Autonomous bed‐sediment imaging‐systems for revealing temporal variability of grain size

Abstract

Variation in nearshore bed-sediment grain sizeNearshore sediment transport determines the fate of seabed nutrients, contaminants, and pathogens; asserts control on the seabed and water column as habitats; and drives changes in seafloor topography which, in turn, affect wave transformation processes, spatial gradients in energy dissipation, and nearshore hydrodynamic circulation patterns. Relatively small changes in grain size have been shown to change the sign (depositional or erosional) of nearshore net sand transport rates (Ribberink and Chen 1993); affect the vertical grain-size distribution in suspension (McFetridge and Nielsen 1985); and the shape of suspended sediment concentration profiles (Conley et al. 2008). Laboratory experiments with graded beds simulating very high energy sheet-flow conditions show preferential transport of the coarse fractions in the mixture (e.g., van der Werf et al. 2006), and that the transport of each sizefraction is strongly influenced by the presence of other fractions (e.g., Wilcock 1988).Model calculations of suspended-sediment flux have been shown to become highly inaccurate within hours if the effects of variable bed-sediment grain-size are ignored, because waves and currents can modify the spatial distribution of seabed sediments in a variety of shelf settings over this time-scale (Harris and Wiberg 2002). However, advances in modeling grainsize sorting (spatial segregation) and its underlying selective transport mechanisms are hampered by few observations at sufficient coverage/frequency with which to compare theory. The result is that most nearshore (e.g., Bailard 1981; Larson and Kraus 1995) and regional shelf (e.g., Harris and Coleman 1998;Zhang et al. 1999;Cookman and Flemings 2001) models tend to oversimplify grain-size distribution effects on sediment transport because detailed observations of the behavior of a mixture of size fractions is lacking.A more complete understanding of the role of grain size in the physics of sediment transport requires the collection of grain-size data with more temporal and spatial coverage, and AbstractWe describe a remotely operated video microscope system, designed to provide high-resolution images of seabed sediments. Two versions were developed, which differ in how they raise the camera from the seabed. The first used hydraulics and the second used the energy associated with wave orbital motion. Images were analyzed using automated frequency-domain methods, which following a rigorous partially supervised quality control procedure, yielded estimates to within 20% of the true size as determined by on-screen manual measurements of grains. Long-term grain-size variability at a sandy inner shelf site offshore of Santa Cruz, California, USA, was investigated using the hydraulic system. Eighteen months of high frequency (min to h), high-resolution (μm) images were collected, and grain size distributions compiled. The data constitutes the longest known high-frequency record of seabed-grain size at this sample frequency, at any location....

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).…”

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

“…Four example 1024 × 1024 pixel subsets of images from each of six population categories. From top to bottom: (1) well‐sorted gravel; (2) well‐sorted sand and shell hash from underwater camera (described in Buscombe et al , ); (3) relatively poorly sorted gravel and sand–gravel mixtures (including imagery from Warrick et al , ); (4) well‐sorted sand; (5) miscellaneous terrigenous and volcaniclastic grains; and (6) miscellaneous bioclastic (carbonate) grains. [Colour figure can be viewed at wileyonlinelibrary.com]…”

Estimating the settling velocity of bioclastic sediment using common grain‐size analysis techniques

“…White areas in (C) are due to lack of coverage in the aerial imagery. analysis can produce GSDs within 4% of those measured by sieve and settling tube (Barnard et al, 2007;Gallagher et al, 2011;Buscombe et al, 2014). However, comparative studies in bioclastic environments have suggested that sieve can yield significantly larger mean grain-size estimates (up to 300%) than settling tube for coarse samples (mean >1 mm), smaller mean grain-size estimates in fine samples (mean <1 mm; Kench & McLean, 1997), and that the relationship between sieve and settling tube can be significantly non-linear (Smith & Cheung, 2002).…”

“…with variable shapes (rods, discs and plates) and densities (Maiklem, ; Braithwaite, ). Previous methodological comparisons using siliciclastic sediment have shown that sieve and settling tube analysis produce nearly identical GSDs when deposits are well‐sorted (Komar & Cui, ); sieve and laser diffraction analysis yield similar GSDs for sand‐sized material (Cheetham et al ., ; Di Stefano et al ., ); and image analysis can produce GSDs within 4% of those measured by sieve and settling tube (Barnard et al ., ; Gallagher et al ., ; Buscombe et al ., ). However, comparative studies in bioclastic environments have suggested that sieve can yield significantly larger mean grain‐size estimates (up to 300%) than settling tube for coarse samples (mean >1 mm), smaller mean grain‐size estimates in fine samples (mean <1 mm; Kench & McLean, ), and that the relationship between sieve and settling tube can be significantly non‐linear (Smith & Cheung, ).…”

“…Buscombe (2013) proposed a statistical method based on wavelet analysis that estimates the full GSD, without the need to isolate and segment each grain and without the need for site-specific calibration. To the authors' knowledge, this method has been tested only on siliciclastic sediment (Buscombe et al, 2014;King et al, 2016).…”