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
Highlights
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
The British Geological Survey (BGS) regularly provides ‘stonematching’ advice to this end, and possesses a baseline of sandstone quarry samples and thin sections geared towards this purpose
The direct benefit that a computationally derived similarity measure could deliver for sandstone ‘stone matching’ and the availability of a well-established baseline dataset well suited for image analysis provided the impetus for the study we describe in this paper
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. It may be able to augment or even replace manual interpretation, offering potentially huge advantages, when applied to large sample suites and image datasets. Some examples of such applications in the geosciences include; correlating strata across boreholes, defining regional lithological variations between sandstone units, and monitoring the consistency of a quarry’s output. If such a system can be designed and proven to be successful for these tasks, it will have the potential to reduce errors, and drastically improve consistency and robustness, when compared to standard manual interpretation methods, when they may be completed by various teams of geologists. An approach that did not require the pre-selection of parameters was sought
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Quarterly Journal of Engineering Geology and Hydrogeology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
