Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-7486
Authors: Reinhardt, Marcel
Jacob, Arne
Sadeghnejad, Saeid
Cappuccio, Francesco
Arnold, Pit
Frank, Sascha
Enzmann, Frieder
Kersten, Michael
Title: Benchmarking conventional and machine learning segmentation techniques for digital rock physics analysis of fractured rocks
Online publication date: 4-Aug-2022
Year of first publication: 2022
Language: english
Abstract: Image segmentation remains the most critical step in Digital Rock Physics (DRP) workflows, affecting the analysis of physical rock properties. Conventional segmentation techniques struggle with numerous image artifacts and user bias, which lead to considerable uncertainty. This study evaluates the advantages of using the random forest (RF) algorithm for the segmentation of fractured rocks. The segmentation quality is discussed and compared with two conventional image processing methods (thresholding-based and watershed algorithm) and an encoder–decoder network in the form of convolutional neural networks (CNNs). The segmented images of the RF method were used as the ground truth for CNN training. The images of two fractured rock samples are acquired by X-ray computed tomography scanning (XCT). The skeletonized 3D images are calculated, providing information about the mean mechanical aperture and roughness. The porosity, permeability, flow fields, and preferred flow paths of segmented images are analyzed by the DRP approach. Moreover, the breakthrough curves obtained from tracer injection experiments are used as ground truth to evaluate the segmentation quality of each method. The results show that the conventional methods overestimate the fracture aperture. Both machine learning approaches show promising segmentation results and handle all artifacts and complexities without any prior CT-image filtering. However, the RF implementation has superior inherent advantages over CNN. This method is resource-saving (e.g., quickly trained), does not need an extensive training dataset, and can provide the segmentation uncertainty as a measure for evaluating the segmentation quality. The considerable variation in computed rock properties highlights the importance of choosing an appropriate segmentation method.
DDC: 550 Geowissenschaften
550 Earth sciences
Institution: Johannes Gutenberg-Universität Mainz
Department: FB 09 Chemie, Pharmazie u. Geowissensch.
Place: Mainz
ROR: https://ror.org/023b0x485
DOI: http://doi.org/10.25358/openscience-7486
Version: Published version
Publication type: Zeitschriftenaufsatz
License: CC BY
Information on rights of use: https://creativecommons.org/licenses/by/4.0/
Journal: Environmental earth sciences
81
Pages or article number: 71
Publisher: Springer
Publisher place: Berlin
Issue date: 2022
ISSN: 1866-6299
Publisher DOI: 10.1007/s12665-021-10133-7
Appears in collections:JGU-Publikationen

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