School of electrical and information Engineering, Beijing University of Civil Engineering and Architecture
Huayang New Material Technology Group Co., Ltd.

地震属性常常用来进行构造解释以及预测。为克服单一地震属性预测带来的多解性和不确定性的问题,采用地震多属性融合技术对地质构造进行解释以及预测。基于经典的机器学习随机森林算法模型,提出了一种改进的随机森林算法对多种地震属性进行融合分类,将地震多属性融合技术与改进的随机森林算法结合,建立了基于改进随机森林算法的地质构造识别模型。以山西新元煤炭责任有限公司二条带二采区作为研究区域,基于三维地震勘探成果提取到的 12 种地震属性,通过对12 种属性进行属性相关性分析以及特征重要性分析,依据结果保留了全部 12 种属性进行后续的属性融合。利用揭露验证后的地质构造−断层和陷落柱作为样本标签,提出一种改进网格搜索的优化算法,将分类器数目与单棵决策树的最大特征数组成参数对进行网格搜索,基于 Python 语言平台建立算法模型,实验结果表明改进后的算法模型预测准确率达到 97%,经过后续的模型验证,证明了相比于逻辑回归、梯度提升与决策树等几种算法,改进后的随机森林算法能够更加有效地识别地质构造中的断层与陷落柱等异常体,且识别准确率更高,算法适用性更加广泛。

Seismic attributes are often used for structural interpretation and prediction. In order to overcome the problems of multiple solutions and uncertainty caused by single seismic attribute prediction, seismic multi-attribute fusion technology is used to interpret and predict geological structures. Based on the classical machine learning random forest algorithm model, an improved random forest algorithm isproposed to fuse and classify multiple seismic attributes. Combining the seismic multi-attribute fusion technology with the improved random forest algorithm, a geological structure recognition model based on the improved random forest algorithm is established. Taking thesecond mining area of the second belt of Shanxi Xinyuan Coal Co., Ltd. as the research area, based on the twelve seismic attributes extracted from the three-dimensional seismic exploration results, through the attribute correlation analysis and feature importance analysis of thetwelve attributes, according to the results, all twelve attributes are retained for subsequent attribute fusion. Using the exposed and verifiedgeological structure faults and collapse columns as sample labels, an improved grid search optimization algorithm is proposed. The number of classifiers and the maximum feature number of a single decision tree are combined to search the grid. The algorithm model is established based on Python language platform. The experimental results show that the prediction accuracy of the improved algorithm modelreaches 97%, After subsequent model verification, it is proved that compared with several algorithms such as logistic regression, gradientlifting and decision tree, the improved random forest algorithm can more effectively identify abnormal bodies such as faults and collapsecolumns in geological structures, with higher recognition accuracy and wider applicability.

国家重点研发计划支撑资助项目(2018YFC0807806);北京建筑大学研究生创新资助项目

王怀秀, 冯思怡, 刘最亮. 基于改进随机森林算法的地质构造识别模型[J]. 煤炭科学技术, 2023, 51(4): 149-156.

WANG Huaixiu, FENG Siyi, LIU Zuiliang. Geological structure recognition model based on improved random forest algorithm[J]. CoalScience and Technology, 2023, 51(4): 149-156.