Frequency adaptive fault detection by feature pyramid network with wavelet transform

Fault detection is a key step in seismic structure interpretation. Current research has achieved good results in fault detection by using synthetic training data to train deep-learning models. However, there is an inevitable difference in frequency bandwidth between synthetic training data and real seismic data, which makes it difficult for deep-learning models to obtain ideal fault detection results on real seismic data. To solve this problem, the feature pyramid network (FPN) is introduced to obtain multiscale deep-learning features, which can reduce the impact of seismic data frequency bandwidth differences on fault detection. Then, we apply the multiscale wavelet transform to extract multiscale frequency spectral features of the seismic data and combine them with the multiscale deep-learning features through concatenation operation. Furthermore, the seismic data is decomposed into signals with different frequency bands through the wavelet transform, and we use the energy of these signals as the network weights of multiscale mixed features to further improve the frequency adaptability of our method. Based on these works, we not only improve the fault detection effect in a specific work area but also improve the generalization ability of the deep-learning model in different work areas, thus further promoting the application of deep learning in actual production. Compared with the fault detection results by the traditional deep-learning model U-Net and the traditional FPN on multiple real seismic data and synthetic seismic data, experimental results demonstrate the effectiveness of our method.