Name
Abstract | ||
|---|---|---|
Automatic identification of faults on seismic structural images is a challenging yet crucial task in quantitative seismic interpretation. Human picking or attribute-based fault detection methods may misidentify faults on noisy, complex seismic images. We develop a new automatic fault detection method using a nested residual U-shaped convolutional neural network. Each of the encoders and decoders in this neural network is a residual U-Net, leading to a nested architecture. The final fault map results from the fusion of three fault maps with low, medium, and high fault resolutions. We demonstrate the excellent fault-detection capability of our nested neural network using a series of synthetic and field seismic images. We find that our approach produces clearer and more interpretable fault maps than the current state-of-the-art U-Net fault detection method, particularly on noisy seismic images. Our new automatic fault detection method can facilitate reliable quantitative seismic interpretation on field seismic images. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TGRS.2021.3073840 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING |
Keywords | DocType | Volume |
Fault detection, Decoding, Spatial resolution, Computer architecture, Noise measurement, Neural networks, Geology, Convolutional neural network (CNN), deep learning, fault detection, nested residual U-Net (NRU), NRU-Net, seismic image, supervised machine learning, U-Net | Journal | 60 |
ISSN | Citations | PageRank |
0196-2892 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kai Gao | 1 | 78 | 39.46 |
| Lianjie Huang | 2 | 0 | 0.34 |
| Yingcai Zheng | 3 | 0 | 0.34 |