Method to Address Brittle Rock Strength Scatter Through Fracture Density

Wang, Jie; Ma, Feifei

doi:10.1007/s00603-019-1736-x

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…With the development of deep learning algorithms in the field of intelligent image recognition, rock classification methods are no longer limited to physical test methods (Guo et al, 2010; Zijuan et al, 2018) and mathematical statistics methods (Fan et al, 2017; Mingjia et al, 2012). Many scholars apply deep learning methods to rock image recognition.…”

Section: Introductionmentioning

confidence: 99%

Deep learning‐based recognition method of red bed soft rock image

et al. 2023

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In order to improve the investigation efficiency, improve the traditional engineering investigation work mode, realize the intelligent investigation, and improve the economic and social benefits of enterprises, a red soft rock image intelligent analysis and recognition system is proposed based on deep learning methods. The intelligent recognition system includes two core algorithms: soft rock image decomposition and soft rock lithology/weathering degree recognition. The research shows that the identification model of weathering degree and the lithology identification model based on the convolutional neural network (CNN) algorithm have a good identification effect, and the identification probability of moderately weathered rock reaches 95.22% and the accuracy of lithology identification is 91.34%. With the increase in training data, the recognition effect will be further improved. The intelligent identification system has been integrated into the WeChat mini programme, App, and Web system, which can be directly applied to field geological survey operations to assist geological workers in the investigation work, and realize the intelligent identification and classification of red bed soft rock.

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Section: Introductionmentioning

confidence: 99%