Fully Automated Carbonate Petrography Using Deep Convolutional Neural Networks

Carbonate rocks are important archives of past ocean conditions as well as hosts of economic resources such as hydrocarbons, water, and minerals. Geologists typically perform compositional analysis of grain, matrix, cement and pore types in order to interpret depositional environments, diagenetic modification, and reservoir quality of carbonate strata. Such information can be obtained primarily from petrographic analysis, a task that is costly, labor-intensive, and requires in-depth knowledge of carbonate petrology and micropaleontology. Recent studies have leveraged machine learning-based image analysis, including Deep Convolutional Neural N...  more

Carbonate rocks are important archives of past ocean conditions as well as hosts of economic resources such as hydrocarbons, water, and minerals. Geologists typically perform compositional analysis of grain, matrix, cement and pore types in order to interpret depositional environments, diagenetic modification, and reservoir quality of carbonate strata. Such information can be obtained primarily from petrographic analysis, a task that is costly, labor-intensive, and requires in-depth knowledge of carbonate petrology and micropaleontology. Recent studies have leveraged machine learning-based image analysis, including Deep Convolutional Neural Networks (DCNN), to automate description, classification and interpretation of thin sections, subsurface core images and seismic facies, which would accelerate data acquisition and reproducibility for these tasks. In carbonate rocks, this approach has been applied primarily to recognize carbonate lithofacies, and no attempt has been made to individually identify and quantify various types of carbonate grains, matrix, and cement. In this study, the applicability and performance of DCNN-based object detection and image classification approaches are assessed with respect to carbonate compositional analysis. The training data comprised of more than 13,000 individually labeled objects from nearly 4000 carbonate petrographic images. The dataset is grouped into six and nine different classes for the image classification and object detection tasks, respectively. Even with a small and relatively imbalanced training set, the DCNN was able to achieve an F1 score of 92% for image classification and 84% mean precision for object detection by combining one-cycle policy, class weight, and label mixup-smoothing methods. This study highlights the inefficiency of image classification as an approach to replicating human description and classification of carbonate petrography. By contrast, DCNN-based object detection appears capable of approaching human speed and accuracy in the area of carbonate petrography because it is able to individually locate and identify different carbonate components with greater cost-efficiency, speed, and reproducibility than conventional (human) petrographic analysis.