A New Fault Recognition Method Based on Empirical Mode Decomposition and Texture Attributes

Abstract

Small faults developed in coal seams are one of the major causes of coal mine accidents. Accurately predicting small faults in coal fields is an urgent requirement for efficient and safe production in coal mines. This article proposes a new small fault identification method that combines the empirical mode decomposition method and the seismic texture attribute extraction method to address the problem of large errors caused by noise in the results of small fault prediction. Firstly, the basic principles of the empirical mode method and the texture attribute method were studied, and then the fault recognition ability of this method was tested and analysed based on a small fault seismic forward modelling. Meanwhile, empirical mode decomposition is performed on actual seismic data to identify small faults by using texture attributes and by adding noise to the seismic record; this article compared the seismic record of texture properties in the presence and absence of noise. The results indicate that the texture attribute method can predict small faults well, but this method is easily disturbed by noise. The empirical mode decomposition method used in this paper can remove noise interference and highlight characteristics of the texture attribute. Therefore, the small fault prediction method that combines empirical mode decomposition with texture attributes can effectively identify small faults and play an important geological guarantee role in ensuring safe and efficient production in coal mines.