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- W3201993819 abstract "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. Thematic collection: This article is part of the Digitization and Digitalization in engineering geology and hydrogeology collection available at: https://www.lyellcollection.org/cc/digitization-and-digitalization-in-engineering-geology-and-hydrogeology" @default.
- W3201993819 created "2021-10-11" @default.
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- W3201993819 date "2021-09-29" @default.
- W3201993819 modified "2023-09-27" @default.
- W3201993819 title "Machine learning applied to pore-space geometry in sandstones: a tool for evaluating grain-scale similarity?" @default.
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- W3201993819 doi "https://doi.org/10.1144/qjegh2020-183" @default.
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