Automatic Identification of Seismic Faults via the Integration of ResNet-50 Residual Blocks and Convolutional Attention Modules

Fault identification is an important aspect of seismic data interpretation and a key step in structural interpretation. Traditional fault identification involves manual marking by geological interpreters, which is not only time-consuming and inefficient but also prone to human error. A deep learning-based fault identification method, which uses an attention mechanism to focus on target features, is proposed to address the aforementioned issues and increase the accuracy of fault identification. A convolutional block attention module (CBAM) is used in the decoding layer of the U-Net network, and a ResNet-50 residual block is utilized in the encoding layer. Thus, a fault identification method based on convolutional neural networks, which is referred to as Res-CBAM-UNet, is established. Data augmentation on synthetic seismic data and their corresponding fault labels was performed, and the model was trained using the newly generated training dataset as the input to enhance the generalization capability of the network model. Subsequently, the model was compared and analyzed using CBAM-UNet, ResNet34-UNet, and ResNet50-UNet networks and tested using seismic data from actual working areas. Results indicate that the designed Res-CBAM-UNet network has good fault identification performance, with high continuity in fault identification and high computational efficiency.