IGGCAS OpenIR  > 矿产资源研究院重点实验室
A Deep Learning Estimation of the Earth Resistivity Model for the Airborne Transient Electromagnetic Observation
Wu, Xin1,2,3; Xue, Guoqiang1,2,3; Zhao, Yang1,2,3; Lv, Pengfei1,2,3; Zhou, Zhou1,2,3; Shi, Jinjing1,2,3
2022-03-01
Source PublicationJOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
ISSN2169-9313
Volume127Issue:3Pages:19
AbstractBecause of the relatively weak useful signal and noises with complex characteristics, the data processing of airborne electromagnetic observation is very difficult. As the downstream of data processing, inversion generally cannot further distinguish whether there are residual noises in the processed data. That is, in the current processing flow, the signal-noise distinguishing is isolated from the signal-model mapping. The reliability of the estimated earth resistivity model will be seriously affected due to the probable disadvantages in the denoising process. To this end, we propose a manifold assumption, and so establish one "feedback" mechanism between signal-noise distinguishing and signal-model mapping. On this basis, we propose a deep learning method: through simultaneous optimal training the network parts for signal-noise distinguishing and earth resistivity model estimation respectively, the entire neural network can perform the denoising and inversion in the traditional sense at the same time, so as to obtain more objective and reliable estimation results of earth resistivity model. We use the Stacked Auto-encoder neural network structure to implement the proposed method, and test the network performance with simulation and measured data. The results show that the proposed method can obtain a more reliable earth resistivity model directly from the noisy data.
Keywordairborne transient electromagnetic deep learning manifold assumption stacked auto-encoder
DOI10.1029/2021JB023185
Funding OrganizationScientific Equipment Instrument and Development Project of the Chinese Academy of Sciences ; Scientific Equipment Instrument and Development Project of the Chinese Academy of Sciences ; R&D of Key Instruments and Technologies for Deep Resources Prospecting ; R&D of Key Instruments and Technologies for Deep Resources Prospecting ; Key Technologies for Deep Resources Prospecting of Beijing Municipal Science and Technology Commission ; Key Technologies for Deep Resources Prospecting of Beijing Municipal Science and Technology Commission ; Natural Science Foundation of China ; Natural Science Foundation of China ; Scientific Equipment Instrument and Development Project of the Chinese Academy of Sciences ; Scientific Equipment Instrument and Development Project of the Chinese Academy of Sciences ; R&D of Key Instruments and Technologies for Deep Resources Prospecting ; R&D of Key Instruments and Technologies for Deep Resources Prospecting ; Key Technologies for Deep Resources Prospecting of Beijing Municipal Science and Technology Commission ; Key Technologies for Deep Resources Prospecting of Beijing Municipal Science and Technology Commission ; Natural Science Foundation of China ; Natural Science Foundation of China ; Scientific Equipment Instrument and Development Project of the Chinese Academy of Sciences ; Scientific Equipment Instrument and Development Project of the Chinese Academy of Sciences ; R&D of Key Instruments and Technologies for Deep Resources Prospecting ; R&D of Key Instruments and Technologies for Deep Resources Prospecting ; Key Technologies for Deep Resources Prospecting of Beijing Municipal Science and Technology Commission ; Key Technologies for Deep Resources Prospecting of Beijing Municipal Science and Technology Commission ; Natural Science Foundation of China ; Natural Science Foundation of China ; Scientific Equipment Instrument and Development Project of the Chinese Academy of Sciences ; Scientific Equipment Instrument and Development Project of the Chinese Academy of Sciences ; R&D of Key Instruments and Technologies for Deep Resources Prospecting ; R&D of Key Instruments and Technologies for Deep Resources Prospecting ; Key Technologies for Deep Resources Prospecting of Beijing Municipal Science and Technology Commission ; Key Technologies for Deep Resources Prospecting of Beijing Municipal Science and Technology Commission ; Natural Science Foundation of China ; Natural Science Foundation of China
WOS KeywordNEURAL-NETWORKS ; NOISE-REDUCTION ; HIGH-RESOLUTION ; PARAMETERIZATION ; PREDICTION ; ALGORITHM ; INVERSION
Language英语
Funding ProjectScientific Equipment Instrument and Development Project of the Chinese Academy of Sciences[YJKYYQ20190004] ; R&D of Key Instruments and Technologies for Deep Resources Prospecting[ZDYZ20121-03] ; Key Technologies for Deep Resources Prospecting of Beijing Municipal Science and Technology Commission[Z181100005718001] ; Natural Science Foundation of China[42030106] ; Natural Science Foundation of China[42074121]
Funding OrganizationScientific Equipment Instrument and Development Project of the Chinese Academy of Sciences ; Scientific Equipment Instrument and Development Project of the Chinese Academy of Sciences ; R&D of Key Instruments and Technologies for Deep Resources Prospecting ; R&D of Key Instruments and Technologies for Deep Resources Prospecting ; Key Technologies for Deep Resources Prospecting of Beijing Municipal Science and Technology Commission ; Key Technologies for Deep Resources Prospecting of Beijing Municipal Science and Technology Commission ; Natural Science Foundation of China ; Natural Science Foundation of China ; Scientific Equipment Instrument and Development Project of the Chinese Academy of Sciences ; Scientific Equipment Instrument and Development Project of the Chinese Academy of Sciences ; R&D of Key Instruments and Technologies for Deep Resources Prospecting ; R&D of Key Instruments and Technologies for Deep Resources Prospecting ; Key Technologies for Deep Resources Prospecting of Beijing Municipal Science and Technology Commission ; Key Technologies for Deep Resources Prospecting of Beijing Municipal Science and Technology Commission ; Natural Science Foundation of China ; Natural Science Foundation of China ; Scientific Equipment Instrument and Development Project of the Chinese Academy of Sciences ; Scientific Equipment Instrument and Development Project of the Chinese Academy of Sciences ; R&D of Key Instruments and Technologies for Deep Resources Prospecting ; R&D of Key Instruments and Technologies for Deep Resources Prospecting ; Key Technologies for Deep Resources Prospecting of Beijing Municipal Science and Technology Commission ; Key Technologies for Deep Resources Prospecting of Beijing Municipal Science and Technology Commission ; Natural Science Foundation of China ; Natural Science Foundation of China ; Scientific Equipment Instrument and Development Project of the Chinese Academy of Sciences ; Scientific Equipment Instrument and Development Project of the Chinese Academy of Sciences ; R&D of Key Instruments and Technologies for Deep Resources Prospecting ; R&D of Key Instruments and Technologies for Deep Resources Prospecting ; Key Technologies for Deep Resources Prospecting of Beijing Municipal Science and Technology Commission ; Key Technologies for Deep Resources Prospecting of Beijing Municipal Science and Technology Commission ; Natural Science Foundation of China ; Natural Science Foundation of China
WOS Research AreaGeochemistry & Geophysics
WOS SubjectGeochemistry & Geophysics
WOS IDWOS:000776510500053
PublisherAMER GEOPHYSICAL UNION
Citation statistics
Document Type期刊论文
Identifierhttp://ir.iggcas.ac.cn/handle/132A11/105026
Collection矿产资源研究院重点实验室
Corresponding AuthorXue, Guoqiang
Affiliation1.Chinese Acad Sci, Key Lab Mineral Resources, Beijing, Peoples R China
2.Chinese Acad Sci, Innovat Acad Earth Sci, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Wu, Xin,Xue, Guoqiang,Zhao, Yang,et al. A Deep Learning Estimation of the Earth Resistivity Model for the Airborne Transient Electromagnetic Observation[J]. JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH,2022,127(3):19.
APA Wu, Xin,Xue, Guoqiang,Zhao, Yang,Lv, Pengfei,Zhou, Zhou,&Shi, Jinjing.(2022).A Deep Learning Estimation of the Earth Resistivity Model for the Airborne Transient Electromagnetic Observation.JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH,127(3),19.
MLA Wu, Xin,et al."A Deep Learning Estimation of the Earth Resistivity Model for the Airborne Transient Electromagnetic Observation".JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH 127.3(2022):19.
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