#6372. MudrockNet: Semantic segmentation of mudrock SEM images through deep learning

October 2026publication date
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Journal’s subject area:
Computers in Earth Sciences;
Information Systems;
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Abstract:
Segmentation and analysis of individual pores and grains of mudrocks from scanning electron microscope images is non-trivial because of imaging artifacts, variation in pixel grayscale values across images, and overlaps in grayscale values among different physical features such as silt grains, clay grains and pores, which make identifications difficult. Moreover, because grains and pores often have overlapping grayscale values, direct application of threshold-based segmentation techniques is not sufficient. Recent advances in the field of computer vision have made it easier and faster to segment images and identify multiple occurrences of such features in an image, provided that ground-truth data for training the algorithm are available. Here we propose a deep learning SEM image segmentation model, MudrockNet based on Googles DeepLab-v3+ architecture implemented with the TensorFlow library.
Keywords:
Convolutional neural network; Deep learning; Digital rock physics; Image analysis; Image segmentation; Machine learning; Mudrocks; Neural networks

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