A Deep Learning Estimation of the Earth Resistivity Model for the Airborne Transient Electromagnetic Observation
文献类型：期刊论文
| 作者 | Wu, Xin1,2,3; Xue, Guoqiang1,2,3; Zhao, Yang1,2,3; Lv, Pengfei1,2,3; Zhou, Zhou1,2,3; Shi, Jinjing1,2,3 |
| 刊名 | JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
 |
| 出版日期 | 2022-03-01 |
| 卷号 | 127期号:3页码:19 |
| 关键词 | airborne transient electromagnetic deep learning manifold assumption stacked auto-encoder |
| ISSN号 | 2169-9313 |
| DOI | 10.1029/2021JB023185 |
| 英文摘要 | Because 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. |
| WOS关键词 | NEURAL-NETWORKS ; NOISE-REDUCTION ; HIGH-RESOLUTION ; PARAMETERIZATION ; PREDICTION ; ALGORITHM ; INVERSION |
| 资助项目 | Scientific 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] |
| WOS研究方向 | Geochemistry & Geophysics |
| 语种 | 英语 |
| WOS记录号 | WOS:000776510500053 |
| 出版者 | AMER GEOPHYSICAL UNION |
| 资助机构 | 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 ; 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 |
| 源URL | [http://ir.iggcas.ac.cn/handle/132A11/105026]  |
| 专题 | 地质与地球物理研究所_中国科学院矿产资源研究重点实验室 |
| 通讯作者 | Xue, Guoqiang |
| 作者单位 | 1.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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：地质与地球物理研究所
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