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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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Veröffentlicht in:Geosciences vol. 15, no. 4 (2025), p. 121
1. Verfasser: Lu Chuyang
Weitere Verfasser: Xie Xingbing, Xu, Yang, Zhou, Lei, Liangjun, Yan
Veröffentlicht:
MDPI AG
Schlagworte:
Accuracy
Hydraulic fracturing
Mineral resources
Data processing
Signal processing
Interpolation
Oil reservoirs
Spectrum analysis
Data analysis
Time series
Resource exploration
Exploration
Fourier transforms
Signal to noise ratio
Electromagnetic exploration
Root-mean-square errors
Signal quality
Noise reduction
Neural networks
Power lines
Random noise
Mineral exploration
Signal-to-noise ratio
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Abstract: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.
ISSN:2076-3263
DOI:10.3390/geosciences15040121
Quelle:Publicly Available Content Database