Controlled-source electromagnetic data denoising based on improved vision transformer
Xiao Guo-zhen, Li Guang*, Zhang Kun*, Wang Xin, and Li-Jin
1. Nanchang Key Laboratory of Intelligent Sensing Technology and Instruments for Geological Hazards, East China University of Technology, Nanchang 330013, China.
2. The SinoProbe Laboratory, Chinese Academy of Geological Sciences, Beijing 100094, China.
Abstract：
Controlled-source electromagnetic method (CSEM) has been widely applied in engineering and environmental surveys, resource and energy exploration, as well as geological disaster detection. However, due to the increasingly human noises, CSEM data are inevitably subjected to electromagnetic noise, which severely affect the detection results. To address this issue, we propose an improved vision transformer (IVIT) deep learning denoising network to suppress cultural noise, and use wide-fi eld electromagnetic (WFEM, a kind of CSEM) data as an example to validate the eff ectiveness and superiority. First, typical high-quality CSEM data are selected and a series of simulated noises are added to create a sample library. Second, the well-prepared sample library is used to train the IVIT network. Finally, the well-trained model is employed to perform a one-step denoising operation on the noisy CSEM data to obtain high-quality data. Comparative experiments are conducted with denoising convolutional neural network (DnCNN), residual networks (ResNet), residual DnCNN (ResDnCNN), ResDnCNN combined with shift-invariant sparse coding (ResDnCNN-SISC), U-Net networks, and long short-term memory networks (LSTM). The proposed method eff ectively removes white noise, pulse noise, and square wave noise, and improves the signal-to-noise ratio (SNR) by approximately 20 dB. Compared with the competitive methods, it has obvious advantages. Analysis of CSEM data from Sichuan Province, China, shows that the data processed by the proposed method results in smoother apparent resistivity curves. In summary, the proposed denoising method can be used to suppress the strong noise of CSEM data, which is helpful for subsequent research of inversion.
基金资助:本研究得到了深地国家科技重大专项（Nos.2024ZD1000208 and 2025ZD1007602）、国家自然科学基金（No.42364006）、中国地质科学院SinoProbe实验室开放基金（No.SL202401）、江西省主要学科学术和技术带头人培养项目（No.20232BCJ23051）和江西省自然科学基金资助（No.20232kAB203070）的资助。
作者简介: The research is fi nancially supported by the Deep Earth National Science and Technology Major Project (Nos.2024ZD1000208 and 2025ZD1007602), the National Natural Science Foundation of China (No. 42364006), the Fund from SinoProbe Laboratory, Chinese Academy of Geological Sciences (No. SL202401), Training Program for Academic and Technical Leaders in Major Disciplines of Jiangxi Province (No. 20232BCJ23051), and Natural Science Foundation of Jiangxi Province (No.20232BAB203070).
2025, Vol. 22 
