Open Access
ARTICLE
Research on Efficient Seismic Data Acquisition Methods Based on Sparsity Constraint
Caifeng Cheng1, 2, Xiang’e Sun1, *, Deshu Lin3, Yiliu Tu4
1 School of Electronics and Information, Yangtze University, Jingzhou, 434023, China.
2 College of Engineering and Technology, Yangtze University, Jingzhou, 434020, China.
3 School of Computer Science, Yangtze University, Jingzhou, 434023, China.
4 Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, Canada.
* Corresponding Author: Xiang’e Sun. Email: .
Computers, Materials & Continua 2020, 64(1), 651-664. https://doi.org/10.32604/cmc.2020.09874
Received 23 January 2020; Accepted 11 April 2020; Issue published 20 May 2020
Abstract
In actual exploration, the demand for 3D seismic data collection is increasing,
and the requirements for data are becoming higher and higher. Accordingly, the collection
cost and data volume also increase. Aiming at this problem, we make use of the nature of
data sparse expression, based on the theory of compressed sensing, to carry out the research
on the efficient collection method of seismic data. It combines the collection of seismic
data and the compression in data processing in practical work, breaking through the
limitation of the traditional sampling frequency, and the sparse characteristics of the
seismic signal are utilized to reconstruct the missing data. We focus on the key elements
of the sampling matrix in the theory of compressed sensing, and study the methods of
seismic data acquisition. According to the conditions that the compressed sensing sampling
matrix needs to meet, we introduce a new random acquisition scheme, which introduces
the widely used Low-density Parity-check (LDPC) sampling matrix in image processing
into seismic exploration acquisition. Firstly, its properties are discussed and its conditions
for satisfying the sampling matrix in compressed sensing are verified. Then the LDPC
sampling method and the conventional data acquisition method are used to synthesize
seismic data reconstruction experiments. The reconstruction results, signal-to-noise ratio
and reconstruction error are compared to verify the seismic data based on sparse
constraints. The LDPC sampling method improves the current seismic data reconstruction
efficiency, reduces the exploration cost and the effectiveness and feasibility of the method.
Keywords
Cite This Article
C. Cheng, X. Sun, D. Lin and Y. Tu, "Research on efficient seismic data acquisition methods based on sparsity constraint,"
Computers, Materials & Continua, vol. 64, no.1, pp. 651–664, 2020. https://doi.org/10.32604/cmc.2020.09874