Paper Info
Title
Seismic Dip Estimation With a Domain Knowledge Constrained Transfer Learning Approach
Abstract
Accurate estimation of volumetric seismic dip is of great significance for subsequent seismic processing and interpretation works. Recently, with the development of deep learning techniques, convolutional networks are also applied for seismic dip estimation. Compared with traditional approaches, estimating dips with convolutional networks is not only more efficient but also shows great promise in accuracy and robustness. However, if we take dips estimated by traditional approaches as labels and train networks on the field seismic data directly, the accuracy and robustness of learned networks are influenced due to the error in dip labels. An alternative solution is synthesizing realistic seismic samples with accurate dip labels. However, we find that due to the differences in seismic responses and structural patterns between the synthetic and field seismic data, networks directly learned from synthetic samples cannot guarantee their generalization on the field seismic data. To overcome these drawbacks, we develop a transfer learning approach for improvement. The proposed approach pretrains the dip estimation network on synthetic seismic samples at first and then transfers it to the targeted field seismic data with a domain knowledge-inspired fine-tuning process. Moreover, the proposed approach also highlights the combination of deep learning techniques and domain knowledge in seismic processingx2014;several subtle realizations, such as knowledge-driven sample augmentation, knowledge constrained loss function, and knowledge motivated transfer learning strategy, are introduced, which greatly enhance the learning of the seismic dip estimation network. The advantages of the proposed approach in accuracy, robustness, and resolution are validated by applying the estimated dips for structural filtering and curvature extraction on the Netherlands F3 and Kerry3D seismic data, which further confirms its practicality in the real-world application. We believe that the proposed approach has provided an effective improved way for further seismic dip estimation practices, and the present domain knowledge constrained deep learning case will also inspire researchers in the same discipline.
Year
DOI
Venue
2022
10.1109/TGRS.2021.3061438
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Keywords
DocType
Volume
Estimation, Transfer learning, Task analysis, Robustness, Deep learning, Azimuth, Tensors, Convolutional neural network, domain knowledge constraint, seismic dip estimation, transfer learning
Journal
60
ISSN
Citations 
PageRank 
0196-2892
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Yile Ao133.19
Wenkai Lu22816.27
Pengcheng Xu301.01
Bowu Jiang401.69