A Practical Method for the Automatic Recognition of Rock Structures in Panoramic Borehole Image during Deep-Hole Drilling Engineering

Digital panoramic borehole imaging technology has been widely used in the practice of drilling engineering. Based on many high-definition panoramic borehole images obtained by the borehole imaging system, this paper puts forward an automatic recognition method based on clustering and characteristic...

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Autores principales:	Xianjian Zou, Chuanying Wang, Huajun Zhang, Shuangyuan Chen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	borehole image automatic recognition structural plane clustering image process drilling engineering Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/deac04b668354e21a7be0a95f707fbc3
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id	oai:doaj.org-article:deac04b668354e21a7be0a95f707fbc3
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spelling	oai:doaj.org-article:deac04b668354e21a7be0a95f707fbc32021-11-11T15:25:41ZA Practical Method for the Automatic Recognition of Rock Structures in Panoramic Borehole Image during Deep-Hole Drilling Engineering10.3390/app1121104902076-3417https://doaj.org/article/deac04b668354e21a7be0a95f707fbc32021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10490https://doaj.org/toc/2076-3417Digital panoramic borehole imaging technology has been widely used in the practice of drilling engineering. Based on many high-definition panoramic borehole images obtained by the borehole imaging system, this paper puts forward an automatic recognition method based on clustering and characteristic functions to perform intelligent analysis and automatic interpretation researches, and successfully applied to the analysis of the borehole images obtained at the Wudongde Hydropower Station in the south-west of China. The results show that the automatic recognition method can fully and quickly automatically identify most of the important structural planes and their position, dip, dip angle and gap width and other characteristic parameter information in the entire borehole image. The recognition rate of the main structural plane is about 90%. The accuracy rate is about 85%, the total time cost is about 3 h, and the accuracy deviation is less than 4% among the 12 boreholes with a depth of about 50 m. The application of automatic recognition technology to the panoramic borehole image can greatly improve work efficiency, reduce the time cost, and avoid the interference caused by humans, making it possible to automatically recognize the structural plane parameters of the full-hole image.Xianjian ZouChuanying WangHuajun ZhangShuangyuan ChenMDPI AGarticleborehole imageautomatic recognitionstructural planeclusteringimage processdrilling engineeringTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10490, p 10490 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	borehole image automatic recognition structural plane clustering image process drilling engineering Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	borehole image automatic recognition structural plane clustering image process drilling engineering Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Xianjian Zou Chuanying Wang Huajun Zhang Shuangyuan Chen A Practical Method for the Automatic Recognition of Rock Structures in Panoramic Borehole Image during Deep-Hole Drilling Engineering
description	Digital panoramic borehole imaging technology has been widely used in the practice of drilling engineering. Based on many high-definition panoramic borehole images obtained by the borehole imaging system, this paper puts forward an automatic recognition method based on clustering and characteristic functions to perform intelligent analysis and automatic interpretation researches, and successfully applied to the analysis of the borehole images obtained at the Wudongde Hydropower Station in the south-west of China. The results show that the automatic recognition method can fully and quickly automatically identify most of the important structural planes and their position, dip, dip angle and gap width and other characteristic parameter information in the entire borehole image. The recognition rate of the main structural plane is about 90%. The accuracy rate is about 85%, the total time cost is about 3 h, and the accuracy deviation is less than 4% among the 12 boreholes with a depth of about 50 m. The application of automatic recognition technology to the panoramic borehole image can greatly improve work efficiency, reduce the time cost, and avoid the interference caused by humans, making it possible to automatically recognize the structural plane parameters of the full-hole image.
format	article
author	Xianjian Zou Chuanying Wang Huajun Zhang Shuangyuan Chen
author_facet	Xianjian Zou Chuanying Wang Huajun Zhang Shuangyuan Chen
author_sort	Xianjian Zou
title	A Practical Method for the Automatic Recognition of Rock Structures in Panoramic Borehole Image during Deep-Hole Drilling Engineering
title_short	A Practical Method for the Automatic Recognition of Rock Structures in Panoramic Borehole Image during Deep-Hole Drilling Engineering
title_full	A Practical Method for the Automatic Recognition of Rock Structures in Panoramic Borehole Image during Deep-Hole Drilling Engineering
title_fullStr	A Practical Method for the Automatic Recognition of Rock Structures in Panoramic Borehole Image during Deep-Hole Drilling Engineering
title_full_unstemmed	A Practical Method for the Automatic Recognition of Rock Structures in Panoramic Borehole Image during Deep-Hole Drilling Engineering
title_sort	practical method for the automatic recognition of rock structures in panoramic borehole image during deep-hole drilling engineering
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/deac04b668354e21a7be0a95f707fbc3
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A Practical Method for the Automatic Recognition of Rock Structures in Panoramic Borehole Image during Deep-Hole Drilling Engineering

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