A machine learning-based lithologic mapping method
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摘要: 通过野外地质调查与机器学习方法的有机融合，提出了一种基于梯度提升决策树算法的岩性单元填图方法。研究以多龙矿集区为模型试验区，选择1∶5万勘查地球化学数据为基础预测数据，以1∶5万区域地质图为参考，进行基于梯度提升决策树算法的岩性预测填图模型试验。首先选择研究区内小范围空白区开展野外填图，建立原始数据集并初步构建岩性单元与预测数据对应关系；其次利用机器学习方法对预测数据进行多分类任务，进而开展目标填图区预测填图工作；最后通过概率选区选定概率较低目标区，开展进一步的小范围野外地质调查填图，对原始数据和知识库进行补充，迭代循环以上流程，直至预测填图达到要求。试验显示，随着迭代次数的增加，模型精度不断提高，并在7次迭代后模型准确率达到87%。该方法强调在实际应用中野外地质调查与基于机器学习预测填图的深度融合，以及野外实地工作在整个流程中的重要性和不可或缺性；同时能够充分挖掘已有数据资料的有用信息，用于辅助修正已有岩性填图内容，或根据已勘探区资料对邻近的未勘探区进行岩性分类，有效减少野外填图工作量，是对岩性填图方法、地质单元定量预测识别的有益探索，为区域地质填图工作提供了新的参考思路和辅助手段。Abstract: In this study, a gradient boosting decision tree (GBDT)-based lithologic mapping method constituted by field survey and machine learning is introduced. The Duolong mineral district, Tibet, China is currently chosen for model test. During the practical application, geochemical data at a 1:50000 scale is analyzed to identify lithologic units, while a geological map at the same scale currently provides lithologic units identified by field survey. Lithologic units within a small area are firstly collected from the geological map. Correspondence between geochemical data and lithologic units within the small area can consequently be marked, by which the GBDT method is applied to reclassify the geochemical data and further predict lithologic units in the Duolong district. Transforming the result to a probability distribution, areas with low probability can be identified, and further investigation will be implemented to update geological knowledge and correspondence between geochemical and lithologic units. Iteration of the process will lead a reasonable lithologic mapping result. It is shown that the model accuracy increases with iteration growing, and reaches 87% after 7 iterations. The currently proposed method highlights deep integration of field survey and machine learning algorithm, and emphasizes importance of field work in the whole modeling process. Useful geo-information can be deeply mined from existing data and further updates former geological understandings. Meanwhile, lithologic units within un-explored areas can be identified based on the knowledge in explored areas. The GBDT-based method which effectively reduces field work is a meaningful exploration in lithologic mapping and will provide a new reference and supplementary way to geological mapping.
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Key words:
- data mining /
- information fusion /
- geologic unit /
- decision tree /
- geological mapping
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图 1 多龙矿集区岩性分布图
Figure 1. Spatial distribution of the lithologic units in the Duolong mineral district, Tibet, China
图 4 概率分布选区示意图
Figure 4. Schematic diagram of probability distribution-based area selection
图 5 多龙矿集区岩石单元预测结果
Figure 5. Prediction results of lithologic units in the Duolong mineral district
表 1 模型迭代性能统计表
Table 1. Performance of model iteration
迭代次数 宏平均精确率 宏平均召回率 宏平均F1分数 准确率 1 0.348 0.193 0.185 0.473 2 0.472 0.362 0.361 0.571 3 0.633 0.472 0.507 0.635 4 0.737 0.600 0.638 0.707 5 0.761 0.683 0.701 0.768 6 0.797 0.715 0.746 0.821 7 0.827 0.765 0.789 0.870 
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表 2 迭代分析结果信息统计表
Table 2. Statistics table of iteration results
迭代次数 面积占比 岩性种类数 1 0.088 13 2 0.171 17 3 0.262 18 4 0.357 19 5 0.445 19 6 0.531 19 7 0.622 19
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表 3 模型分类精度表
Table 3. Table of classification accuracy of the current model
岩性单元 岩性符号 宏平均F1分数 流纹岩 λ53 0.880 第四系残坡积物 Q4 0.926 上第三系康托组棕红色粘土及砂砾石层 N1k 0.794 下白垩统美日切组上段火山角砾岩 K1m3 0.834 下白垩统美日切组中段火山碎屑岩 K1m2 0.810 下白垩统美日切组下段安山玢岩、安山质玄武岩 K1m1 0.843 中侏罗统色哇组二段：变石英砂岩、变长石石英砂岩夹深灰色粉砂质板岩 J2s2 0.909 中侏罗统色哇组一段：变长石石英砂岩砂、砾岩夹深灰色至深黑色变石英粉砂岩 J2s1 0.933 中侏罗统曲色组二段：变长石石英砂岩、粉砂岩、粉砂质板岩、夹硅质岩、灰绿色玄武岩、基性火山熔岩 J2q2 0.870 中侏罗统曲色组一段：深灰色粉砂质板岩夹变长石石英砂岩、灰岩条带及透镜体 J2q1 0.849 不明火山角砾岩铁帽 B53|Fe 0.839 褐红色、褐灰色安山岩 α53 0.739 浅绿灰色辉长岩 ν53 0.841 灰绿色辉绿岩 βμ53 0.683 灰绿色闪长岩、石英闪长岩 δ53 0.935 英安岩 ξ53 0.906 花岗闪长斑岩 γδπ53 0.756 绿帘石化玄武质安山岩 β53 0.882 蚀变体 SB 0.834
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