Using deep learning for automatic detection and segmentation of carbonate microtextures

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The 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.
Original languageEnglish (US)
Title of host publicationSecond International Meeting for Applied Geoscience & Energy
PublisherSociety of Exploration Geophysicists and American Association of Petroleum Geologists
DOIs
StatePublished - Aug 15 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-09-14
Acknowledgements: For computer time, this research used the resources of the Supercomputing Laboratory at King Abdullah University of Science & Technology (KAUST) in Thuwal, Saudi Arabia.

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