The Bayesian Inversion Method With a Surrogate Modeling Based on Neural Network for GATEM Data-Reference-Cited by-同舟云学术

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The Bayesian Inversion Method With a Surrogate Modeling Based on Neural Network for GATEM Data

Published:2024 Issue: Volume:62 Page:1-9
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Wu Qiong¹^ORCID,Gong Junling¹,Wang Weiyi¹,Ji Yanju²,Li Dongsheng¹

Affiliation:

1. College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China

2. College of Instrumentation and Electrical Engineering and the Key Laboratory of Geophysical Exploration Equipment, Ministry of Education, Jilin University, Changchun, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/36/10354519/10637427.pdf?arnumber=10637427

Reference32 articles.

1. A comparison of data from airborne, semi‐airborne, and ground electromagnetic systems

2. Comparison of novel semi-airborne electromagnetic data with multi-scale geophysical, petrophysical and geological data from Schleiz, Germany

3. Development of Grounded Electrical Source Airborne Transient EM (GREATEM)

4. Frequency and time domain three-dimensional inversion of electromagnetic data for a grounded-wire source

5. The processing workflow of semi-airborne transient electromagnetic data using geological modeling and three-dimensional numerical simulation – A case history in Yunnan, China

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