Content area
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.
Details
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
