Deep Convolutions for In-Depth Automated Rock Typing

TL;DR

This paper introduces a convolutional neural network-based method to rapidly and accurately classify rock types from images, significantly reducing the time geologists spend on rock description tasks.

Contribution

It presents a novel application of CNN architectures for rock typing, comparing several models and achieving high accuracy and efficiency in core analysis.

Findings

GoogLeNet achieved up to 95% validation accuracy.

The best model can analyze 50 meters of core in one minute.

CNN-based methods outperform traditional color and feature analysis.

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.