利用闪锌矿微量元素地球化学进行铅锌矿床类型的机器学习识别

作者：小柯机器人 发布时间：2026/7/5 19:31:03

近日，昆明理工大学Zhao, Dong团队利用闪锌矿微量元素地球化学进行铅锌矿床类型的机器学习识别：来自TPE优化SVM模型和SHAP解释的见解。该项研究成果发表在2026年6月24日出版的《地球化学学报》杂志上。

闪锌矿中微量元素分布模式的独特地球化学指纹，对于区分Pb-Zn矿床类型尤为有用。

研究组基于闪锌矿分析数据集，利用树结构帕尔森估计器（TPE）优化的支持向量机（SVM）算法，开发了一个高性能的闪锌矿矿床类型分类模型。该数据集包含来自同行评审文献的3117条闪锌矿分析数据，涵盖了全球102个代表性Pb-Zn矿床，分属五种主要成因类型，包括沉积喷流型（SEDEX）、火山成因块状硫化物型（VMS）、密西西比河谷型（MVT）、矽卡岩型和浅成低温热液型矿床。每条分析涵盖12种关键微量元素（Mn、Fe、Co、Cu、Ga、Ge、Ag、Cd、In、Sn、Sb和Pb）。

优化后的模型展现出卓越的判别能力，测试集准确率达到0.9749，并在精确率、召回率和F1分数上表现一致。SHAP（SHapley加性解释）可解释性分析揭示，关键指示元素（Mn、Ge和Co）对成因分类至关重要，但不同矿床类型间存在差异性的微量元素特征模式。

降维分析（UMAP和t-SNE）显示了岩浆热液型矿床（矽卡岩、VMS、浅成低温热液型）与沉积相关体系（MVT、SEDEX）之间的明显聚类，反映了闪锌矿微量元素特征的系统性差异。通过在富乐和浩布高Pb-Zn矿床上进行基于机器学习的盲测分类验证了该方法。结果表明，TPE优化的SVM模型能够识别闪锌矿中可解释的地球化学模式，是区分不同类型Pb-Zn矿床的有效工具。

附：英文原文

Title: Machine learning identification of Pb–Zn deposit types using sphalerite trace-element geochemistry: Insights from a TPE-optimized SVM model and SHAP interpretation

Author: Chen, Zhongyuan, Ren, Tao, Zhao, Dong

Issue&Volume: 2026-06-24

Abstract: The unique geochemical fingerprints of trace-element distribution patterns in sphalerite are particularly useful for discriminating Pb–Zn deposit types. In this study, we developed a high-performance sphalerite classification model for deposit types based on a dataset of sphalerite analyses, using tree-structured Parzen estimator (TPE) optimization with a support vector machine (SVM) algorithm. The dataset comprises 3117 analyses of sphalerite sourced from peer-reviewed publications covering 102 representative Pb–Zn deposits worldwide spanning five major genetic types, including sedimentary exhalative (SEDEX), volcanic massive sulfide (VMS), Mississippi Valley type (MVT), skarn, and epithermal deposits. Each analysis covers 12 critical trace elements (Mn, Fe, Co, Cu, Ga, Ge, Ag, Cd, In, Sn, Sb, and Pb). The optimized model demonstrated exceptional discriminative capability. It achieved a test-set accuracy of 0.9749 and delivered consistent performance across the precision, recall, and F1-score metrics. SHAP (SHapley Additive exPlanations) interpretability analysis revealed that key indicator elements (Mn, Ge, and Co) are critical for genetic classification, although there are distinct patterns of trace elements across deposit types. Dimensionality-reduction analyses (UMAP and t-SNE) reveal distinct clustering of magmatic-hydrothermal deposits (skarn, VMS, epithermal) and sedimentary-related systems (MVT, SEDEX), reflecting systematic differences in sphalerite trace-element signatures. This methodology was validated by conducting blind, machine-learning-based classification tests on the Fule and Haobugao Pb–Zn deposits. The results suggest that the TPE-optimized SVM model can identify interpretable geochemical patterns in sphalerite, making it an effective tool for distinguishing between different types of Pb–Zn deposits.

DOI: 10.1007/s11631-026-00889-9

Source: https://link.springer.com/article/10.1007/s11631-026-00889-9