An Adaptive Decomposition Method with Low Parameter Sensitivity for Non-Stationary Noise Suppression in Magnetotelluric Data

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Publicado en:Minerals vol. 15, no. 8 (2025), p. 808-830
Autor principal: Guo Zhenyu
Otros Autores: Huang, Cheng, Jiang, Wen, Hong, Tao, Han Jiangtao
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MDPI AG
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024 7 |a 10.3390/min15080808  |2 doi 
035 |a 3244048528 
045 2 |b d20250101  |b d20251231 
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100 1 |a Guo Zhenyu  |u Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China; guozhenyu@xidian.edu.cn (Z.G.); jiangwen@mail.xidian.edu.cn (W.J.); thong@mail.xidian.edu.cn (T.H.) 
245 1 |a An Adaptive Decomposition Method with Low Parameter Sensitivity for Non-Stationary Noise Suppression in Magnetotelluric Data 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a Magnetotelluric (MT) sounding is a crucial technique in mineral exploration. However, MT data are highly susceptible to various types of noise. Traditional data processing methods, which rely on the assumption of signal stationarity, often result in severe distortion when suppressing non-stationary noise. In this study, we propose a novel, adaptive, and less parameter-dependent signal decomposition method for MT signal denoising, based on time–frequency domain analysis and the application of modal decomposition. The method uses Variational Mode Decomposition (VMD) to adaptively decompose the MT signal into several intrinsic mode functions (IMFs), obtaining the instantaneous time–frequency energy distribution of the signal. Subsequently, robust statistical methods are introduced to extract the independent components of each IMF, thereby identifying signal and noise components within the decomposition results. Synthetic data experiments show that our method accurately separates high-amplitude non-stationary interference. Furthermore, it maintains stable decomposition results under various parameter settings, exhibiting strong robustness and low parameter dependency. When applied to field MT data, the method effectively filters out non-stationary noise, leading to significant improvements in both apparent resistivity and phase curves, indicating its practical value in mineral exploration. 
653 |a Mineral exploration 
653 |a Kurtosis 
653 |a Data processing 
653 |a Wavelet transforms 
653 |a Parameter sensitivity 
653 |a Optimization 
653 |a Signal processing 
653 |a Data analysis 
653 |a Statistical methods 
653 |a Time series 
653 |a Lagrange multiplier 
653 |a Spectrum analysis 
653 |a Fourier transforms 
653 |a Time-frequency analysis 
653 |a Noise sensitivity 
653 |a Noise reduction 
653 |a Frequency domain analysis 
653 |a Decomposition 
653 |a Algorithms 
653 |a Synthetic data 
653 |a Energy distribution 
700 1 |a Huang, Cheng  |u Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China; guozhenyu@xidian.edu.cn (Z.G.); jiangwen@mail.xidian.edu.cn (W.J.); thong@mail.xidian.edu.cn (T.H.) 
700 1 |a Jiang, Wen  |u Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China; guozhenyu@xidian.edu.cn (Z.G.); jiangwen@mail.xidian.edu.cn (W.J.); thong@mail.xidian.edu.cn (T.H.) 
700 1 |a Hong, Tao  |u Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China; guozhenyu@xidian.edu.cn (Z.G.); jiangwen@mail.xidian.edu.cn (W.J.); thong@mail.xidian.edu.cn (T.H.) 
700 1 |a Han Jiangtao  |u College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China; hanjt@jlu.edu.cn 
773 0 |t Minerals  |g vol. 15, no. 8 (2025), p. 808-830 
786 0 |d ProQuest  |t ABI/INFORM Global 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3244048528/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3244048528/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3244048528/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch