Rapid fault extraction from seismic images via deep learning

Dingkun ZHU, Chengyu ZHENG, Weiming WANG, Haoran XIE, Kwok Shing CHENG, Fu Lee WANG, Mingqiang WEI

Research output: Contribution to journalArticlespeer-review

1 Citation (Scopus)

Abstract

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%. Copyright © 2022 SPIE and IS&T.

Original languageEnglish
Article number051423
JournalJournal of Electronic Imaging
Volume31
Issue number5
Early online dateAug 2022
DOIs
Publication statusPublished - Sept 2022

Citation

Zhu, D., Zheng, C., Wang, W., Xie, H., Cheng, G., Wang, F. L., & Wei, M. (2022). Rapid fault extraction from seismic images via deep learning. Journal of Electronic Imaging, 31(5). Retrieved from https://doi.org/10.1117/1.JEI.31.5.051423

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