Open Access

ARTICLE

Research on Efficient Seismic Data Acquisition Methods Based on Sparsity Constraint

Caifeng Cheng^{1, 2}, Xiang’e Sun^{1, *}, 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.

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Keywords

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