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地质力学学报:2021,27(3):339-349
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一种基于机器学习算法的岩性填图方法
(1.自然资源部古地磁与古构造重建重点实验室, 北京 100081;2.中国地质科学院地质力学研究所, 北京 100081;3.中国地质大学(北京), 北京 100083;4.中国地质科学院矿产资源研究所, 北京 100037)
A machine learning-based lithologic mapping method
(1.Key Laboratory of Paleomagnetism and Tectonic Reconstruction of Natural Resources, Beijing 100081, China;2.Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, China;3.China University of Geosciences, Beijing 100083, China;4.Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China)
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投稿时间:2020-11-09    修订日期:2021-01-10
中文摘要: 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.
文章编号:     中图分类号:P628    文献标志码:
基金项目:国家自然科学基金项目(41822206,41772353)
引用文本:
冀全伟,王文磊,刘治博,等,2021.一种基于机器学习算法的岩性填图方法[J].地质力学学报,27(3):339-349.DOI:10.12090/j.issn.1006-6616.2021.27.03.031
JI Quanwei,WANG Wenlei,LIU Zhibo,et al,2021.A machine learning-based lithologic mapping method[J].Journal of Geomechanics,27(3):339-349.DOI:10.12090/j.issn.1006-6616.2021.27.03.031

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