Impact of dataset size and convolutional neural network architecture on transfer learning for carbonate rock classification

Modern geological practices, in both industry and academia, rely largely on a legacy of observational data at a range of scales. However, widespread ambiguities in the petrographic description of rock facies reduce the reliability of descriptive data. Previous studies have demonstrated a great potential for the use of convolutional neural networks (CNNs) in the classification of facies from digital images; however, it remains to be determined which of the available CNN architectures performs best for a geological classification task. We evaluate the ability of top-performing CNNs to classify carbonate core images using transfer learning, systematically developing a performance comparison between these architectures on a complex geological dataset. Three datasets with orders of magnitude difference in data quantity (7000–104,000 samples) were created that contain images across seven classes from the modified Dunham Classification for carbonate rocks. Following training of nine different CNNs of four architectures on these datasets, we find the Inception-v3 architecture to be most suited to this classification task, achieving 92% accuracy when trained on the larger dataset. Furthermore, we show that even when using transfer learning the size of the dataset plays a key role in the performance of the models, with those trained on the smaller datasets showing a strong tendency to overfit. This has direct implications for the application of deep learning in geosciences as many papers currently published use very small datasets of less than 5000 samples. Application of the framework developed in this research could aid the future of deep learning based carbonate classification, with further potential to be easily modified to suit the classification of cores originating from different formations and lithologies

Machine learning applications for geoscience problems

Geoscientists have used machine learning for at least three decades and the applications spam many fields, from seismic processing and interpretation, to remote sensing classification, to analysis of well log data, among many others. More popular in some fields (e.g. seismic interpretation, remote sensing analysis) than others (e.g. paleontology), machine learning tools can leverage research in different areas of geoscience. Although machine learning is becoming more popular in different fields of geoscience, some concepts of more modern applications, convolutional neural networks in particular, are still vaguely understood by non-practitioners. I present some of the key concepts of machine learning with more details on the foundations of convolutional neural networks and some techniques that can help better understand convolutional neural networks behavior. I then present five case studies, mostly using convolutional neural networks and transfer learning. Transfer learning is a methodology that allow us to repurpose filters created by convolutional neural networks on a primary task to perform a secondary task. The five case studies start with a broader application of convolutional neural networks for different geoscience images, including thin-sections and core photographs. Then I present a how to perform core classification using convolutional neural networks. Next, how microfossils can be classified by the same methodology. I present a more detailed analysis of transfer learning using different remote sensing datasets. In the final case study, I show applications of supervised learning techniques to help forecast Megaelectron-Volt electrons inside Earth’s outer radiation belt. I conclude the dissertation with a summary and comments on the expectation of future research

Object detection algorithms to identify skeletal components in carbonate cores

Identification of constituent grains in carbonate rocks requires specialist experience. A carbonate sedimentologist must be able to distinguish between skeletal grains that change through geological ages, preserved in differing alteration stages, and cut in random orientations across core sections. Recent studies have demonstrated the effectiveness of machine learning in classifying lithofacies from thin section, core, and seismic images, with faster analysis times and reduction of natural biases. In this study, we explore the application and limitations of convolutional neural network (CNN) based object detection frameworks to identify and quantify multiple types of carbonate grains within close-up core images of carbonate lithologies. We compiled nearly 400 images of high-resolution core images from three ODP and IODP expeditions. Over 9000 individual carbonate components of 11 different classes were manually labelled from this dataset. Using pre-trained weights, a transfer learning approach was applied to evaluate one-stage (YOLO v5) and two-stage (Faster R–CNN) detectors under different feature extractors (CSP-Darknet53 and ResNet50-FPN, respectively). Despite the current popularity of one-stage detectors, our results show Faster R–CNN with ResNet50-FPN backbone provides the most robust performance, achieving 0.73 mean average precision (mAP). Furthermore, we extend the approach by deploying the trained model to two ODP sites from Leg 194 that were not part of the training set (ODP Sites 1196 and 1199), providing a performance comparison with benchmark human interpretation

Automated Analysis of Drill-Core Images Using Convolutional Neural Network

Drill cores provide geological and geotechnical information essential for mineral and hydrocarbon exploration. Modern core scanners can automatically produce a large number of high-resolution core-tray images or unwrapped-core images, which encode important rock properties, such as lithology and geological structures. Current core-image analysis methods, however, are based on outdated algorithms that lack generalization and robustness. In addition, current methods focus on using log data while core images often provide more reliable information about the subsurface formations. With the new era of technology and the evolution of big data and artificial intelligence, core images will be an important asset for subsurface characterization. The future of core analysis, driven by the digital archiving of cores, needs to be considered since the manual core description and its extensive time and labor requirements are outdated. This dissertation aims to lay the foundation of a ‘Digital Geologist’ using advanced machine learning algorithms. It develops and evaluates intelligent workflows using Convolutional Neural Networks (CNNs) to automate core-image analysis, and thus facilitate the evaluation of natural resources. It explores the feasibility of extracting different rock features from core images. First, advanced CNNs are utilized to predict major lithologies of rocks from core-tray images and an overall workflow is optimized for lithology prediction. Second, a CNN is created to assess the physical condition of cores and determine intact core sections to calculate the rock quality designation (RQD) index, which is essential in many geotechnical applications. Third, an innovative approach is developed to extract fractures from unwrapped-core images and determine fracture depth and orientation. The workflow is based on using a state-of-the-art CNN model for instance segmentation, the Mask Region-based Convolutional Neural Network (Mask R-CNN). Lastly, fracture analysis from unwrapped-core images is further studied to obtain more detailed characteristics represented by fracture apertures. Overall, the thesis proposes a transformed workflow of core-image analysis that can be a platform for future studies with potential application in the mining and petroleum industries

Using AI tools to fill an incomplete well log dataset: A workflow

One issue commonly found when working with well log data is the irregular abundance/availability of the different recorded parameters. This is especially applicable when working with datasets collected in different campaigns that may span through the years, even decades, or different companies. Artificial Intelligence may be useful to fill gaps in the original database, resulting in a more complete, standardised one. In this work we present a workflow that can be followed to fills gaps in a dataset using different AI techniques. It consists of four main steps: 1) feature combination selection; 2) hyperparameter tuning; 3) performance assessment and best option choice; 4) blind testing. The process can be performed iteratively, successively populating the database with missing parameters, starting with those for which there are more available training data and whose results are more reliable. In this work, we present an example in which we filled an incomplete dataset consisting of wells provided by the UK National Data Repository (NDR) of the Oil & Gas Authority (OGA). The performance of some of the most commonly used artificial intelligence methods (support vector machine, random forest, multi-layer perceptron) was tested varying their hyperparameters until reaching an adequate result