Intelligent Detection of Small Faults Using a Support Vector Machine

The small fault with a vertical displacement (or drop) of 2–5 m has now become an important factor affecting the production efficiency and safety of coal mines. When the 3D seismic data contain noise, it is easy to cause large errors in the prediction results of small faults. This paper proposes an intelligent small fault identification method combining variable mode decomposition (VMD) and a support vector machine (SVM). A fault forward model is established to analyze the response characteristics of different seismic attributes under the condition of random noise. The results show that VMD can effectively realize the attenuation of random noise and the seismic attributes extracted on this basis have a good correlation with the small fault. Through the analysis of the SVM algorithm and the fault forward model, it is proved that it is feasible to realize intelligent predictions of small faults by using seismic attributes as the input of a SVM. The fault prediction method using a SVM that is proposed in this paper has higher accuracy than the principal component analysis method, as the prediction results have important guiding significance and reference value for later coal mining. Therefore, the method presented in this paper can be used as a new intelligent method for small fault identification in coal fields.