The development of remaining oil plays an important role in increasing late production, and low-grade faults seriously impact residual oil exploration and development. Low-grade faults have a small fault displacement, brief extension, and strong concealment, which hinders their prediction using the typical semantic segmentation network. To intelligently identify low-grade faults, we designed a codec target edge detection technique. For the network to fully learn the low-grade faults information, we constructed the encoder using dilated convolution. Next, we introduced an attention mechanism to the decoder to improve the capture of location information from a shallow network and semantic information from a deep network. Finally, the multiscale fusion decoder outputs fault information of different scales, which further improves the identification accuracy of low-grade faults. The training model is applied to simulated data and actual seismic data through ablation experiments. The results show that this method can effectively identify low-grade faults and overcomes problems such as blurred fault cross-location, thicker edge contour lines, lower detection accuracy, and less training data. Compared with conventional holistically nested edge detection (HED) and semantic segmentation (UNet), fault misidentification is reduced, fault continuity is increased, and fault accuracy is improved, providing technical support for the exploration and development of remaining oil and increasing the recovery rate of old oil fields.
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