scholarly journals Review comments to Eaton et al. (2019): Estimating confidence intervals for gravel bed surface grain size distributions by K. Bunte.

2019 ◽  
Author(s):  
Kristin Bunte
Keyword(s):  
Grain Size ◽  
Confidence Intervals ◽  
Size Distributions ◽  
Gravel Bed ◽  
Grain Size Distributions

scholarly journals Uniform grain-size distribution in the active layer of a shallow, gravel-bed, braided river (the Urumqi River, China) and implications for paleo-hydrology

2018 ◽  
Author(s):  
Laure Guerit ◽  
Laurie Barrier ◽  
Youcun Liu ◽  
Clément Narteau ◽  
Eric Lajeunesse ◽  
...  
Keyword(s):  
Grain Size ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Active Layer ◽  
Size Distributions ◽  
Gravel Bed ◽  
Surface Samples ◽  
River Bed ◽  
Braided Stream ◽  
Grain Size Distributions

Abstract. The grain-size distribution of ancient alluvial systems is commonly determined from surface samples of vertically exposed sections of gravel deposits. This method relies on the hypothesis that the grain-size distribution obtained from a vertical cross-section is equivalent to that of the river bed. We report a field test of this hypothesis on samples collected on an active, gravel-bed, braided stream: the Urumqi River in China. We compare data from volumetric samples of a trench excavated in an active thread and surface counts performed on the trench vertical faces. We show that the grain-size distributions obtained from all samples are similar and that the deposit is uniform at the scale of the river active layer, a layer extending from the surface to a depth of approximately ten times the size of the largest clasts.

scholarly journals Introducing <i>PebbleCounts</i>: a grain-sizing tool for photo surveys of dynamic gravel-bed rivers

2019 ◽  
Vol 7 (3) ◽  
pp. 859-877 ◽  
Author(s):  
Benjamin Purinton ◽  
Bodo Bookhagen
Keyword(s):  
Grain Size ◽  
High Energy ◽  
High Mountain ◽  
Size Distributions ◽  
Mountain Rivers ◽  
Gravel Bed ◽  
Automated Method ◽  
Grain Size Distributions ◽  
Manual Selection ◽  
Gravel Bed Rivers

Abstract. Grain-size distributions are a key geomorphic metric of gravel-bed rivers. Traditional measurement methods include manual counting or photo sieving, but these are achievable only at the 1–10 m2 scale. With the advent of drones and increasingly high-resolution cameras, we can now generate orthoimagery over hectares at millimeter to centimeter resolution. These scales, along with the complexity of high-mountain rivers, necessitate different approaches for photo sieving. As opposed to other image segmentation methods that use a watershed approach, our open-source algorithm, PebbleCounts, relies on k-means clustering in the spatial and spectral domain and rapid manual selection of well-delineated grains. This improves grain-size estimates for complex riverbed imagery, without post-processing. We also develop a fully automated method, PebbleCountsAuto, that relies on edge detection and filtering suspect grains, without the k-means clustering or manual selection steps. The algorithms are tested in controlled indoor conditions on three arrays of pebbles and then applied to 12 × 1 m2 orthomosaic clips of high-energy mountain rivers collected with a camera-on-mast setup (akin to a low-flying drone). A 20-pixel b-axis length lower truncation is necessary for attaining accurate grain-size distributions. For the k-means PebbleCounts approach, average percentile bias and precision are 0.03 and 0.09 ψ, respectively, for ∼1.16 mm pixel−1 images, and 0.07 and 0.05 ψ for one 0.32 mm pixel−1 image. The automatic approach has higher bias and precision of 0.13 and 0.15 ψ, respectively, for ∼1.16 mm pixel−1 images, but similar values of −0.06 and 0.05 ψ for one 0.32 mm pixel−1 image. For the automatic approach, only at best 70 % of the grains are correct identifications, and typically around 50 %. PebbleCounts operates most effectively at the 1 m2 patch scale, where it can be applied in ∼5–10 min on many patches to acquire accurate grain-size data over 10–100 m2 areas. These data can be used to validate PebbleCountsAuto, which may be applied at the scale of entire survey sites (102–104 m2). We synthesize results and recommend best practices for image collection, orthomosaic generation, and grain-size measurement using both algorithms.

scholarly journals Introducing PebbleCounts: A grain-sizing tool for photo surveys of dynamic gravel-bed rivers

2019 ◽  
Author(s):  
Benjamin Purinton ◽  
Bodo Bookhagen
Keyword(s):  
Grain Size ◽  
High Energy ◽  
Size Distributions ◽  
Mountain Rivers ◽  
Gravel Bed ◽  
Automated Method ◽  
Grain Size Distributions ◽  
Manual Selection ◽  
Pixel Image ◽  
Gravel Bed Rivers

Abstract. Grain-size distributions are a key geomorphic metric of gravel-bed rivers. Traditional measurement methods include manual counting or photo sieving, but these are achievable only at the 1–10 m2 scale. With the advent of unmanned aerial vehicles and increasingly high-resolution cameras, we can now generate orthoimagery over hectares at sub-cm resolution. These scales, along with the complexity of high-mountain rivers, necessitate different approaches for photo sieving. As opposed to other image segmentation methods that use a watershed approach to automatically segment entire images, our open-source algorithm, PebbleCounts, relies on k-means clustering in the spatial and spectral domain and rapid manual selection of well-delineated grains. The result is improved grain-size estimates for complex river-bed imagery, without any post processing. In a second step, we develop a fully automated method, PebbleCountsAuto, that relies on edge detection and filtering suspect grains, without the k-means clustering or manual selection steps. The algorithms are tested in controlled indoor conditions on three arrays of pebbles and then applied to 12 × 1 m2 orthomosaic clips of high-energy mountain rivers collected with a camera-on-mast setup (akin to a low-flying drone). A 20-pixel b-axis length lower truncation is necessary for attaining accurate grain-size distributions. For the k-means PebbleCounts approach, average percentile bias and precision are 0.03 and 0.09 ψ, respectively, for ~ 1.16 mm/pixel images, and 0.07 and 0.05 ψ for one 0.32 mm/pixel image. The automatic approach has higher bias and precision of 0.13 and 0.15 ψ, respectively, for ~ 1.16 mm/pixel images, but similar values of −0.06 and 0.05 ψ for one 0.32 mm/pixel image. For the automatic approach, only at best 70 % of the grains are correct identifications, and typically around 50 %. PebbleCounts operates most effectively at the 1 m2 scale, where the algorithm can be rapidly applied in ~ 5 minutes in many small areas to acquire accurate grain-size data over 10–100 m2 areas. These data can be used to validate PebbleCountsAuto applied at the scale of entire survey sites (102–104 m2). We synthesize results and recommend best practices for image collection, orthomosaic generation, and grain-size measurement using both algorithms.

scholarly journals Effect of Strain-Induced Precipitation on the Recrystallization Kinetics in a Model Alloy

2021 ◽  
Author(s):  
Mo Ji ◽  
Martin Strangwood ◽  
Claire Davis
Keyword(s):  
Grain Size ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Avrami Exponent ◽  
Model Alloy ◽  
Size Distributions ◽  
Recrystallization Kinetics ◽  
Grain Size Distributions ◽  
Recrystallized Grain Size ◽  
Nb Addition

AbstractThe effects of Nb addition on the recrystallization kinetics and the recrystallized grain size distribution after cold deformation were investigated by using Fe-30Ni and Fe-30Ni-0.044 wt pct Nb steel with comparable starting grain size distributions. The samples were deformed to 0.3 strain at room temperature followed by annealing at 950 °C to 850 °C for various times; the microstructural evolution and the grain size distribution of non- and fully recrystallized samples were characterized, along with the strain-induced precipitates (SIPs) and their size and volume fraction evolution. It was found that Nb addition has little effect on recrystallized grain size distribution, whereas Nb precipitation kinetics (SIP size and number density) affects the recrystallization Avrami exponent depending on the annealing temperature. Faster precipitation coarsening rates at high temperature (950 °C to 900 °C) led to slower recrystallization kinetics but no change on Avrami exponent, despite precipitation occurring before recrystallization. Whereas a slower precipitation coarsening rate at 850 °C gave fine-sized strain-induced precipitates that were effective in reducing the recrystallization Avrami exponent after 50 pct of recrystallization. Both solute drag and precipitation pinning effects have been added onto the JMAK model to account the effect of Nb content on recrystallization Avrami exponent for samples with large grain size distributions.

Grain Growth in Nanocrystalline Nickel

1999 ◽  
Vol 580 ◽  
Author(s):  
G.D. Hibbard ◽  
U. Erb ◽  
K.T. Aust ◽  
G. Palumbo
Keyword(s):  
Thermal Stability ◽  
Grain Size ◽  
Grain Growth ◽  
Size Distributions ◽  
Nanocrystalline Nickel ◽  
Scanning Calorimetry ◽  
Solute Content ◽  
Grain Size Distributions ◽  
Thermal Stability Of ◽  
Total Solute Content

AbstractIn this study, the effect of grain size distribution on the thermal stability of electrodeposited nanocrystalline nickel was investigated by pre-annealing material such that a limited amount of abnormal grain growth was introduced. This work was done in an effort to understand the previously reported, unexpected effect, of increasing thermal stability with decreasing grain size seen in some nanocrystalline systems. Pre-annealing produced a range of grain size distributions in materials with relatively unchanged crystallographic texture and total solute content. Subsequent thermal analysis of the pre-annealed samples by differential scanning calorimetry showed that the activation energy of further grain growth was unchanged from the as-deposited nanocrystalline nickel.

Holocene sediment transport and climate variability of offshore Adélie Land, East Antarctica

2021 ◽  
Author(s):  
◽  
Anna Borisovna Albot
Keyword(s):  
Grain Size ◽  
Sea Ice ◽  
Ross Sea ◽  
Size Distributions ◽  
Accumulation Rates ◽  
Glacial Meltwater ◽  
The Core ◽  
Grain Size Distributions ◽  
The Antarctic ◽  
Mass Accumulation Rates

<p>Grain size analysis of the terrigenous fraction of a laminated diatom ooze dating back to 11.4 kyr recovered offshore Adélie Land, East Antarctic margin was used to examine variations in sediment transport, depositional environments and Holocene climate variability at the location. Interpretations were assisted by additional proxies of primary productivity (δ¹³CFA, BSi%), glacial meltwater input (δDFA) and subsurface temperature (TEXL₈₆). Three lithologic intervals with distinct grain size distributions were identified. At ~11.4 ka the diatom ooze has a clear glacimarine influence which gradually decreases until ~8.2 ka. During this time interval, coincident with the early Holocene warm period, sediment is inferred to have been delivered by glacial meltwater plumes and ice-bergs in a calving bay environment. It is suggested that the glaciers in Adélie Land had retreated to their present day grounding lines by 8.2 ka, and from then on sediment was delivered to the site primarily via the Antarctic Coastal and Slope Front Currents, largely through a suspended sediment load and erosion of the surrounding banks. Enhanced biogenic mass accumulation rates and primary production at 8.2 ka suggest onset of warmer climatic conditions, coincident with the mid-Holocene Climatic Optimum.  At ~4.5 ka, grain size distributions show a rapid increase in mud content coincident with a transient pulse of glacial meltwater and a sudden decrease in biogenic and terrigenous mass accumulation rates. The increased mud content is inferred to have been deposited under a reduced flow regime of the Antarctic Coastal and Slope Front Currents during the Neoglacial period that followed the final stages of deglaciation in the Ross Sea. It is hypothesised here that cessation of glacial retreat in the Ross Sea and the development of the modern day Ross Sea polynya resulted in enhanced Antarctic Surface Water production which led to increased sea ice growth in the Adélie Land region. The presence of sea ice led to reduced primary production and a decrease in the maximum current strength acting to advect coarser-sized terrigenous sediment to the core site during this time.  Sedimentation rates appear to have a strong correlation with the El Niño Southern Oscillation (ENSO) over the last 8.2 kyr, and are inferred to be related to changing sea ice extent and zonal wind strength. Light laminae counts (biogenic bloom events) appear to decrease in frequency during time intervals dominated by El Niño events. Spectral analysis of the greyscale values of core photographs reveals peaks in the 2-7 year band, known ENSO periods, which increase in frequency in the mid-and-late Holocene. Spectral analyses of the sand percent and natural gamma ray (NGR, a measure of clay mineral input) content of the core reveal peaks in the ~40-60, 200-300, 600, 1200-1600 and 2200-2400 year bands. The most significant of these cycles in the NGR data is in 40-60 year band may be related to internal mass balance dynamics of the Mertz Glacier or to the expansion and contraction of the Antarctic circumpolar vortex. Cycles in the 200-300 and 2200-2400 year bands are related to known periods of solar variability, which have previously been found to regulate primary productivity in Antarctic coastal waters. Cycles in the 590-625 and 1200-1600 year bands have a strong signal through the entire record and are common features of Holocene climatic records, however the origin of these cycles is still under debate between solar forcing and an independent mode of internal ocean oscillation.</p>

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