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Enhanced Electrical Resistivity Tomography With Prior Physical Information

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成果类型:
期刊论文
作者:
Jia, Zhuo;Huang, Meijia;Huo, Zhijun;Li, Yabin
通讯作者:
Li, YB
作者机构:
[Huang, Meijia; Jia, Zhuo] Changsha Univ Sci & Technol, Sch Civil Engn, Changsha 410114, Peoples R China.
[Jia, Zhuo] Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Dept Automat, Beijing 100084, Peoples R China.
[Huo, Zhijun] Fudan Univ, Sch Informat Sci & Technol, Shanghai 200437, Peoples R China.
[Li, Yabin; Li, YB] Jilin Univ, Coll Geoexplorat Sci & Technol, Changchun 130012, Jilin, Peoples R China.
通讯机构:
[Li, YB ] J
Jilin Univ, Coll Geoexplorat Sci & Technol, Changchun 130012, Jilin, Peoples R China.
语种:
英文
关键词:
Deep learning;electrical resistivity tomography (ERT);electrical resistivity tomography (ERT);prior knowledge;prior knowledge;resolution;resolution
期刊:
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
ISSN:
1545-598X
年:
2025
卷:
22
页码:
1-5
基金类别:
Major Research Project on Scientific Instrument Development (Key Technologies and Prototype of Synchronous Seabed Seismic and EM Exploration Systems) (Grant Number: 42327901) 10.13039/501100001809-China National Natural Science Foundation (recommended via department)
机构署名:
本校为第一机构
院系归属:
土木工程学院
摘要:
Electrical resistivity tomography (ERT) is a key geophysical technique that provides detailed information on subsurface structures by measuring the distribution of electrical resistivity underground. ERT suffers from limitations in electrode arrangement, interference from environmental and instrument noise, and existing data processing algorithms that fail to adequately consider geological heterogeneity and uncertainty, resulting in insufficient inversion resolution. Traditional ERT methods rely on simplified algorithms and a limited number of ...

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