Processes controlling the shape of ash particles: Results of statistical IPA

Jordan, Simone Christiane; Dürig, Tobias; Fernand, Raymond Alexander; Zimanowski, Bernd

doi:10.1016/j.jvolgeores.2014.09.012

Cited by 32 publications

(21 citation statements)

References 23 publications

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“…Particle diameter, for example, has been variably defined as the Feret diameter (D MaxFeret ; maximum distance between two parallel lines tangential to the particle outline), Form and roughness [2,19,20,28,32,44,48,49,65,73] Sphericity [1,7,56,69] Roundness [27,50,54] Circularity [9,18,35] Form and roughness [10,[21][22][23]25,39,47,[58][59][60] Shape factor [36]; Particle irregularity [57] Solidity SLD…”

Section: Area Of the Particle A Chmentioning

confidence: 99%

“…Aspect ratio AR [35,49] Aspect ratio [19,28,54,56,66,71,76] Ellipse aspect ratio [18] Ellipticity [54] Elongation El [47] Dmax f eret MIP [10,22,25,39,58,60] LG E [65] log 2 ( A B ) [44,54] Ap Dmax f eret [17] Roundness RD …”

Section: Pch Ppmentioning

confidence: 99%

“…[17]), different origins (e.g. [22,23,39,47,49,51,[58][59][60]) and different aerodynamic properties (e.g. [4,19,21,52,68,69]).…”

Section: Volcanological Applications Of Computed Shape Analysismentioning

confidence: 99%

“…These diagnostic fields have been widely applied to infer the origin of ash deposits (e.g. [39,59,60]), although the extent to which these shape parameter thresholds are translatable between deposits of different grain size, textural or compositional characteristics has not been addressed.…”

Section: Volcanological Applications Of Computed Shape Analysismentioning

confidence: 99%

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Optimising shape analysis to quantify volcanic ash morphology

Liu¹,

Cashman

Rust³

2015

GeoResJ

126

180

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a b s t r a c tAccurate measurements of volcanic ash morphology are critical to improving both our understanding of fragmentation processes and our ability to predict particle behaviour. In this study, we present new ways to choose and apply shape parameters relevant to volcanic ash characterisation. First, we compare shape measurements from different imaging techniques, including cross-sectional (2-D) and projected area images, and discuss their respective applications. We then focus on specific information that can be obtained from shape analysis of 2-D images. Using cluster analysis as an unbiased method to identify key controls on particle morphology, we find that four shape parameters -solidity, convexity, axial ratio, and form factor -can effectively account for the morphological variance within most ash samples. Importantly, these parameters are scaled to values between 0 and 1, and therefore contribute evenly to discrimination diagrams. In particular, co-variation in convexity and solidity can be used to distinguish different juvenile ash components based on characteristic bubble properties. By reducing observations of natural samples to simplified ash geometries, we quantify morphological changes associated with variations in the relative size and shape of bubbles and particles. Using this relationship, we assess the potential application of size-dependent shape analysis for inferring the underlying bubble size distribution, and thus the pre-fragmentation conditions. Finally, we show that particle shape analysis that includes the full range of available grain sizes can contribute not only measurements of particle size and shape, but also information on size-dependent densities.

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Section: Area Of the Particle A Chmentioning

confidence: 99%

Section: Pch Ppmentioning

confidence: 99%

“…[17]), different origins (e.g. [22,23,39,47,49,51,[58][59][60]) and different aerodynamic properties (e.g. [4,19,21,52,68,69]).…”

Section: Volcanological Applications Of Computed Shape Analysismentioning

confidence: 99%

Section: Volcanological Applications Of Computed Shape Analysismentioning

confidence: 99%

See 2 more Smart Citations

Optimising shape analysis to quantify volcanic ash morphology

Liu¹,

Cashman

Rust³

2015

GeoResJ

126

180

View full text Add to dashboard Cite

show abstract

“…Through 3D textural investigations we are able to view structures that are the result of strain localization [Wright and Wimberg 2009, Shields et al 2014, deformation [Okumura et al 2010;Caricchi et al 2011, Ashwell et al 2015, crystal aggregation and crystal fragmentation [Pamukcu et al 2012], convection , Carey et al 2013] and development of permeability [Bai et al 2010, Degruyter et al 2010b, Bai et al 2011, Kendrick et al 2013, Ashwell et al 2015 in magma, which can be related to experiments, and which in the near future could feed into experimental and numerical modeling. Finally, information on fragmentation mechanisms (the terms "phreatic", "hydrothermal", and "hydromagmatic" are cause of great amount of discussion and debate about their meaning and use) can, in part, be assessed from particle morphology [Dellino et al 2012, Jordan et al 2014, Liu et al 2015b, while TGSD provide information on fragmentation efficiency [Kueppers et al 2006, Rust and Cashman 2011, Costa et al 2016. However, particle morphology alone is not a proxy for the fragmentation mechanism; the textural state rather shows the state of magma upon quenching, following fragmentation.…”

Section: From Magma Ascentmentioning

confidence: 99%