Grain shape plays an important role in textural analysis of sedimentary grains. Textural analysis helps to determine the formation, transportation and deposition processes of sedimentary rocks. However, there is a lack of standardized methodology for quantitative characterization of grain shapes. The utility of fully automated image analysis for grain shape measurement is assessed in this paper. This research aimed to identify the most useful shape parameters for textural characterization of populations of grains and determine the relative importance of the parameters. A key aspect of this study is to determine whether, in a particular sedimentary environment, textural maturity of the samples can be ranked based on their grain shape data. Furthermore, discrimination of sedimentary depositional environments is explored on the basis of grain shape. In this study, 20 loose sediment samples from four known depositional environments (beach, aeolian, glacial and fluvial) were analysed using newly implemented automatic image analysis methods. For each sample, a set of 11 shape parameters were calculated for 200 grains. The data demonstrate a progression in textural maturity in terms of roundness, angularity, irregularity, fractal dimension, convexity, solidity and rectangularity. Furthermore, statistical analysis provides strong support for significant differences between samples grouped by environment and generates a ranking consistent with trends in maturity. Based on novel application of machine learning algorithms, angularity and fractal dimension are found to be the two most important parameters in texturally classifying a grain. The results of this study indicate that textural maturity is readily categorized using automated grain shape parameter analysis. However, it is not possible to absolutely discriminate between different depositional environments on the basis of shape parameters alone. This work opens up the possibility of detailed studies of the relationship between textural maturity and sedimentary environment, which may be more complicated than previously considered.

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The characterisation of particle shape is an important analysis in the field of sedimentary geology. At finer scales, it is key for understanding sediment transport while at coarser scales, such as boulders, it is vital for coastal protection. However, the accurate characterisation of particle shape is restricted by the application of 2D imaging for 3D objects or expensive and time-consuming 3D imaging methods such as X-ray tomography or laser scanning. This research outlines a low-cost, easy-to-use 3D particle imaging and shape characterisation methodology employing structure-from-motion (SfM) photogrammetry. A smartphone device was used to capture 2D images of pebble/cobble-sized samples, which were converted to 3D image models using SfM. The 3D image models were then analysed using a comprehensive set of 16 size and shape parameters. Furthermore, a minimum resolution, independent of particle size, is proposed here for the 3D image models for reliable and reproducible size and shape analysis. Thus, the methodology presented here for 3D particle imaging and size and shape analysis can be translated for a range of particle sizes. This work thus opens a pathway for the use of readily accessible imaging devices, such as smartphones, to flexibly obtain image data both in situ as well as in laboratories, thus providing an immensely powerful tool for research and teaching.