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

Buscombe, Daniel; Rubin, David M.

doi:10.1029/2011jf001974

Cited by 6 publications

(21 citation statements)

References 62 publications

(99 reference statements)

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“…This is the second of a pair of papers (the first being Buscombe and Rubin [2012, hereinafter part 1]) which addresses the particle‐scale structure of natural granular material. The principal objective here is to provide simple optical techniques for the non‐intrusive, calibration‐free and automated measurement of some ensemble geometric properties of granular material in a digital image.…”

Section: Introductionmentioning

confidence: 99%

“…The principal objective here is to provide simple optical techniques for the non‐intrusive, calibration‐free and automated measurement of some ensemble geometric properties of granular material in a digital image. Part 1 presented an approach for simulating realistic volumes of granular material. We use various outputs from this model, as well as images of real sediment from a variety of sedimentary populations, to develop and test methods for the standard deviation of particle sizes (arithmetic sorting), for sediment both with and without a visible and quantifiable areal (termed ‘apparent’) void fraction.…”

Section: Introductionmentioning

confidence: 99%

“…In a plan view image of a granular material, as the geometric projection of a 3D surface onto a 2D plane, particles overlap (parts of particles are sitting on top of others, wholly or partially obscuring those underneath). There are no visible voids, only visible surface and parts of subsurface particles (see part 1 for a more detailed discussion). The terminology used in this paper for such surfaces of sediment is a granular material without apparent void fraction.…”

Section: Introductionmentioning

confidence: 99%

“…Only simulated material can produce large population sets quickly, and with a range of combinations of particle size, particle shape, packing, porosity and other macroscopic (particle‐scale or larger) properties. Section 3 provides a brief summary of how the generalized modeling approach presented in part 1 was used to create sediment surfaces with which to test the algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…In section 5 we present an analytical solution for void to particle transition probabilities in a 2D section through a two‐phase granular material which can be used to estimate the apparent void fraction using only the image's autocorrelation function, the plot of autocorrelation coefficient as a function of lag displacement, hereafter ‘autocorrelogram’. The method is tested using 2D sections (slices) through some simulated granular materials, again generated using the methods in part 1. Finally, the uncertainties and implications of this work are discussed (section 6) before conclusions are drawn (section 7) .…”

Section: Introductionmentioning

confidence: 99%

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

Buscombe

Rubin

2012

J. Geophys. Res.

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[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,

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Buscombe

Rubin

2012

J. Geophys. Res.

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SediNet: a configurable deep learning model for mixed qualitative and quantitative optical granulometry

Buscombe

2019

Earth Surf Processes Landf

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I describe a configurable machine‐learning framework to estimate a suite of continuous and categorical sedimentological properties from photographic imagery of sediment, and to exemplify how machine learning can be a powerful and flexible tool for automated quantitative and qualitative measurements from remotely sensed imagery. The model is tested on a dataset consisting of 409 images and associated detailed label data. The data are from a much wider sedimentological spectrum than previous optical granulometry studies, consisting of both well‐ and poorly sorted sediment, terrigenous, carbonate, and volcaniclastic sands and gravels and their mixtures, and grain sizes spanning over two orders of magnitude. I demonstrate the model framework by configuring it in several ways, to estimate two categories (describing grain shape and population, respectively) and nine numeric grain size percentiles in pixels from a single input image. Grain size is then recovered using the physical size of a pixel. Finally, I demonstrate that the model can be configured and trained to estimate equivalent sieve diameters directly from image features, without the need for area‐to‐mass conversion formulas and without even knowing the scale of one pixel. Thus it is the only optical granulometry method proposed to date that does not necessarily require image scaling. The flexibility of the model framework should facilitate numerous application in the spatiotemporal monitoring of the grain size distribution, shape, mineralogy and other quantities of interest of sedimentary deposits as they evolve, as well as other texture‐based proxies extracted from remotely sensed imagery. © 2019 John Wiley & Sons, Ltd.

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Transferable wavelet method for grain‐size distribution from images of sediment surfaces and thin sections, and other natural granular patterns

2013

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In images of sedimentary or granular material, or simulations of binary (two-phase) granular media, in which the individual grains are resolved, the complete size distribution of apparent grain axes is well-approximated by the global power spectral density function derived using a Morlet wavelet. This approach overcomes many limitations of previous automated methods for estimating the grain-size distribution from images, all of which rely on either: identification and segmentation of individual grains; calibration and/or relatively large sample sizes. The new method presented here is tested using: (i) various types of simulations of two-phase media with a size distribution, with and without preferred orientation; (ii) 300 sample images drawn from 46 populations of sands and gravels from around the world, displaying a wide variability in origin (biogenic and mineralogical), size, surface texture and shape; (iii) petrographic thin section samples from nine populations of sedimentary rock; (iv) high-resolution scans of marine sediment cores; and (v) nonsedimentary natural granular patterns including sea ice and patterned ground. The grain-size distribution obtained is equivalent to the distribution of apparent intermediate grain diameters, grid by number style. For images containing sufficient well-resolved grains, root mean square errors are within tens of percent for percentiles across the entire grain-size distribution. As such, this method is the first of its type which is completely transferable, unmodified, without calibration, for both consolidated and unconsolidated sediment, isotropic and anisotropic two-phase media, and even non-sedimentary granular patterns. The success of the wavelet approach is due, in part, to it quantifying both spectral and spatial information from the sediment image simultaneously, something which no previously developed technique is able to do.

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

Cited by 6 publications

References 62 publications

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: 2. Direct measures of particle properties

SediNet: a configurable deep learning model for mixed qualitative and quantitative optical granulometry

Transferable wavelet method for grain‐size distribution from images of sediment surfaces and thin sections, and other natural granular patterns

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