Advances in the simulation and automated measurement of well‐sorted granular material: 2. Direct measures of particle properties

Abstract

[1] In this, the second of a pair of papers on the structure of well-sorted natural granular material (sediment), new methods are described for automated measurements from images of sediment, of: 1) particle-size standard deviation (arithmetic sorting) with and without apparent void fraction; and 2) mean particle size in material with void fraction. A variety of simulations of granular material are used for testing purposes, in addition to images of natural sediment. Simulations are also used to establish that the effects on automated particle sizing of grains visible through the interstices of the grains at the very surface of a granular material continue to a depth of approximately 4 grain diameters and that this is independent of mean particle size. Ensemble root-mean squared error between observed and estimated arithmetic sorting coefficients for 262 images of natural silts, sands and gravels (drawn from 8 populations) is 31%, which reduces to 27% if adjusted for bias (slope correction between observed and estimated values). These methods allow non-intrusive and fully automated measurements of surfaces of unconsolidated granular material. With no tunable parameters or empirically derived coefficients, they should be broadly universal in appropriate applications. However, empirical corrections may need to be applied for the most accurate results. Finally, analytical formulas are derived for the one-step pore-particle transition probability matrix, estimated from the image's autocorrelogram, from which void fraction of a section of granular material can be estimated directly. This model gives excellent predictions of bulk void fraction yet imperfect predictions of pore-particle transitions.Citation: Buscombe, D., and D. M. Rubin (2012), Advances in the simulation and automated measurement of well-sorted granular material: 2. Direct measures of particle properties,

Advances in the simulation and automated measurement of well‐sorted granular material: 1. Simulation

“…This is the first of a pair of papers (the second being Buscombe and Rubin [2012, hereinafter, part 2]) on the particle‐scale structure of natural granular material (sediment), in which a new approach to the three‐dimensional simulation of such materials has been developed principally in order to meet the needs of two expanding research areas, namely: 1) automated methods for in situ sampling of granular properties from images of sediment; and 2) the development of discrete particle models for investigating the physics of granular and fluid‐granular flows.…”

“…A final disadvantage of such an approach is that it inevitably introduces a fine bias in estimates of the population particle size statistics, by counting the partial axes of some particles. Therefore, some (as yet unknown) stereology is required which relates the size distribution of the axes of apparent particles comprising the surface to the actual distribution of diameters of those particles (this problem is considered further in part 2).…”

“…In this contribution we develop a new approach to the simulation of three‐dimensional populations of particles, packed into sediment beds. Both the individual particles and the bulk properties of the simulated granular material are realistic enough for application in the further development of automated estimation of particle properties, which is the subject of part 2. Therein, images of the planform of volumes of simulated particles aid the testing of new measurement techniques for the direct and completely automated estimate of the standard deviation of the distribution of particle sizes within an image of sediment.…”

Digital grain-size analysis based on autocorrelation algorithm

“…For its utility in field settings, especially in remote A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT 4 or inaccessible areas or long-term deployments, the new non-calibration method facilitates the development of a fully transferable method. In addition, the "autocorrelation" approach including subsequent wavelet method has been successful in obtaining grain-size distribution and associated parameters (Buscombe and Rubin, 2012;Buscombe, 2013;Buscombe et al, 2014).…”

“…Although Buscombe and Rubin (2012) explored a framework for the simulation of natural well-sorted granular material based on the principle of Voronoi tessellation, most former researchers generally focused on the mixed-size sediment on site, and for the well-sorted sediment detailed validation of autocorrelation method was scarce. However, well-sorted granular material is very common in nature, and the first step would be an appropriate estimation of its size characteristics before applying this technique to the mixed-size sediment case.…”