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Volume 71 Number 9
  • E-ISSN: 1365-2478

Abstract

Abstract

Traditional magnetotelluric signal processing usually uses time–frequency transformation method. Wavelet is also a time–frequency transformation method that used to suppress the magnetotelluric noise. However, the selection of the threshold is very significant, and the unsuitable threshold will lead to excessive distortion of the reconstructed signal. Thus, we propose a method for magnetotelluric noise suppression using grey wolf optimized wavelet threshold. First, the magnetotelluric signal is decomposed by wavelet with appropriate wavelet basis and decomposition layers. The generalized cross‐validation criterion is used as the fitness function of grey wolf optimizer algorithm, which optimizes the threshold of each decomposition layer. Then, the detail coefficients of each layer and the maximum layer of the approximation coefficients are used in the optimized threshold. Next, the inverse wavelet transform is performed. Finally, the noise contour is obtained through iteratively searching for the optimal threshold, and the useful magnetotelluric signal is reconstructed. Simulation experiments and measured magnetotelluric data processing show that the large‐scale interference can effectively be suppressed, and the reconstructed magnetotelluric signal retains the more abundant low‐frequency of useful information. Compared with the remote reference method, fixed threshold method and Birge–Massart layered threshold method, the proposed method realizes the wavelet denoising with adaptive threshold selection in the magnetotelluric noise suppression. The results obtained show smoother and more continuous apparent resistivity–phase curves, which verifies the effectiveness of the optimization.

© 2023 European Association of Geoscientists & Engineers. © 2022 European Association of Geoscientists & Engineers.
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/content/journals/10.1111/1365-2478.13294
2023-11-10
2025-05-24

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/content/journals/10.1111/1365-2478.13294
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Removal of strong noise in magnetotelluric data using grey wolf optimized wavelet threshold
Geophysical Prospecting 71, 1807 (2023); https://doi.org/10.1111/1365-2478.13294
/content/journals/10.1111/1365-2478.13294
/content/journals/10.1111/1365-2478.13294
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  • Article Type: Research Article
Keyword(s): generalized cross‐validation criterion; grey wolf optimizer; magnetotelluric; noise suppression; wavelet threshold

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