Completing Any Borehole Images

Borehole images contain the physical information and chemical properties of geological formations, which are crucial for high-resolution interpretation of subsurface stratigraphic and structural features and geological modeling of the subsurface. However, due to the special design of borehole tools and variations in borehole diameter, all kinds of borehole images (FMI, Earth-imager, OMRI, and OBMI) obtained from scanning the borehole walls exhibit varying degrees of data missing, with OBMI data missing up to 70%. We propose a deep-learning approach with a hybrid CNN and Transformer architecture to fill in the gaps in borehole images, addressing the challenges of missing training labels and filling large-scale gaps. To solve the challenge of missing labels of complete borehole images, our deep-learning model is pretrained on a vast collection of complete natural and seismic images and then fine-tuned with a partial loss function on incomplete borehole images. A multistage completion strategy is further introduced into the inference stage to enhance the continuity and textural features of the completed areas. In addition, by incorporating the circular consistency constraint between the left and right sides of the borehole image, our method can reasonably complete the gaps with highly consistent features on both sides of the image. During the tests on borehole images from multiple wells in different work areas with various geological features, our model is capable of completing any type of borehole image with masks of any size, ultimately yielding complete images free of any artifacts, while also possessing richer and more reasonable textures and semantic information. We have open-sourced the code and the fine-tuned models, which are available at https://github.com/zgyustc/LogMAT/tree/master .