Deep Convolutions for In-Depth Automated Rock Typing

Abstract

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.

arXiv comment25 pages, 9 figures, 3 tables, submitted to Computers and Geosciences Journal. Keywords: Core Image; Description; Convolutional Neural Networks; Representation; Geology; Lithotypes

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Cited by in corpus (3)

• DeePore: a deep learning workflow for rapid and comprehensive characterization of porous materials
• Automatic identification of fossils and abiotic grains during carbonate microfacies analysis using deep convolutional neural networks
• Core Box Image Recognition and its Improvement with a New Augmentation Technique