The structural interpretation of seismic data plays an important role in the safe and efficient mining of mines. Seismic attributes are often used for structural interpretation, but the information in seismic data cannot be fully utilized by single attribute and traditional attribute superposition methods. In this paper, the method based on principal component analysis and competitive neural network was used to fuse and cluster various seismic attributes to realize the recognition of complex structures. Firstly, the seismic attributes with strong correlation with the structure were extracted, and then the principal component analysis method was used to obtain the principal component components with the largest contribution rate; finally, the competitive neural network in unsupervised learning method was used to realize the fusion and clustering of the selected principal component components. The seismic data of 1200 exploration area in Xingdong mining area was taken as the research object, the multi-attribute fusion clustering method based on principal component analysis and competitive neural network was applied. The final clustering image can clearly correspond to the actual geological anomalies and effectively distinguish the structural distribution characteristics, which provided a feasible method for multi-attribute structure recognition.
姚江凯，刘家豪. 基于地震属性的机器学习在构造识别中的应用[J]. 煤炭与化工, 2020, 43(12): 67-71.
Yao Jiangkai, Liu Jiahao. Application of machine learning based on seismic attributes in structural recognition. CCI, 2020, 43(12): 67-71.
