Deep Convolutions for In-Depth Automated Rock Typing
This work addresses a domain-specific problem for geologists by improving efficiency in rock typing, though it is incremental as it uses existing neural network architectures.
The paper tackles the time-consuming task of accurate rock description for geologists by applying convolutional neural networks, achieving up to 95% precision with GoogLeNet and enabling description of 50 meters of core in one minute.
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.