Using deep learning to automatically and quickly extract faults from seismic images is of practical significance. An improved U-Net algorithm is proposed by reducing convolutional layers, designing skip connections, enforcing deep supervision, and improving the loss function and learning rate to build a new model. In the operation, the feature map parameters in the network are further revised, the number of training iterations is increased, a callback function is added, and the parameter adjustment training consumes less time and space and has higher accuracy. Experiments on real public datasets show that the improved network can limit the time required to extract a 128 × 128 × 128 three-dimensional image within 200 ms, which not only requires less time and computing power than existing methods but also has an extraction accuracy as high as 97.6%.
|Journal||Journal of Electronic Imaging|
|Early online date||3 Aug 2022|
|Publication status||Published - 3 Aug 2022|
Bibliographical noteFunding Information:
The authors would like to thank the anonymous reviewers for their valuable suggestions. The work described in this paper was supported by the Katie Shu Sui Pui Charitable Trust—Academic Publication Fellowship (Grant No. KSPF2019-02), Hong Kong Metropolitan University.
© 2022 SPIE and IS&T.
- deep learning
- deep supervision
- fault extraction
- seismic images
- three-dimensional U-Net