Abstract
Magnetotelluric (MT) sounding data can easily be damaged by various types of noise, especially in industrial areas, where the quality of measured data is poor. Most traditional de-noising methods are ineffective to the low signal-to-noise ratio of data. To solve the above problem, we propose the use of a de-noising method for the detection of noise in MT data based on discrete wavelet transform and singular value decomposition (SVD), with multiscale dispersion entropy and phase space reconstruction carried out for pretreatment. No “over processing” takes place in the proposed method. Compared with wavelet transform and SVD decomposition in synthetic tests, the proposed method removes the profile of noise more completely, including large-scale noise and impulse noise. For high levels or low levels of noise, the proposed method can increase the signal-to-noise ratio of data more obviously. Moreover, application to the field MT data can prove the performance of the proposed method. The proposed method is a feasible method for the elimination of various noise types and can improve MT data with high noise levels, obtaining a recovery in the response. It can improve abrupt points and distortion in MT response curves more effectively than the robust method can.
Highlights
discrete wavelet transform (DWT) has been introduced in the proposed method, in order to inversely transform the signal in the wavelet domain into a time series, which is available for further processing by singular value decomposition (SVD) decomposition
Figure theoriginal originalsignal signalasasa areference, reference,blue bluelines linesindicate indicatethe thede-noising de-noisingresults resultsofofSVD, SVD,red redlines linesdenote denote the of 19 the results of wavelet transform (WT), purple lineslines show the results improved by theby proposed method, comtheprocessing processing results of WT, purple show the results improved the proposed method, pletely covered by black lines. (a–c) time dataofcontaminated by square noise after completely covered by black lines.show (a–c) the show theseries time of series data contaminated by square noise the usethe of use SVD, proposed method
To solve the MT response distortions caused by high levels of noise, we propose the use of a de-noising method based on discrete wavelet transform and singular value decomposition (SVD) that consists of three sections
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Based on the least squares method, the robust method was introduced for MT de-noising and can reduce the weight coefficients of noise through statistical analysis and coherency [7,10]. The most common way to select parameters for a method is through experience or repeated experiments, but this approach is influenced by subjective factors as well as being time-consuming These methods are ineffective in improving the quality of data when the signal-to-noise ratio is low and the selection of the parameters used in methods is inappropriate. We propose the use of a de-noising method based on discrete wavelet transform and iterative singular value decomposition (SVD). Compared with traditional SVD decomposition, wavelet transform and the robust method, the proposed method can remove the various noise more thoroughly and obtain the useful. MT response curves, which more truly reflect the subsurface electromagnetic structure
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