@Article{cmc.2020.09874,
AUTHOR = {Caifeng Cheng, Xiang’e Sun, Deshu Lin, Yiliu Tu},
TITLE = {Research on Efficient Seismic Data Acquisition Methods Based on Sparsity Constraint},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {1},
PAGES = {651--664},
URL = {http://www.techscience.com/cmc/v64n1/39165},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.09874}
}