A method for seismic fault identification based on self-training with high-stability pseudo-labels

Abstract

The imaging principle of seismic image is different from that of natural image. There are many problems on seismic images, such as limited resolution, complex reflection feature and strong uncertainty, which leads to significant difference in the research emphases of computer vision on seismic image. In response to the above issues, the semi-supervised semantic segmentation method based on self-training with high-stability pseudo-labels is proposed for seismic fault identification. Specifically, the self-training strategy Fault-Seg-ST is proposed to decouple Teacher–Student model predictions by adding random noise to unlabeled images. The selective retraining strategy Fault-Seg-SST is proposed to evaluate the reliability of pseudo-labels based on the stability of model predictions. The contrastive loss function is proposed to learn pixel-level feature representation with intra class affinity and inter class separability. The Fault-Seg-SST has achieved the optimal segmentation performance on both DeepLabV2 (Dice 86%, mIoU 77%) and UNet with ResNet 18 (Dice 83%, mIoU 76%). Experimental results show that the fault identification method based on self-training with high-stability pseudo-labels demonstrates the superiority of selective retraining strategy, and the fault identification model trained with Fault-Seg-SST strategy can achieve the fine-grained fault segmentation.