Fault Identification Based on W-Net in 3-D Seismic Images

The accurate identification and characterization of faults in seismic data is a key step for geological structure interpretation, favorable traps and reservoirs, and recovery of hydrocarbons. Artificial fault marking is not only time-consuming and laborious but also influenced by seismic quality and interpretation experience. The current commonly used U-Net models have satisfactory performance in assisting fault interpretation but have limited ability to fine segmentation and recover lost feature information C due to the lack of representative datasets and limitations of network architecture. This letter alleviates this problem by optimizing network architecture, we propose a concise W-Net architecture to identify fault information by designing two expansive paths to obtain rich context information from different scales and enhance the learning ability of the neural network. In the skip connection part, we add double path semantic segmentation features to effectively transfer information, improve positioning accuracy, optimize memory usage, extract important fault features, and suppress irrelevant information. The synthetic results show that the convergence rate of accuracy and loss of W-Net is faster than that of U-Net in the early stages (epoch \lt =10 ). The geologic detailed information of fault identification with W-Net in 3-D actual seismic data is more abundant and continuous, which indicates that W-Net can serve as an improved tool to automatically characterize the distribution of geologic discontinuity in seismic images.