Study on Post-Stack Signal Denoising for Long-Offset Transient Electromagnetic Data Based on Combined Windowed Interpolation and Singular Spectrum Analysis

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Publikašuvnnas:Geosciences vol. 15, no. 4 (2025), p. 121
Váldodahkki: Lu Chuyang
Eará dahkkit: Xie Xingbing, Xu, Yang, Zhou, Lei, Liangjun, Yan
Almmustuhtton:
MDPI AG
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022 |a 2076-3263 
024 7 |a 10.3390/geosciences15040121  |2 doi 
035 |a 3194611835 
045 2 |b d20250101  |b d20251231 
084 |a 231468  |2 nlm 
100 1 |a Lu Chuyang 
245 1 |a Study on Post-Stack Signal Denoising for Long-Offset Transient Electromagnetic Data Based on Combined Windowed Interpolation and Singular Spectrum Analysis 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a The long-offset transient electromagnetic (LOTEM) method, as a widely applied electromagnetic exploration technique, plays a significant role in mineral resource exploration, hydraulic fracturing monitoring, and fluid identification in oil and gas reservoirs. However, due to external interference, the signals acquired by this method often contain substantial noise, which severely affects the reliability of subsequent inversion and interpretation. Therefore, denoising is a critical issue in LOTEM data processing. To address this problem, this paper proposes a denoising study for LOTEM post-stack signals based on a combination of windowed interpolation and singular spectrum analysis. First, the stacking method and windowed interpolation are employed to remove most of the random noise and power-line interference (including its harmonics). Then, singular spectrum analysis is applied to further suppress noise and obtain higher-quality signal data. Experimental results demonstrate that the proposed method performs well in denoising, effectively reducing the root mean square error (RMSE) of the signal and improving its signal-to-noise ratio (SNR). The method was validated using LOTEM data collected from Zhongjiang County, Sichuan Province. The validation results show that the method can effectively remove noise interference from underground media, providing essential technical support for inversion and interpretation. 
653 |a Accuracy 
653 |a Hydraulic fracturing 
653 |a Mineral resources 
653 |a Data processing 
653 |a Signal processing 
653 |a Interpolation 
653 |a Oil reservoirs 
653 |a Spectrum analysis 
653 |a Data analysis 
653 |a Time series 
653 |a Resource exploration 
653 |a Exploration 
653 |a Fourier transforms 
653 |a Signal to noise ratio 
653 |a Electromagnetic exploration 
653 |a Root-mean-square errors 
653 |a Signal quality 
653 |a Noise reduction 
653 |a Neural networks 
653 |a Power lines 
653 |a Random noise 
653 |a Mineral exploration 
653 |a Signal-to-noise ratio 
700 1 |a Xie Xingbing 
700 1 |a Xu, Yang 
700 1 |a Zhou, Lei 
700 1 |a Liangjun, Yan 
773 0 |t Geosciences  |g vol. 15, no. 4 (2025), p. 121 
786 0 |d ProQuest  |t Publicly Available Content Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3194611835/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3194611835/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3194611835/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch