Automatic fault interpretation method embedded with clustering task in 3D-UNet3+

Abstract

To enhance the efficiency of subsurface data analysis, it is crucial to conduct precise interpretation of faults. Current research mainly focuses on the binary segmentation of faults, which often fails to accurately capture the intricate relationships between different faults. Therefore, this paper further carries out instance segmentation on the basis of binary segmentation of faults, focusing on how to effectively segment fault probability volume. To fulfill the data diversity of deep learning, we have created a labeled fault dataset containing 200 training sets and 20 validation sets based on synthetic data. Afterwards, we develop a 3D-UNet3+ network that fully integrates full-scale information, combined with mean shift clustering technology, to achieve fault instance segmentation. To guarantee precise differentiation among different fault instances, we select discriminative loss as the loss function for training. Extensively tested on synthetic and field data, our algorithm can complete the prediction of new data within tens of seconds and demonstrates excellent segmentation performance. In comparison to prevalent methodologies, our method not only improves segmentation precision but also significantly reduces the number of parameters, offering an innovative and more efficacious resolution for automatic fault interpretation.