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• W4291750984 abstract "PreviousNext No AccessSecond International Meeting for Applied Geoscience & EnergyUsing deep learning for automatic detection and segmentation of carbonate microtexturesAuthors: Claire BirnieViswasanthi ChandraClaire BirnieKAUSTSearch for more papers by this author and Viswasanthi ChandraKAUSTSearch for more papers by this authorhttps://doi.org/10.1190/image2022-3751305.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractThe difficulties involved in studying micrometer-sized micrite crystals, and quantifying the associated impact on large scale geophysical properties, have long hindered our society’s understanding of both Middle Eastern and global microporous limestones. Instance segmentation procedures, from the field of deep learning, offer the ability to identify at a pixel-level each individual crystal within an SEM image, allowing for automated morphological analysis. We illustrate how the common Masked Region-based Convolution Neural Network from computer vision can be adapted to the task of identifying individual micrite crystal within gray-scale SEM images. Leveraging Transfer Learning, the ResNet50 neural architecture is used with weights initialized through a pre-training on Microsoft’s Common Objects in COntext (COCO) dataset. The resulting model accurately detects and separates a number of crystals observed within different SEM images. However the trained model is also shown to be highly susceptible to noise introduced as part of the imaging procedure, for example charging noise. Future work will aim to make the procedure more robust, reducing the impact of noise by adapting the pre-processing workflow and incorporating more noisy images into the training dataset.Keywords: machine learning, SEM analysis, thin sections, deep learning, instance segmentationPermalink: https://doi.org/10.1190/image2022-3751305.1FiguresReferencesRelatedDetails Second International Meeting for Applied Geoscience & EnergyISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2022 Pages: 3694 publication data© 2022 Published in electronic format with permission by the Society of Exploration Geophysicists and the American Association of Petroleum GeologistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 15 Aug 2022 CITATION INFORMATION Claire Birnie and Viswasanthi Chandra, (2022), Using deep learning for automatic detection and segmentation of carbonate microtextures, SEG Technical Program Expanded Abstracts : 376-380. https://doi.org/10.1190/image2022-3751305.1 Plain-Language Summary Keywordsmachine learningSEM analysisthin sectionsdeep learninginstance segmentationPDF DownloadLoading ..." @default.
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• W4291750984 title "Using deep learning for automatic detection and segmentation of carbonate microtextures" @default.
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