Geological structure recognition model based on improved random forest algorithm

• Published in: Méitàn kēxué jìshù Vol. 51; no. 4; pp. 149 - 156
• Main Authors: Huaixiu WANG, Siyi FENG, Zuiliang LIU
• Format: Journal Article
• Language: Chinese
• Published: Editorial Department of Coal Science and Technology 01.04.2023
• Subjects: geological model; geological structure identification; random forest algorithm; seismic attribute fusion
• ISSN: 0253-2336
• DOI: 10.13199/j.cnki.cst.2021-0754

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 is proposed 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 the second 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 the twelve attributes, according to the results, all twelve attribute