3D fault detection method using TransVNet

Introduction In seismic structural interpretation, fault detection plays a crucial role as it serves as the foundation and key step for identifying favorable oil and gas zones. Currently, many re-searchers are utilizing deep learning for automated fault detection. However, the accuracy and continuity of predictions generated by existing convolutional neural networks (CNNs) on real seismic data fail to meet practical production requirements. Methods To address this issue, we integrated the Transformer architecture into the V-Net framework, proposing a fault detection method based on the TransVNet network. This approach utilizes semantic segmentation technology to generate a fault probability volume by assessing the likelihood of each data point in the input dataset being part of a fault. Results For comparison, we referenced the classical U-Net network and the recently proposed TransUNet network, validating the feasibility of our method through theoretical seismic data. Subsequently, we applied the TransVNet network to actual seismic data. Without employing transfer learning, the fault detection results demonstrate that our proposed method exhibits superior fault detection capability, higher prediction accuracy, and better continuity compared to existing approaches. Discussion The method proposed in this article demonstrates that deep learning can be applied to fault detection in complex regions, which can enhance the accuracy and continuity of fault detection.