Multi-channel geomagnetic signal processing based on deep residual network and MVMD

The geomagnetic data are of great value in earthquake prediction, space weather monitoring, mineral resources exploration, and deep structure exploration of the earth. However, the geomagnetic data are increasingly polluted by cultural noise, which greatly complicates the high-precision imaging of the earth's interior. Therefore, we extend the deep residual network (ResNet) and multivariate variational mode decomposition (MVMD) to the processing of geomagnetic signals and propose a novel multi-channel geomagnetic signal processing method. Firstly, a large number of manually labeled data sets are trained by ResNet to obtain a signal-to-noise recognition model. Then the trained model is used to identify the noisy fragments from the raw observation signal. Hereafter, MVMD is adopted to perform multi-channel signal-to-noise separation on noisy segments, and the denoised segments are obtained. Finally, the noisy segments in the original observation signal are replaced by the denoised segments to obtain a complete high-quality signal. To verify the effectiveness of the method, we designed simulation experiments. The results show that the proposed method can improve the signal-to-noise ratio of the observed signal by about 15 dB, which has obvious advantages over VMD, complementary ensemble empirical mode decomposition (CEEMD), mathematical morphological filtering (MMF), and Wavelet, and is suitable for the batching processing of multi-channel signals. We apply the proposed method to the geomagnetic data observed in the Philippine Sea and the Western Pacific Ocean. The results show that the recognition accuracy of the proposed method is about 98%, and can greatly improve the signal quality. The normalized cross-correlation between the processed signal and the high-quality signal of the adjacent station at the same time has increased from 94.75% before denoising to 97.34%, indicating that the result is reliable. Our method is expected to improve the accuracy and reliability of geomagnetic data imaging.