期刊论文

Li, Zhiming

英文

GEOPHYSICS

0016-8033

2021

86

1

V41-V54

10.1190/geo2019-0615.1

The prestack data with this research are from the Viking Graben data set, released by Mobil Oil Company. The authors also appreciate the associate editor and four anonymous reviewers for their help improving this paper during revision. 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).

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Seismic data interpolation is an effective way of recovering missing traces and obtaining enough information for subsequent processing. Unlike traditional methods, deep neural network (DNN)-based methods do not need to make assumptions because they can self-learn the relationship between sampled data and complete data using large training data sets and complete the interpolation with a small computational burden. However, current DNN-based approaches only focus on reducing the difference between the recovered and original data during training, which helps to improve the quality of the reconstr...