Autoren:	Reinhardt, Marcel Jacob, Arne Sadeghnejad, Saeid Cappuccio, Francesco Arnold, Pit Frank, Sascha Enzmann, Frieder Kersten, Michael
Titel:	Benchmarking conventional and machine learning segmentation techniques for digital rock physics analysis of fractured rocks
Online-Publikationsdatum:	4-Aug-2022
Erscheinungsdatum:	2022
Sprache des Dokuments:	Englisch
Zusammenfassung/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-Sachgruppe:	550 Geowissenschaften 550 Earth sciences
Veröffentlichende Institution:	Johannes Gutenberg-Universität Mainz
Organisationseinheit:	FB 09 Chemie, Pharmazie u. Geowissensch.
Veröffentlichungsort:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-7486
Version:	Published version
Publikationstyp:	Zeitschriftenaufsatz
Nutzungsrechte:	CC BY
Informationen zu den Nutzungsrechten:	https://creativecommons.org/licenses/by/4.0/
Zeitschrift:	Environmental earth sciences 81
Seitenzahl oder Artikelnummer:	71
Verlag:	Springer
Verlagsort:	Berlin
Erscheinungsdatum:	2022
ISSN:	1866-6299
DOI der Originalveröffentlichung:	10.1007/s12665-021-10133-7
Enthalten in den Sammlungen:	DFG-491381577-H