Fault-Seg-Net: A method for seismic fault segmentation based on multi-scale feature fusion with imbalanced classification

Abstract

Fault identification has important geological significance and practical production value. Due to the effects of earth filtering and environmental noise, it is difficult to identify minor faults, and manual fault identification is inefficient. In this study, an end-to-end deep learning semantic segmentation network Fault-Seg-Net is proposed to identify fault on seismic images, which simultaneously learns global semantic features and local detailed features. In Fault-Seg-Net, a multi-scale residual module is designed to expand the receptive field to mine fine-grained fault features from the low-dimensional feature space. Fault-Seg-Attention module is designed to model long-distance dependencies of pixel spatial location to compensate for the spatial continuity loss. In addition, a compound loss is used to guide the model training to handle imbalanced seismic image segmentation tasks. Experimental results on synthetic datasets have verified that Fault-Seg-Net can achieve high Precision (88.6%), Recall (89.2%), Dice (88.8%) and mIoU (81.5%) simultaneously, which is significantly better than traditional image processing methods and deep learning semantic segmentation networks. Experimental results on real large-scale field datasets have verified that Fault-Seg-Net has important practical value and strong robustness. This study provides an effective solution for intelligent seismic fault identification under complex geological environment.