Academic Journal of Environment & Earth Science, 2025, 7(3); doi: 10.25236/AJEE.2025.070307.
Ying Yunxiang1,2,3, Hu Zhiwen1,2,3, Wu Wannan1,3, Chi Mingzhang1,2,3, Che Mengqiang1,2,3
1Anhui Mengcheng National Geophysical Field Observation and Research Station, Anhui, Mengcheng, 233500, China
2Mengcheng Earthquake Monitoring Center Station, Mengcheng, Anhui, 233500, China
3Anhui Seismological Bureau, Hefei, Anhui, 230031, China
The application of geomagnetic signals in geophysics is very important, and geomagnetic signals has significant value in the fields of resource exploration, geological exploration and pipeline exploration. However, geomagnetic signals are susceptible to interference from environmental noise during acquisition, which seriously affects the quality of the data and the accuracy of subsequent analysis. In order to effectively remove noise from geomagnetic signals, this paper proposes a wavelet threshold denoising method based on ant colony optimization. Firstly, the wavelet transform is performed on the geomagnetic signal, and the multi-scale analysis ability of the wavelet transform is used to separate the signal and noise. Then, the Generalized Cross-Validation (GCV) function is used to select the optimal wavelet threshold, which does not require prior knowledge of noise and can effectively balance denoising and signal fidelity. Finally, the ant colony optimization algorithm was combined to iteratively optimize the threshold to find the optimal solution, so as to improve the denoising performance. Through comparative experiments, the proposed method is superior to the traditional denoising method in terms of signal-to-noise ratio (SNR) and root mean square error (RMSE), which proves its effectiveness and superiority in geomagnetic signal processing.
Geomagnetic Signal, Noise Suppression, Wavelet Transform, Ant Colony Optimization, Generalized Cross-Validation (GCV)
Ying Yunxiang, Hu Zhiwen, Wu Wannan, Chi Mingzhang, Che Mengqiang. Research on Geomagnetic Signal Denoising Method Based on Ant Colony Optimization Algorithm. Academic Journal of Environment & Earth Science (2025), Vol. 7, Issue 3: 49-54. https://doi.org/10.25236/AJEE.2025.070307.
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