期刊论文

Liu, Qun(q.liu@cug.edu.cn);Fu, Lihua(lihuafu@cug.edu.cn)

英文

GEOPHYSICS

0016-8033

2021

86

2

V131-V142

10.1190/geo2019-0570.1

The authors would like to thank the associate editor D. Velis and four anonymous reviewers for helping to improve this paper during the revisions. The authors also thank professor S. Fomel for providing shot gathers from the deepwater Gulf of Mexico survey. This research is financially supported by the National Key R&D Program of China (2018YFC1503705), the Science and Technology Research Project of Hubei Provincial Department of Education (B2017597), the Hubei Subsurface Multi-scale Imaging Key Laboratory (China University of Geosciences) (SMIL-2018-06), and the Fundamental Research Funds for the Central Universities (CCNU19TS020).

本校为其他机构

The reconstruction of seismic data with missing traces has been a long-standing issue in seismic data processing. Deep learning (DL) has emerged as a popular tool for seismic interpolation; it learns priors from training data sets of incomplete/complete data pairs. However, these DL methods are restricted to training data because they are supervised. When the features of the testing and training data are different, the recovery performance decreases, which prevents practical application. We have introduced a "deep-seismic-prior-based"approach v...