The intelligent fault identification method based on multi-source information fusion and deep learning

Faults represent significant geological structures. Conventional fault identification methods pri-marily rely on the linear features of faults, achieved through the interpretation of remote sensing imagery (RSI). To more accurately enhance the morphological features of faults and achieve their rapid, precise, and intelligent identification, this paper employs a multi-source information fusion method. By analyzing and processing RSI, digital elevation model, and geological map data, the spectral, topographic, geomorphic, and structural features of faults are extracted. By training samples and applying fusion algorithms, the spectral, topographic, geomorphic, and structural features are integrated to enhance the morphological features information of faults. Ultimately, intelligent fault identification is realized through deep learning-based image recognition technology. First, 16 influencing factors are selected from the perspectives of spectral, topographic, geomorphic, and structural features. Second, the importance of each influencing factor is predicted using 4 machine learning methods. Finally, fault identification is carried out on the fault identification map, which is fused with multi-source feature information, using the Convolutional Neural Network Model. The study applies the method to the southern part of Jinzhai County, Lu'an City. The results indicate that among the machine learning methods, the classification and regression Trees model achieved an accuracy of 0.993, true positive rate of 0.988, F1-score of 0.994. Topographic position index(TPI), Valley line (VL), Surface cutting depth (SCD), and RSI all show high importance across the four machine learning models, indicating their crucial role in fault identification. For the Convolutional Neural Network model-based method, the Validation Accuracy(Val_Accuracy) was 0.990, F1-score was 0.736, and Validation Loss(Val_Loss) was 0.025, suggesting that this method can accurately identify faults in the study area.