A Deep Learning Approach of RQD Analysis for Rock Core Images via Cascade Mask R-CNN-Based Model-Reference-Cited by-同舟云学术

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A Deep Learning Approach of RQD Analysis for Rock Core Images via Cascade Mask R-CNN-Based Model

Published:2024-08-15 Issue: Volume: Page:
ISSN:0723-2632
Container-title:Rock Mechanics and Rock Engineering
language:en
Short-container-title:Rock Mech Rock Eng

Author:

Zhang Ye,Ma Yunlin,Li Yanlong,Wen Lifeng

Funder

National Natural Science Foundation of China

National Science Fund for Distinguished Young Scholars

Natural Science Basic Research Program of Shaanxi Province

National key R&D plan

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00603-024-04100-4.pdf

Reference51 articles.

1. Alzubaidi F, Mostaghimi P, Swietojanski P et al (2021) Automated lithology classification from drill core images using convolutional neural networks. J Petrol Sci Eng 197:107933

2. Alzubaidi F, Makuluni P, Clark SR et al (2022a) Automatic fracture detection and characterization from unwrapped drill-core images using mask R-CNN. J Petrol Sci Eng 208:109471

3. Alzubaidi F, Mostaghimi P, Si G et al (2022b) Automated rock quality designation using convolutional neural networks. Rock Mech Rock Eng 55(6):3719–3734

4. Amankwah A, Aldrich C (2011) Automatic ore image segmentation using mean shift and watershed transform. In: Proceedings of 21st international conference radioelektronika 2011, Brno, Czech Republic. IEEE, New York, pp 1–4

5. Andrä H, Combaret N, Dvorkin J et al (2013) Digital rock physics benchmarks—part I: imaging and segmentation. Comput Geosci 50:25–32

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