LightUNetFault3D: A Lightweight U-Net for 3-D Seismic Fault Detection

In seismic data interpretation, accurate delineation of faults is crucial for subsurface hydrocarbon resource exploration and production. Traditional manual interpretation is time-consuming, labor-intensive, and subjective. Deep learning has been widely studied for automatic fault detection in recent years. U-Net and its variants dominate the network structures for their excellent performance in terms of accuracy and generalizability; however, they often require significant computational resources and memory usage. For conventional U-Nets, they usually need large channel number at each layer for diverse feature extraction. As multiple channels may capture similar features, this may lead to feature redundancy and a waste for memory and computing resource. To mitigate this problem, we propose a lightweight 3-D fault detection neural network, LightUNetFault3D, which contains global spatial convolution module (GSCM) and semantic difference module (SDM), which dramatically decreases channel number at both encoding and decoding side of U-Net. In the encoding stage, GSCM is used to improve the ability to capture long-range features by introducing additional global spatial information. In the decoding stage, SDM, containing difference and fusion operations, is used instead of original skip connection. Difference is conducive to extracting boundary features in seismic data, which are closely related to fault detection. Fusion performs weighted summation of differential features from encoder and decoder instead of concatenation. These two modules greatly improve the efficacy of feature extraction for the task of fault detection, and a small number of channels is used in LightUNetFault3D. Consequently, the capacity and floating-point operations per second (FLOPS) of LightUNetFault3D are only 15% and 9% of the baseline FaultSeg3D model. Meanwhile, LightUNetFault3D still achieves continuous fine fault structures on field dataset tests.