Structural Similarity-Aware Cross-domain Transformer for Improved Seismic Fault Detection

Abstract

Seismic Fault Detection is a crucial aspect of oil exploration. While traditional deep learning methods struggle to handle complex seismic data patterns, training a deep learning model solely on synthetic seismic data may not yield satisfactory results. This research paper, involves utilizing a pre-trained vision-based transformer to extract relevant features from seismic data. By leveraging the knowledge learned from a different but related task, the model can capture general fault patterns in field data. In this framework, a portion of the pre-trained model architecture is employed and trained using a Structural Similarity-based loss function to learn fault-related features. This allows the model to adapt to the faulted structures of a geological dataset and improve fault detection performance in field data applications. In comparison to the state-of-the-art method, the proposed method yields improved results on real field dataset.