Magnetotelluric data denoising with recurrent neural network

PreviousNext No AccessSEG 2019 Workshop: Mathematical Geophysics: Traditional vs Learning, Beijing, China, 5–7 November 2019Magnetotelluric data denoising with recurrent neural networkAuthors: Hang Chen*Rongwen GuoJianxin LiuYongfei WangRongpei LinHang Chen*School of Geosciences and Info-Physics, Central South UniversityKey Laboratory of Non-ferrous Resources and Geological Hazard Detection, Central South UniversitySearch for more papers by this author, Rongwen GuoSchool of Geosciences and Info-Physics, Central South UniversityKey Laboratory of Non-ferrous Resources and Geological Hazard Detection, Central South UniversitySearch for more papers by this author, Jianxin LiuSchool of Geosciences and Info-Physics, Central South UniversityKey Laboratory of Non-ferrous Resources and Geological Hazard Detection, Central South UniversitySearch for more papers by this author, Yongfei WangSchool of Geosciences and Info-Physics, Central South UniversityKey Laboratory of Non-ferrous Resources and Geological Hazard Detection, Central South UniversitySearch for more papers by this author, and Rongpei LinSchool of Geosciences and Info-Physics, Central South UniversityKey Laboratory of Non-ferrous Resources and Geological Hazard Detection, Central South UniversitySearch for more papers by this authorhttps://doi.org/10.1190/iwmg2019_28.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract For the magnetotelluric (MT) method, the weak signal obtained can be easily affected by various kinds of noise. How to obtain the unbiased impedance estimation from measured data with complex noise is one focus of current research. In this paper, we design a recurrent neural network (RNN) algorithm with Long Short-Term Memory architecture to denoise MT data. We use synthetic data with errors generated from different stable distributions for training and testing. The network performs well in both the validation dataset and test dataset. Besides, the results show that the impedance estimates from the MT data denoised by RNN is much better than those without application of RNN using the traditional robust and least square methods, especially for MT data with strong noise. Keywords: magnetotelluric, neural networks, noise, tensor, impedancePermalink: https://doi.org/10.1190/iwmg2019_28.1FiguresReferencesRelatedDetailsCited byMagnetotelluric noise suppression via convolutional neural networkJin Li, Yecheng Liu, Jingtian Tang, and Fanhong Ma16 January 2023 | GEOPHYSICS, Vol. 88, No. 1 SEG 2019 Workshop: Mathematical Geophysics: Traditional vs Learning, Beijing, China, 5–7 November 2019ISSN (online):2159-6832Copyright: 2020 Pages: 138 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 17 Jan 2020 CITATION INFORMATION Hang Chen*, Rongwen Guo, Jianxin Liu, Yongfei Wang, and Rongpei Lin, (2020), "Magnetotelluric data denoising with recurrent neural network," SEG Global Meeting Abstracts : 116-118. https://doi.org/10.1190/iwmg2019_28.1 Plain-Language Summary Keywordsmagnetotelluricneural networksnoisetensorimpedancePDF DownloadLoading ...