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Article: Novel Rock Image Classification: The Proposal and Implementation of HKUDES_Net

TitleNovel Rock Image Classification: The Proposal and Implementation of HKUDES_Net
Authors
KeywordsAlerting level
Automatic rock classification
Convolutional neural networks (CNNs)
HKUDES_Net
Issue Date10-Feb-2023
PublisherSpringer
Citation
Rock Mechanics and Rock Engineering, 2023, v. 56, n. 5, p. 3825-3841 How to Cite?
Abstract

Rock classification provides vital information to geosciences and geological engineering practices. Reaping the benefits of the advances of computer vision-based deep learning artificial intelligence (AI) technology, this study aims to develop a next-generation convolutional neural network (CNN) to perform automatic rock classification. Two major challenging issues have been particularly addressed. First, most of the previous rock classifications are simply transfer learning of CNNs that are trained by life-like scenarios. Second, classifying rock types with similar textures leads to severe overfitting of CNNs. In this study, a novel CNN called HKUDES_Net is proposed and implemented to classify seven common Hong Kong rock types, namely fine-grained granite, medium-grained granite, coarse-grained granite, coarse ash tuff, fine ash tuff, feldsparphyric rhyolite, and granodiorite. With the aid of dynamic expansion, squeeze and excitation, and other strategies, HKUDES_Net can classify rock types with similar texture patterns/colors but different grain sizes. As compared with the other ten landmark CNNs and seven feature-based algorithms, HKUDES_Net has the best performance in precision (90.9%), recall (90.4%), and f1-score (90.5%). By implementing the alerting level, which restricts the training loss hovering above a small constant and prevents the validation loss from rising, the overfitting has been efficiently eliminated. The proposal and implementation of HKUDES_Net highlight the value of interdisciplinary research and will continuously pave the way for better coupling AI and geosciences.


Persistent Identifierhttp://hdl.handle.net/10722/337489
ISSN
2023 Impact Factor: 5.5
2023 SCImago Journal Rankings: 1.902
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhou, Yimeng-
dc.contributor.authorWong, Louis Ngai Yuen-
dc.contributor.authorTse, Keith Ki Chun-
dc.date.accessioned2024-03-11T10:21:17Z-
dc.date.available2024-03-11T10:21:17Z-
dc.date.issued2023-02-10-
dc.identifier.citationRock Mechanics and Rock Engineering, 2023, v. 56, n. 5, p. 3825-3841-
dc.identifier.issn0723-2632-
dc.identifier.urihttp://hdl.handle.net/10722/337489-
dc.description.abstract<p>Rock classification provides vital information to geosciences and geological engineering practices. Reaping the benefits of the advances of computer vision-based deep learning artificial intelligence (AI) technology, this study aims to develop a next-generation convolutional neural network (CNN) to perform automatic rock classification. Two major challenging issues have been particularly addressed. First, most of the previous rock classifications are simply transfer learning of CNNs that are trained by life-like scenarios. Second, classifying rock types with similar textures leads to severe overfitting of CNNs. In this study, a novel CNN called HKUDES_Net is proposed and implemented to classify seven common Hong Kong rock types, namely fine-grained granite, medium-grained granite, coarse-grained granite, coarse ash tuff, fine ash tuff, feldsparphyric rhyolite, and granodiorite. With the aid of dynamic expansion, squeeze and excitation, and other strategies, HKUDES_Net can classify rock types with similar texture patterns/colors but different grain sizes. As compared with the other ten landmark CNNs and seven feature-based algorithms, HKUDES_Net has the best performance in precision (90.9%), recall (90.4%), and f1-score (90.5%). By implementing the alerting level, which restricts the training loss hovering above a small constant and prevents the validation loss from rising, the overfitting has been efficiently eliminated. The proposal and implementation of HKUDES_Net highlight the value of interdisciplinary research and will continuously pave the way for better coupling AI and geosciences.<br></p>-
dc.languageeng-
dc.publisherSpringer-
dc.relation.ispartofRock Mechanics and Rock Engineering-
dc.subjectAlerting level-
dc.subjectAutomatic rock classification-
dc.subjectConvolutional neural networks (CNNs)-
dc.subjectHKUDES_Net-
dc.titleNovel Rock Image Classification: The Proposal and Implementation of HKUDES_Net-
dc.typeArticle-
dc.identifier.doi10.1007/s00603-023-03235-0-
dc.identifier.scopuseid_2-s2.0-85147761044-
dc.identifier.volume56-
dc.identifier.issue5-
dc.identifier.spage3825-
dc.identifier.epage3841-
dc.identifier.eissn1434-453X-
dc.identifier.isiWOS:000932677000002-
dc.identifier.issnl0723-2632-

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