A transferable method for the automated grain sizing of river gravels

Abstract

The spatial and temporal resolution of surface grain‐size characterization is constrained by the limitations of traditional measurement techniques. In this paper we present an extremely rapid image‐processing‐based procedure for the measurement of exposed fluvial gravels and other coarse‐grained sediments, defining the steps required to minimize the errors in the derived grain‐size distribution. This procedure differs significantly from those used previously. It is based around a robust object‐detection algorithm that produces excellent results on images exhibiting a wide range of sedimentary conditions, crucially, without any user intervention or site‐specific parameterization. The procedure is tested using a data set comprising 39 images from three rivers with contrasting grain lithology, shape, roundness, and packing configuration and representing a very wide range of textures. It is shown to perform more consistently than the best existing automated method, achieving a precision equivalent to that obtainable by Wolman sampling, but taking between one sixth and one twentieth of the time. The error in area‐by‐number grain‐size distribution percentiles is typically less than 0.05 ?.