Depthwise separable convolution Unet for 3D seismic data interpolation

Jin, Zhenhui1; Li, Xinze1; Yang, Hui2; Wu, Bangyu1; Zhu, Xu1

2023-01-11

10.3389/feart.2022.1005505

FRONTIERS IN EARTH SCIENCE   影响因子和分区

2296-6463

In seismic exploration, dense and evenly spatial sampled seismic traces are crucial for successful implementation of most seismic data processing and interpretation algorithms. Recently, numerous seismic data reconstruction approaches based on deep learning have been presented. High dimension-based methods have the benefit of making full use of seismic signal at different perspectives. However, with the transformation of data dimension from low to high, the parameter capacity and computation cost of training deep neural network increase significantly. In this paper, we introduce depthwise separable convolution instead of standard convolution to reduce the operation cost of Unet for 3D seismic data missing trace interpolation. The structural similarity (SSIM), L-1 hybrid loss function, and switchable normalization further improve the reconstruction performance of the network. The comparative experiments on the synthetic and field seismic data show that depthwise separable convolution can effectively reduce the number of network parameters and computation intensity with the interpolation results comparable to the standard convolution results.

3D seismic data interpolation Unet depthwise separable convolution hybrid loss switchable normalization seismic imaging data analysis

SCI

英语

其他

National Natural Science Foundation of China[41974122] ; Guangdong Provincial Key Laboratory of Geophysical High-resolution Imaging Technology[2022B1212010002]

Geology

Geosciences, Multidisciplinary

WOS:000922206000001

FRONTIERS MEDIA SA

Web of Science

1.Xi An Jiao Tong Univ, Sch Math & Stat, Xian, Shaanxi, Peoples R China
2.Southern Univ Sci & Technol, Guangdong Prov Key Lab Geophys High Resolut Imagin, Shenzhen, Guangdong, Peoples R China

Jin, Zhenhui,Li, Xinze,Yang, Hui,et al. Depthwise separable convolution Unet for 3D seismic data interpolation[J]. FRONTIERS IN EARTH SCIENCE,2023,10.

Jin, Zhenhui,Li, Xinze,Yang, Hui,Wu, Bangyu,&Zhu, Xu.(2023).Depthwise separable convolution Unet for 3D seismic data interpolation.FRONTIERS IN EARTH SCIENCE,10.

Jin, Zhenhui,et al."Depthwise separable convolution Unet for 3D seismic data interpolation".FRONTIERS IN EARTH SCIENCE 10(2023).