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convolutional neural network (ResDnCNN) and shift-invariant sparse coding (SISC) for denoising CSEM data. Firstly, a sample library was constructed by adding simulated noises of different types and amplitudes to high-quality CSEM data collected. Then, the sample library was used for model training in the ResDnCNN, resulting in a network model specifically designed for denoising CSEM data. Subsequently, the trained model was employed to denoise the measured data, generating preliminary denoised data. Finally, the preliminary denoised data was processed using SISC to obtain the final denoised high-quality data. Comparative experiments with the ResNet, DnCNN, U-Net, and long short-term memory (LSTM) networks demonstrated the significant advantages of our proposed method. It effectively removed strong noise such as Gaussian, impulse, and square wave, resulting in an improvement of the signal-to-noise ratio by nearly 20 dB. Testing on CSEM data from Sichuan Province, China, showed that the apparent resistivity curves plotted using our method were smoother and more credible.<\/jats:p>","DOI":"10.3390\/rs15184456","type":"journal-article","created":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T09:09:21Z","timestamp":1694423361000},"page":"4456","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Noise Attenuation for CSEM Data via Deep Residual Denoising Convolutional Neural Network and Shift-Invariant Sparse Coding"],"prefix":"10.3390","volume":"15","author":[{"given":"Xin","family":"Wang","sequence":"first","affiliation":[{"name":"Nanchang Key Laboratory of Intelligent Sensing Technology and Instruments for Geological Hazards, East China University of Technology, Nanchang 330013, China"},{"name":"Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Ministry of Education, Changsha 410083, China"}]},{"given":"Ximin","family":"Bai","sequence":"additional","affiliation":[{"name":"Jiangxi Institute Co., Ltd. of Survey and Design, Nanchang 330095, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8693-3757","authenticated-orcid":false,"given":"Guang","family":"Li","sequence":"additional","affiliation":[{"name":"Nanchang Key Laboratory of Intelligent Sensing Technology and Instruments for Geological Hazards, East China University of Technology, Nanchang 330013, China"},{"name":"Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Ministry of Education, Changsha 410083, China"},{"name":"Fundamental Science on Radioactive Geology and Exploration Technology Laboratory, East China University of Technology, Nanchang 330013, China"}]},{"given":"Liwei","family":"Sun","sequence":"additional","affiliation":[{"name":"Nanchang Key Laboratory of Intelligent Sensing Technology and Instruments for Geological Hazards, East China University of Technology, Nanchang 330013, China"}]},{"given":"Hailong","family":"Ye","sequence":"additional","affiliation":[{"name":"Jiangxi Institute Co., Ltd. of Survey and Design, Nanchang 330095, China"}]},{"given":"Tao","family":"Tong","sequence":"additional","affiliation":[{"name":"Jiangxi Institute Co., Ltd. of Survey and Design, Nanchang 330095, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"E77","DOI":"10.1190\/geo2020-0164.1","article-title":"3D multinary inversion of controlled-source electromagnetic data based on the finite-element method with unstructured mesh","volume":"86","author":"Cai","year":"2021","journal-title":"Geophysics"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.1093\/gji\/ggaa213","article-title":"Effects of electrical anisotropy on long-offset transient electromagnetic data","volume":"222","author":"Liu","year":"2020","journal-title":"Geophys. 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