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....

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Vydané v:Marine and petroleum geology Ročník 167; s. 106965
Hlavní autori: Dawson, Harriet L., John, Cédric M.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.09.2024
Predmet:
Carbonates
Deep learning
Geological images
Machine learning
Object detection
Skeletal grains
Deep learning
Skeletal grains
Geological images
Carbonates
Machine learning
Object detection
ISSN:0264-8172, 1873-4073
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Popis
Shrnutí: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. •We present a deep learning framework to identify and quantify skeletal grains in carbonate cores.•Despite the current popularity of one-stage detectors in the wider computer science community, the two-stage detector provides the most robust performance for geological applications.•The general trend of fossil assemblages throughout the cores was visibly similar between machine learning and human frequencies on the same geologic material.•The general trends for nearly all groups are significantly similar and are obtained at a fraction of the time needed for a human to do the same work.
ISSN:0264-8172
1873-4073
DOI:10.1016/j.marpetgeo.2024.106965