Open AccessElectromagnetic Noise Reduction in GREATEM Signal Using Singular Value Decomposition in a Junkyard
Author(s) -
Ming Guo,
Xuben Wang,
Zhenxiong Zhang
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 - 10.1088/1755-1315/660/1/012089
Subject(s) - singular value decomposition , electromagnetic field , attenuation , inversion (geology) , noise reduction , signal (programming language) , singular value , acoustics , singular spectrum analysis , mathematics , physics , computer science , optics , algorithm , geology , paleontology , eigenvalues and eigenvectors , quantum mechanics , structural basin , programming language
The grounded electrical source airborne transient electromagnetic system (GREATEM) is a popular geophysical method in recent years [1], has advantages such as convenience, high efficiency, large detection range, high signal-to-noise ratio, and good spatial resolution. However, since the GREATEM transient electromagnetic secondary field electromagnetic response belongs to a wide-band signal and a large attenuation amplitude, late-stage data is easily contaminated by multiple noises, which will seriously affect the results of the inversion interpretation of the data. Therefore, it is still meaningful to study the de-noising methods for GREATEM’s data. According to the characteristics of the secondary field electromagnetic response, the singular value decomposition method is used to process the theoretical synthesized signal containing white Gaussian noise. The singular value decomposition and reconstruction of the effective singular value and its corresponding vector are performed, and the analog signal is restored for the purpose of denoising. Then process the measured data of a landfill site. Whether it is viewed from the transient electromagnetic secondary field attenuation curve or the electromagnetic induction profile curve, it can be seen that the singular value decomposition is very useful in denoising. Finally, comparing the effect of singular value decomposition method on the inversion results, the study found that this method can effectively improve the quality of inversion imaging.