Open AccessInterference identification and removal of seismic precursor observation data based on singular spectrum analysis method
Author(s) -
Xiao Wang,
Yalu Wang,
Jun Wang,
Jingguo Huang,
Dan Yu,
Qing Ye,
Gaochuan Liu
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/671/1/012038
Subject(s) - singular spectrum analysis , interference (communication) , field (mathematics) , computer science , spectrum (functional analysis) , singular value , noise (video) , spectrum analysis , identification (biology) , signal (programming language) , singular value decomposition , process (computing) , acoustics , algorithm , data mining , mathematics , telecommunications , physics , artificial intelligence , image (mathematics) , channel (broadcasting) , eigenvalues and eigenvectors , astrophysics , pure mathematics , biology , programming language , operating system , botany , quantum mechanics
The singular spectrum analysis (SSA) method has the characteristics that it is not affected by the noise spectrum distribution, and is superior to the traditional denoising method. Moreover, it has been commonly used in signal analysis in the fields of oceanography, machinery, and electronic technology. This paper systematically introduces the basic principle and implementation process of singular spectrum analysis method. Then the singular spectrum analysis method is applied to the interference identification and removal of seismic precursor observation data. Taking deformation data and geoelectric field observation data as an example, the results show that the singular spectrum analysis method can effectively separate the rainfall interference in deformation observation data, and has important guiding significance for the subsequent removal of rainfall interference. In addition, the singular spectrum analysis method can be used to extract and remove the interference of HVDC and subway from the geoelectric field observation data, and the effect is obvious, which can effectively improve the data quality and better serve the earthquake analysis and prediction.