Petrographic microfacies classification with deep convolutional neural networks. (September 2020) - Pires de Lima, Rafael; Duarte, David; Nicholson, Charles; Slatt, Roger; Marfurt, Kurt J.
Abstract: Petrographic analysis is based on the microscopic description and classification of rocks and is a crucial technique for sedimentary and diagenetic studies. When compared to hand specimens, thin sections provide better and more accurate means for analysis of mineral proportion, distribution, texture, pore space analysis, and cement composition. Most petrographic analysis relies on visual inspection of rock thin sections under a microscope, a task that is laborious even for experienced geologists. Large projects with a tight time frame requiring the analysis of a large amount of thin sections may require multiple petrographers, thereby risking the introduction of inconsistency in the analysis. To address this challenge, we explore the use of deep convolutional neural networks (CNN) as a tool for acceleration and automatization of microfacies classification. We make use of transfer learning based on robust and reliable CNN models trained with a large amount of non-geological images. With a relatively small number of labeled thin sections used in "fine-tuning" training we are able to adapt CNN models that achieve low error levels (<5%) for the classification of microfacies from the same dataset, and moderate results (<40%) for the classification of microfacies of thin sections from different datasets. These alternate datasets differ from the training data on two independent factors: the thin sections are from different formations and are prepared by different laboratories. While becoming widely accepted as a useful tool in the biological and manufacturing disciplines, CNN is currently underutilized in the geoscience community; we foresee an increase of use of such techniques to help accelerate and quantify a wide variety of geological tasks. Highlights: Workflow for microfacies classification with convolutional neural networks. Methodology can be used to quickly organize large amounts of thin section images. High accuracy (>95%) for several tests performed (using data from same source). Accuracy decreases when model is used to classify data processed by different labs.