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Novel Rock Image Classification: The Proposal and Implementation of HKUDES_Net

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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.

Highlights

  • A next-generation convolutional neural network (CNN) called HKUDES_Net is developed to perform automatic rock classification.

  • HKUDES_Net can classify rock types with similar texture patterns/colors but different grain sizes.

  • By implementing the alerting level in HKUDES_Net, the overfitting has been efficiently eliminated.

Plain Language Summary

Rock classification provides vital information to geosciences and geological engineering practices. However, the traditional way of rock classification is to a certain degree experience-based. Computer vision-based artificial intelligence (AI), especially convolutional neural network (CNN), a sub-branch of AI, may help people automatically classify rock types with the hope to train AI models (CNNs), which can instantly and precisely perceive rock images similar to how human beings observe rocks. In other words, by putting a rock image into a CNN, the CNN will read the information from the image and tell people what rock type in the image is. This study develops and implements a next-generation CNN called HKUDES_Net to classify seven different rock types. Adopting different computational strategies, HKUDES_Net can classify rock types of different grain sizes with similar texture patterns/colors, which is challenging for other CNNs. As compared with the other landmark CNNs and machine learning algorithms, HKUDES_Net has much a better prediction accuracy performance. The proposal and implementation of HKUDES_Net exemplify an interdisciplinary research study, and we are confident that this paper will be instrumental to pave the way for further coupling AI and geosciences.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

The first author acknowledges the Postgraduate Scholarship from the University of Hong Kong. Mr Hui Zong, PhD candidate at Tongji University in China and Mr Yuhan Ping, PhD candidate at Department of Computer Science, the University of Hong Kong, are also acknowledged for discussing the architecture of the proposed HKUDES_Net in great detail. The authors also acknowledge Mr. Frankie Lo and his colleagues from GEO for constructive discussions on data collection and CNN training plans involved in this study. The rock image database is not publicly available at present, and it will be made open source at a later stage. Other data related to the present study are available online (at https://doi.org/10.6084/m9.figshare.21768689.v1).

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  1. Department of Earth Sciences, The University of Hong Kong, Hong Kong, China

    Yimeng Zhou, Louis Ngai Yuen Wong & Keith Ki Chun Tse

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Correspondence to Louis Ngai Yuen Wong.

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Zhou, Y., Wong, L.N.Y. & Tse, K.K.C. Novel Rock Image Classification: The Proposal and Implementation of HKUDES_Net. Rock Mech Rock Eng 56, 3825–3841 (2023). https://doi.org/10.1007/s00603-023-03235-0

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