Year: 2025
Type: article
Abstract: Fault interpretation is a key link in seismic data interpretation. With the continuous increase in seismic data volume in original production, manual fault picking cannot efficiently handle massive seismic data. With the development of artificial intelligence technology, the automatic and rapid picking of faults has become a hot topic in the application of deep learning methods in the field of seismic data interpretation. Therefore, this paper proposes a seismic data fault identification method based on SEU-Net. The SEU-Net network introduces SE blocks on the basis of the original U-Net network, enhancing the weights of effective channels through adaptive feature recalibration, and combines a multi-scale feature fusion strategy to improve the model’s ability to identify fault edges and minor faults. The experimental results show that compared with the original U-Net network, the SEU-Net network exhibits higher fault identification accuracy and robustness both in synthetic seismic data and original work area data. This research provides an efficient and automated solution for fault detection in seismic data and it holds certain theoretical value and practical application potential.
Source: Applied Sciences
DOI: 10.3390/app151810152
Authors: Wenbo Ren, Xuan Chen, Xiansheng Zhu, Dian Bao, Xinming He, Yan Zhao, Ming Zhao
Language: English
Subfield: Geophysics
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4414279999