Quartz grain microtexture analysis using Artificial Intelligence: application to tsunami and storm deposits provenance studies

Quartz grain surface microtextures observed by scanning electron microscopy (SEM) provide important information on sediment transport history, depositional processes and sediment provenance. Traditionally, the interpretation of these features has relied upon qualitative visual assessment—an approach deeply rooted in expert judgement and cumulative experience. While fundamental, this methodology is inherently susceptible to subjectivity and inter-analyst variability. To counter balance this problem, we explore image-based classification approaches (utilizing Deep Learning frameworks) as a tool to support quartz microtextural analysis and assist in the identification of likely depositional environments thus establishing sediment provenance relationships.

A dataset of 3 367 SEM images was compiled, spanning a diverse range of sedimentary contexts: aeolian dunes, beach faces’, alluvial systems, basal sands, and nearshore, alongside with high-energy deposits from storm and tsunami events. Based on this dataset, five classification models were developed. Three were designed to discriminate between the full set of seven depositional classes, while two focused on a reduced classification scheme comprising four classes (alluvial, beach, dune and nearshore). All models were optimised using an increasing number of training epochs to assess the stability and evolution of classification performance. The results obtained were further examined in comparison with SandAI, an existing tool for microtexture classification, to evaluate its behaviour when applied to new sedimentary contexts and datasets acquired under different conditions.

The most consistent classification results were obtained for environments characterised by well-preserved and distinctive mechanical microtextures (e.g. aeolian sediments). Conversely, while environments defined by overlapping processes occasionally yielded higher nominal accuracies in QzTexNet (CNN-based models developed within the scope of this work), this is potentially attributed to their over-representation in the dataset. Analysis of classification outcomes indicates that microtextural overprinting, dataset imbalance and variations in image quality reduced the visibility of diagnostic features, thereby complicating the differentiation of depositional settings. Nevertheless, the data suggests that our models successfully capture sedimentologically meaningful patterns when surface textures remain clear. While SandAI showed stable performance within its original scope, its accuracy was limited, peaking at 47% for its target environments and dropping significantly when faced with complex deposits like tsunami or nearshore grains. In contrast, the newly developed QzTexNet models showed slightly more encouraging results, reaching accuracies of around 55% and demonstrating a steady improvement through successive refinements.

Ultimately, these findings demonstrate that automated classification offers a powerful complement to traditional analysis, particularly in ensuring reproducibility across large-scale datasets. Solely based on our database, it was observed that challenges regarding dataset equilibrium and textural complexity persist, targeted methodological refinements and supervised training hold significant potential. Such advancements represent a promising frontier in sedimentary provenance studies, particularly for the rigorous identification of deposits linked to extreme geological events.

This work is supported by FCT, I.P./MCTES through national funds (PIDDAC): LA/P/0068/2020 (https://doi.org/10.54499/LA/P/0068/2020), UID/50019/2025(https://doi.org /10.54499/UID/PRR/50019/2025), UID/PRR2/50019/2025). Finally this work is a contribution to project iCoast (project 14796 COMPETE2030-FEDER-00930000).