Affiliation:
1. Department of Information and Computational Sciences, School of Mathematical Sciences, Peking University, Beijing 100871, China
Abstract
Seismic fault interpretation holds great significance in the fields of geophysics and geology. However, conventional methods of seismic fault recognition encounter various issues. For example, models trained on synthetic data often exhibit inadequate generalization when applied to field seismic data, and supervised learning is heavily dependent on the quantity and quality of annotated data, being susceptible to the subjectivity of interpreters. To address these challenges, we propose applying self-supervised pre-training methods to seismic fault recognition, exploring the transfer of 3D Transformer-based backbone networks and different pre-training methods on fault recognition tasks, thereby enabling the model to learn more powerful feature representations from extensive unlabeled datasets. Additionally, we propose an innovative pre-training strategy for the entire segmentation network based on the characteristics of seismic data and introduce a multi-scale decoding and fusion module that significantly improves recognition accuracy. Specifically, during the pre-training stage, we compare various self-supervision methods, like MAE, SimMIM, SimCLR, and a joint self-supervised learning approach. We adopt multi-scale decoding step-by-step fitting expansion targets during the fine-tuning stage. Ultimately merging features to refine fault edges, the model displays superior adaptability when handling narrow, elongated, and unevenly distributed fault annotations. Experiments demonstrate that our proposed method achieves state-of-the-art performance on Thebe, the currently largest publicly annotated dataset in this field.
Funder
National Key Research and Development Program of China
Reference48 articles.
1. Fault interpretation in seismic reflection data: An experiment analysing the impact of conceptual model anchoring and vertical exaggeration;Alcalde;Solid Earth,2019
2. Three-dimensional seismic interpretation and fault sealing investigations, Nun River Field, Nigeria;Bouvier;AAPG Bull.,1989
3. Building realistic structure models to train convolutional neural networks for seismic structural interpretation;Wu;Geophysics,2020
4. Fossen, H. (2010). Structural Geology, Cambridge University Press.
5. Posamentier, H.W., Davies, R.J., Cartwright, J.A., and Wood, L. (2007). Seismic Geomorphology—An Overview, Geological Society. Special Publications.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献