Segmentation of borehole acoustic reflection image using feature pyramid network and transfer learning

Abstract

Borehole acoustic reflection imaging technology is widely utilized for high-resolution detection of subsurface geological structures. However, the presence of significant noise in migration images poses a challenge for accurate identification of useful reflectors. In this paper, we propose a deep learning-based image segmentation method to address this challenge. Our approach employs a Feature Pyramid Network with an EfficientNet-b4 encoder, which is trained on a large simulation dataset. To handle the class imbalance issue caused by the small proportion of fractures, we employ the Dice loss function. Additionally, the Lovász-hinge loss function is utilized to enhance network training and achieve higher IoU metric values. To extract high-level semantic features, the trained network is fine-tuned on a hand-labeled field dataset using transfer learning. The results obtained from processing field migration images demonstrate the effectiveness and robustness of our proposed methods.