Machine learning applied to pore-space geometry in sandstones: a tool for evaluating grain-scale similarity?

Hall, Alexander; Gillespie, Martin; Everett, Paul; Christodoulou, Vyron; Walsh, Jo. 2022 Machine learning applied to pore-space geometry in sandstones: a tool for evaluating grain-scale similarity? Quarterly Journal of Engineering Geology and Hydrogeology, 55 (1), qjegh2020-183. https://doi.org/10.1144/qjegh2020-183

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Official URL: http://dx.doi.org/10.1144/qjegh2020-183

Abstract/Summary

The ability to identify similar sandstones to a given sample is important where the provenance of the sample is unknown or the quarry of origin is no longer in operation. In the case of building stones from heritage buildings in protected areas, it may be mandatory. Here, a proof of concept for an automated similarity measure is presented by means of a convolutional autoencoder that is able to extract features from a sample thin section and use these features to identify the most similar sample in an existing image library. The approach considers only the shape of the pore space between grains, as, if the pore space alone contains enough information to distinguish between samples, the required image pre-processing and training of a model is greatly simplified. The trained model is able to predict correctly the progenitor quarry of a thin section, from an eight-class dataset of Scottish sandstones, with an accuracy of 47.9%. This prototype, although insufficient for commercial purposes, forms a benchmark for future models against which improvements can be assessed and some of which are suggested.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1144/qjegh2020-183
ISSN:	1470-9236
Date made live:	23 Nov 2021 14:00 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/531422

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1144/qjegh2020-183

ISSN:

1470-9236

Date made live:

23 Nov 2021 14:00 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/531422