Tensor Super-resolution for Seismic Data
Songjie Liao, Xiao-Yang Liu, Feng Qian, Miao Yin, Guangmin Hu
Abstract
In this paper, we propose a novel method for generating high-granularity three-dimensional (3D) seismic data from low-granularity data based on tensor sparse coding, which jointly trains a high-granularity dictionary and a low-granularity dictionary. First, considering the high-dimensional properties of seismic data, we introduce tensor sparse coding to seismic data interpolation. Second, we propose that the dictionary pairs trained by low-granularity seismic data and high-granularity seismic data have the same sparse representation, which are used to recover high-granularity data with the high-granularity dictionary. Finally, experiments on the seismic data of an actual field show that the proposed method effectively perform seismic trace interpolation and can improve the resolution of seismic data imaging.
BibTeX
@inproceedings{icassp2019_tensorsuperresol,
title = {Tensor Super-resolution for Seismic Data},
author = {Songjie Liao and Xiao-Yang Liu and Feng Qian and Miao Yin and Guangmin Hu},
booktitle = {ICASSP 2019},
year = {2019}
}