Rock-type classification of an iron ore deposit using digital image analysis technique

Chatterjee, Snehamoy; Bhattacherjee, Ashis; Samanta, Biswanath; Pal, Samir Kumar

doi:10.1504/ijmme.2008.020455

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Cited by 8 publications

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“…In works by Chatterjee et al (Chatterjee, 2013;Chatterjee et al, 2010Chatterjee et al, , 2008Patel et al, 2017aPatel et al, , 2017bPatel et al, , 2016 crushed rocks from different mines were analyzed. Each rock was separately segmented from the image.…”

Section: Introductionmentioning

confidence: 99%

Deep convolutions for in-depth automated rock typing

Baraboshkin

Ismailova

Orlov

et al. 2020

Computers & Geosciences

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The description of rocks is one of the most time-consuming tasks in the everyday work of a geologist, especially when very accurate description is required. We here present a method that reduces the time needed for accurate description of rocks, enabling the geologist to work more efficiently. We describe the application of methods based on color distribution analysis and feature extraction. Then we focus on a new approach, used by us, which is based on convolutional neural networks. We used several well-known neural network architectures (AlexNet, VGG, GoogLeNet, ResNet) and made a comparison of their performance. The precision of the algorithms is up to 95% on the validation set with GoogLeNet architecture. The best of the proposed algorithms can describe 50 m of full-size core in one minute.

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