College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing
State Key Laboratoryof Coal Resource and Safety Mining, China University of Mining and Technology-Beijing
Huaneng Coal Technology ResearchCo., Ltd.

The fault interpretation of traditional seismic data mainly relies on the knowledge and experience of the interpreter,which has the problems of heavy workload and low efficiency. In order to construct high-quality data sets and increasethe accuracy of interpretation, machine learning can integrate the existing geological data, the knowledge and experienceof the interpreter. A fault recognition method based on Local Linear Embedding (LLE) and Support Vector Machine(SVM) algorithms is constructed to improve the accuracy of fault interpretation by machine learning methods. First,the basic principles of LLE and SVM algorithms are introduced to illustrate the calculation process and main parameters ofalgorithms. Then a fault forward modeling model is established to analyze the fault response characteristics of different attributes. Aiming at the information redundancy among various seismic attributes in the training data set, the seismic attributedata are dimensionally reduced by LLE and principal component analysis (PCA). The intersection diagram shows thatthe LLE algorithm has a better dimensionality reduction effect for nonlinear data volumes. The SVM, PCA-SVM and LLESVMrecognition models of fault were trained by using 11854 known structural information data points revealed by sixroadways and five drilled wells in the Xishangzhuang Coalfield. Accuracy rate A, recall rate R, precision rate P and Fvalue were used as the measurement standards to compare the prediction and classification performance of each model inthe research area. Among them, the LLE-SVM model has the best overall performance, with a precision rate of 94.4%,much higher than those of other models. Finally, the whole research area is predicted by using the models, and analyzed bycombining the actual disclosure and artificial interpretation results. The comprehensive results show that the fault identificationmethod based on LLE and SVM can effectively highlight the fault response characteristics while removing redundantinformation, reduce the influence of subjective factors, and improve the efficiency of fault interpretation.

国家重点研发计划资助项目(2018YFC0807803)

邹冠贵，丁建宇，任珂，等. 基于LLE和SVM的地震断层自动识别方法[J]. 煤炭学报，2023，48(4)：1634−1644

ZOU Guangui，DING Jianyu，REN Ke，et al. Automatic identification method of seismic fault based on LLE and SVM[J]. Journal of China Coal Society，2023，48(4)：1634−1644