Deep Convolutions for In-Depth Automated Rock Typing

09/23/2019

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by   E. E. Baraboshkin, et al.

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One of the most time-consuming tasks in everyday geologist work is a description of rocks, especially when very accurate description should be done. Here we present the method which helps to maximize the efficiency of geologist and reduce the time for rocks description. We describe the application of methods based on color distribution analysis and feature extraction, as well as the new approach 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 proposed algorithms can describe the 50 m of full-size core in an automated mode in a minute.