Automatic identification method of seismic fault based on LLE and SVM

Abstract

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 increase the accuracy of interpretation, machine learning can integrate the existing geological data, the knowledge and experience of 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 of algorithms. 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 attribute data are dimensionally reduced by LLE and principal component analysis (PCA). The intersection diagram shows that the LLE algorithm has a better dimensionality reduction effect for nonlinear data volumes. The SVM, PCA-SVM and LLE-SVM recognition models of fault were trained by using 11854 known structural information data points revealed by six roadways and five drilled wells in the Xishangzhuang Coalfield. Accuracy rate A, recall rate R, precision rate P and F value were used as the measurement standards to compare the prediction and classification performance of each model in the 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 by combining the actual disclosure and artificial interpretation results. The comprehensive results show that the fault identification method based on LLE and SVM can effectively highlight the fault response characteristics while removing redundant information, reduce the influence of subjective factors, and improve the efficiency of fault interpretation.

Keywords

• fault identification
• seismic attributes optimization
• 3d coalfield seismic
• locally linear embedding
• support vector machine