Identification of Underground Artificial Cavities Based on the Bayesian Convolutional Neural Network.

Xia, Jigen; Peng, Ronghua; Li, Zhiqiang; Li, Junyi; He, Yizhuo; Li, Gang

Xia, Jigen; Peng, Ronghua; Li, Zhiqiang; Li, Junyi; He, Yizhuo; Li, Gang.

Afiliação

Xia J; School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China.
Peng R; The 22nd Research Institute of China Electronics Technology Group Corporation, Xinxiang 453003, China.
Li Z; School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China.
Li J; The 22nd Research Institute of China Electronics Technology Group Corporation, Xinxiang 453003, China.
He Y; The 22nd Research Institute of China Electronics Technology Group Corporation, Xinxiang 453003, China.
Li G; College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130012, China.

Sensors (Basel) ; 23(19)2023 Sep 29.

Article em En | MEDLINE | ID: mdl-37836999

Palavras-chave

BCNN; apparent resistivity imaging method; artificial cavities; identification methods

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sensors (Basel) Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

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