Machine Learning-Based Discrimination Model for Rare Earth Elements of Fluorite Deposits in Guizhou Province
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摘要:
贵州西部萤石矿集区内新识别出的隐爆角砾岩型萤石矿具有巨大的找矿潜力.然而该类型萤石矿角砾状构造、热液蚀变等特征易与其他热液角砾岩型矿床或强烈构造改造的脉型矿床相混淆，如何准确识别研究区内隐爆角砾岩型和与盆地卤水相关热液填充型萤石矿是实现贵州省萤石矿找矿突破的关键科学问题之一.本文通过对系统收集的隐爆角砾岩型、与岩浆热液相关的热液充填型和与盆地卤水相关热液填充型三种成因类型萤石矿的稀土元素数据进行支持向量机和随机森林机器学习分类模型对比研究，并结合基于主成分分析的统计分析、降维可视化和稀土元素分离度评分体系定量评估进行综合研究.结果显示支持向量机构建的判别模型准确率与稳定性均显著优于随机森林，可以更加有效地判别这三种成因类型萤石矿，并识别出可用于区分三种成因类型萤石矿的关键元素精炼候选池，构建出了新的Tb/Dy-Sm/Yb、δCe-Sm/Yb、δCe-Sm/Tm、δEu-Sm/Lu判别图，后续实验也验证了该方法可以有效区分隐爆角砾岩型、与岩浆热液相关的热液充填型和与盆地卤水相关热液填充型萤石矿.
Abstract:The newly identified cryptic explosive breccia-type fluorite deposits in the western Guizhou fluorite ore concentration area possess significant prospecting potential. However, the brecciated textures, hydrothermal alteration, and other characteristics of this type of fluorite deposit are easily confused with those of other hydrothermal breccia-type deposits or intensely structurally altered vein-type deposits. Therefore, accurately distinguishing between cryptic explosive breccia-type fluorite deposits and basin brine-related hydrothermal filling-type fluorite deposits in the study area is one of the key scientific challenges for achieving breakthroughs in fluorite prospecting in Guizhou Province. This paper conducts a comparative study of Support Vector Machine (SVM) and Random Forest machine learning classification models using systematically collected rare earth element (REE) data from three genetic types of fluorite deposits: cryptic explosive breccia-type, magmatic hydrothermal-related filling-type, and basin brine-related hydrothermal filling-type, which is combined with comprehensive analysis, including statistical analysis based on Principal Component Analysis (PCA), dimensionality reduction visualization, and quantitative evaluation using an REE separation scoring system. The results indicate that the discriminant model constructed by SVM exhibits significantly higher accuracy and stability compared to Random Forest, enabling more effective discrimination among these three genetic types of fluorite deposits. Furthermore, it identifies a refined candidate pool of key elements that can be used to distinguish them. Newly constructed discriminant diagrams (Tb/Dy vs Sm/Yb, δCe vs Sm/Yb, δCe vs Sm/Tm, δEu vs Sm/Lu) have been developed, which effectively differentiate among cryptic explosive breccia-type, magmatic hydrothermal-related hydrothermal filling-type, and basin brine-related hydrothermal filling-type fluorite deposits.
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图 1 贵州省主要成矿带与萤石矿分布图（据周琦等, 2025修改）
1.三级成矿单元；2.四级成矿单元；3.四级成矿带亚带；4.四川盆地Fe-Cu-Au‒石油‒天然气‒石膏‒钙芒硝‒石盐‒煤和煤层气成矿区；5.鄂渝湘黔前陆褶断冲断带西段Pb-Zn-Cu-Ag-Fe-Mn-Hg-Sb‒磷‒铝土矿‒硫铁矿‒煤‒煤层气‒页岩气成矿带；6.江南加里东造山带Sn-W-Au-Sb-Fe-Mn-Cu‒重晶石‒滑石成矿带；7.南盘江‒右江印支造山带Au-Sb-Hg-Ag-Mn-水晶‒石膏成矿区；8.萤石矿
Fig. 1. Simplified map of major metallogenic belts and fluorite deposits in Guizhou Province (modified by Zhou et al., 2025)
图 2 萤石矿床成因类型判别综合研究技术路线
Fig. 2. Technical framework for the comprehensive study of genetic type discrimination in fluorite deposits
图 9 稀土元素中位数球粒陨石标准化蛛网图
球粒陨石标准化值据Sun and McDonough（1989）
Fig. 9. Median chondrite-normalized spider diagram of rare earth elements
图 11 基于分离度定量筛选的萤石矿床判别图解
Fig. 11. Discriminant diagrams for fluorite deposits based on quantitative separation efficiency screening
表 1 萤石矿数据来源及地质特征
Table 1. Data sources and geological characteristics of fluorite deposits
成因类型 矿床名称 地理位置 赋矿围岩/时代 数据来源文献 隐爆角砾岩型 大厂锑矿 贵州省晴隆县 大厂层（P₂） 王均, 2019 老厂 滇黔交界地区 上、下二叠统间的蚀变硅化岩 蔡华君等, 1996 晴隆锑矿 黔西南晴隆县 二叠系 彭建堂等, 2002 冬瓜林萤石矿 贵州省晴隆县 二叠系中统茅口组‒上统龙潭组 徐阳东等，2023 高岭萤石矿床 贵州省晴隆县 震旦系‒寒武系碳酸盐岩 金少荣等, 2018 与盆地卤水相关热液填充型 双河重晶石‒萤石矿床 黔东北地区 下奥陶统碳酸盐岩 李敏，2022 沿河大竹园萤石矿床 黔东北地区 下奥陶统桐梓组及红花园组碳酸盐岩 张遵遵等，2018 务川双河、鹿坪萤石矿床 黔东北地区 奥陶系桐梓组和红花园组碳酸盐岩 陈登等，2023 金亮萤石矿床 黔东北地区 奥陶系下统桐梓组和红花园组灰岩 郭宇等，2023 西克尔萤石矿 塔里木盆地西克尔地区 中下奥陶统鹰山组 饶红娟等，2010 麦子坪萤石矿 四川马边 寒武系纽芬兰统筇竹寺组 张航飞等，2025 冯家重晶石‒萤石矿床 重庆东南部彭水地区 下奥陶统红花园组 邹灏等，2016 下陈萤石矿 浙江天台盆地 朝川组(K₁c) 邹灏等，2014 足补萤石矿 四川省西南部 寒武系龙王庙组 Yu et al.，2022 姚家塔萤石矿 皖南郎溪县 早白垩世姚村岩体 帅秋燕等，2025 双河重晶石‒萤石矿床 中国扬子地块东南部 早奥陶世红花园组 Zou et al.，2022 三叉口萤石矿 巴楚三叉口 中奥陶统 张兴阳等，2006 与岩浆热液相关的热液充填型 张厝萤石矿床 福建省邵武市 晚侏罗世正长花岗岩 周博文, 2023 满提萤石矿 内蒙古四子王旗北部 下二叠统大石寨组 白彦, 2020 宝林萤石矿 内蒙古赤峰市林西县 黑云母二长花岗岩 王凯, 2022 马岱沟萤石矿 内蒙古林西县 花岗岩 王凯, 2022 俄力木台萤石矿 内蒙古林西县 火山岩 王凯, 2022 两家营子萤石矿 内蒙古林西县 浅变质碎屑岩 王凯, 2022 曹家屯萤石矿 内蒙古林西县 浅变质碎屑岩 王凯, 2022 八面山萤石矿 浙江八面山 寒武系华严寺组、杨柳岗组 夏学惠等, 2009 
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表 2 各主成分的方差贡献率及关键变量的载荷特征
Table 2. Variance contribution rates of principal components and loading characteristics of key variables
主成分 方差贡献率 累计方差贡献率 主要载荷特征（按绝对值排序） PC1 57.08% 57.08% Tb (0.290)，Dy (0.288)，Ho (0.285)，Sm（0.279），ΣREE (0.277)，Er (0.277) PC2 18.14% 75.22% LREE/HREE (0.433)，Eu（0.399），δEu（0.391），La (0.344), Ce (0.237), Pr (0.221)
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表 3 支持向量机与随机森林模型性能对比（%）
Table 3. Performance comparison of support vector machine and random forest models (%)
模型 准确率 精确率 召回率 F1分数 交叉验证准确率 支持向量机 93.18 93.32 93.18 93.18 87.29±0.078 3 随机森林 88.64 90.37 88.64 87.97 87.33±0.088 0
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表 4 关键判别元素分布特征对比
Table 4. Comparison of distribution characteristics for key discriminant elements
元素 隐爆角砾岩型中位数 与岩浆热液相关的热液充填型中位数 与盆地卤水相关的热液填充型中位数 中位数比值 La 5.712 9 16.387 1 3.871 0 1.00∶2.87∶0.68 Ce 4.294 6 11.819 3 2.592 8 1.00∶2.75∶0.60 Pr 7.286 9 13.114 8 2.549 2 1.00∶1.80∶0.35 Nd 5.683 3 12.816 7 3.397 1 1.00∶2.26∶0.60 Sm 6.410 3 14.871 8 4.794 9 1.00∶2.32∶0.75 Eu 6.394 6 8.843 5 15.729 5 1.00∶1.38∶2.46 Gd 10.193 1 14.980 7 4.373 9 1.00∶1.47∶0.43 Tb 8.839 7 23.839 7 2.215 2 1.00∶2.70∶0.25 Dy 7.850 9 26.583 9 1.754 7 1.00∶3.39∶0.22 Ho 7.451 3 29.526 5 1.392 8 1.00∶3.96∶0.19 Er 6.333 3 27.761 9 1.285 7 1.00∶4.38∶0.20 Tm 4.629 6 32.716 0 1.234 6 1.00∶7.07∶0.27 Yb 3.253 6 32.631 6 0.837 3 1.00∶10.03∶0.26 Lu 2.795 0 28.260 9 1.242 2 1.00∶10.11∶0.44 Y 210.625 0 350.480 8 40.360 6 1.00∶1.66∶0.19 注：中位数比值为：隐爆角砾岩型∶与岩浆热液相关的热液充填型∶与盆地卤水相关的热液填充型.
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表 5 比值投图分离度评分排名（前9名）
Table 5. Ranking of separation scores from ratio plots (top 9)
排名 比值组合 分离度评分 1 Tb/Dy vs Sm/Yb 2.974 057 2 δCe vs Sm/Yb 2.643 964 3 δCe vs Sm/Tm 2.214 603 4 δEu vs Sm/Lu 1.921 437 5 δEu vs Sm/Tm 1.826 782 6 LREE/HREE vs Sm/Lu 1.626 312 7 LREE/HREE vs Tb/Yb 1.393 709 8 ΣREE vs Tb/Yb 1.374 621 9 ΣREE vs La/Yb 1.332 607
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