Fracture Extraction From Logging Image Using a Dual Encoder-Decoder Architecture With Swin Transformer

Imaging logging is a method of imaging the physical parameters of the borehole wall or the objects around the borehole according to the observation of the geophysical field in the borehole. Imaging logging data can determine the dip angle and structural characteristics of the formation and observe the geometry and development degree of fractures. The performance of existing target segmentation networks relies on large volumes of data. However, logging images are expensive to acquire, so how to effectively extract fractures from small samples of logging images is an urgent problem to be solved. Therefore, we developed a dual encoder-decoder structure using the Swin Transformer, which uses the self-attention mechanism of a hierarchical Vision Transformer with shifted window to model the remote context information. It can overcome the limitations of most convolutional neural network-based methods that cannot establish long-term dependencies and global contextual connections in convolutional operations. In addition, the shifted window mechanism substantially improves the computational efficiency of the model, and the hierarchical structure allows flexibility in modeling at different scales. At the same time, skip connections are established between adjacent layers of the structure, and the higher-level feature maps are stitched with the lower-level feature maps in channel dimensions, which can obtain more high-resolution detail information of fractures, and thus improve the segmentation accuracy. The experimental results show that the performance is better than the mainstream segmentation networks under small training sets of logging images. The effectiveness of our method reveals that it is practical in fracture extraction of logging images.