Multitype Geomagnetic Noise Removal Via an Improved U-Net Deep Learning Network.

Geomagnetic data are widely used in earthquake prediction, mantle conductivity imaging, and other fields. However, the problem of geomagnetic data being contaminated by cultural noise is becoming increasingly serious. Existing denoising methods have shortcomings such as insufficient flexibility and the need for manual intervention. To this end, we modify the U-Net and propose a new intelligent geomagnetic signal denoising method based on the network. The novel network not only combines the advantages of denoising convolutional neural network (DnCNN) and U-Net, but also utilizes the shortcut connections to prevent network degradation. We obtain a high-precision denoising model through elaborate training sets. The processing results of synthetic data show that the improved U-Net can remove various types of noise in one step, such as impulse noise, square wave noise, and Gaussian noise. The signal-to-noise ratio (SNR) of the denoised signal increases by an average of more than 20 dB, and the average normalized-cross correlation (NCC) between the denoised signal and the high-quality signal reaches 0.9998. Compared with Wavelet threshold denoising, DnCNN, and U-Net, the improved U-Net has obvious advantages. We apply the method to real geomagnetic data collected in Guangxi, Yunnan, Gansu, and Tibet, China. The results demonstrate that the proposed method can significantly improve the tippers, coherencies, and induction arrows. Compared with traditional methods, our method eliminates subjective bias, improves the adaptability to different types of noise, and is conducive to improving the resolution and reliability of geomagnetic depth sounding (GDS).