Bulletin of Geological Science and Technology. 2023, (06) Core

Intelligent recognition and quantitative analysis of borehole hydraulic geological images utilizing multiple deep learning modelsChinese Full Text English Full Text (MT)

Zhang Ye;Chen Jinqiao;Li Yanlong;State Key Laboratory of Eco-hydraulics in Northwest Arid Region,Xi′an University of Technology;

Abstract: [Objective] Borehole Televiewer（BHTV） imaging serves as an effective tool for analyzing deep rock formations in geological engineering, offering crucial insights into surface discontinuity extensions and the distribution of buried discontinuities, especially within complex geological environments associated with hydraulic engineering projects. Recognizing the current issues of subjectivity and low efficiency, prevalent in the manual calculation of rock discontinuity morphological characteristics... More

Keywords：

deep learning; Attention mechanism; Unet; image thinning; fracture quantitative analysis; borehole; intelligent recognition;

DOI：
10.19509/j.cnki.dzkq.tb20220091
Series：
(A) Mathematics/ Physics/ Mechanics/ Astronomy; (C) Architecture/ Energy/ Traffic/ Electromechanics, etc; (I) Electronic Technology ＆ Information Science
Subject：
Hydraulic and Hydropower Engineering; Computer Software and Application of Computer
Classification Code：
TP391.41;TV221.2

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