Refined lithology identification: Methodology, challenges and prospects

Shi, Heng; Xu, ZhenHao; Lin, Peng; Ma, Wen

doi:10.1016/j.geoen.2023.212382

Cited by 1 publication

(2 citation statements)

References 27 publications

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“…This model, which employs a semantic segmentation algorithm with a spatial pyramid module and an encoderdecoder architecture, represents a significant step forward in precise mineral recognition. Shi et al [12] introduced a classification model for fine-grained lithology identification, which was tested on 160 laboratory lithologies and 13 on-site lithologies. Achieving an F1 score of 0.9764 in lab and 0.6143 in field conditions, the study underscores the model's varying performance across different environments.…”

Section: Computer Vision-based Computational Methodsmentioning

confidence: 99%

“…Recent advancements in computer-assisted methods, particularly in neural networkbased lithological identification, have largely focused on coarse-grained hand specimen recognition or specific mineral identification under microscopes [10][11][12]. However, there remains a significant gap in the detailed classification and recognition of lithologies at a finer scale.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning for Refined Lithology Identification of Sandstone Microscopic Images

Wang,

Li,

Long

et al. 2024

Minerals

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Refined lithology identification is an essential task, often constrained by the subjectivity and low efficiency of classical methods. Computer-aided automatic identification, while useful, has seldom been specifically geared toward refined lithology identification. In this study, we introduce Rock-ViT, an innovative machine learning approach. Its architecture, enhanced with supervised contrastive loss and rooted in visual Transformer principles, markedly improves accuracy in identifying complex lithological patterns. To this end, we have collected public datasets and implemented data augmentation, aiming to validate our method using sandstone as a focal point. The results demonstrate that Rock-ViT achieves superior accuracy and effectiveness in the refined lithology identification of sandstone. Rock-ViT presents a new perspective and a feasible approach for detailed lithological analysis, offering fresh insights and innovative solutions in geological analysis.

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Section: Computer Vision-based Computational Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%