Furthering the investigation of eruption styles through quantitative shape analyses of volcanic ash particles

Nurfiani, Dini; Maisonneuve, C. Bouvet de

doi:10.1016/j.jvolgeores.2017.12.001

Cited by 38 publications

(30 citation statements)

References 50 publications

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“…Particle morphomics can be a powerful tool to analyze particle morphology, and provided information can aid in understanding of particle morphology-biogeochemical tracer interactions and improving the predictability and accuracy of sediment source tracing. Volcanic ash particles research . Volcanic ash particles carry a wealth of information on various processes occurring during volcanic eruptions, and their morphology has been recognized to reflect numerous eruptive parameters such as magma viscosity, volatile content, degree of interaction with external water, particle transport and sedimentation 3,29 . Many investigations of ash morphology have been carried out with multi-targets at unravelling the origin of ash and the related magma fragmentation dynamics 30 , determining the physical properties of magma and its degree of interaction with external water 31 , modeling the processes of ash dispersal and settling 32 , forecasting the continuation or ending of an eruptive episode 29 .…”

Section: Discussionmentioning

confidence: 99%

“…Volcanic ash particles carry a wealth of information on various processes occurring during volcanic eruptions, and their morphology has been recognized to reflect numerous eruptive parameters such as magma viscosity, volatile content, degree of interaction with external water, particle transport and sedimentation 3,29 . Many investigations of ash morphology have been carried out with multi-targets at unravelling the origin of ash and the related magma fragmentation dynamics 30 , determining the physical properties of magma and its degree of interaction with external water 31 , modeling the processes of ash dispersal and settling 32 , forecasting the continuation or ending of an eruptive episode 29 . The particle morphomics methodology outlined in this study can promote volcanic ash particles research to dredge up the intrinsic nature of morphology, e.g., diversity, backtrack the generation process and mechanism by linking the eruptive parameters, and predict the behavior and fate by linking the environmental conditions. Marine particle monitoring .…”

Section: Discussionmentioning

confidence: 99%

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Particle morphomics by high-throughput dynamic image analysis

Sun

2019

Sci Rep

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A novel omics-like method referred to as “particle morphomics” has been proposed in the present study. The dynamic images of >2,000,000 particles per sample in sediments, soils and dusts were collected by a Sympatec GmbH QICPIC particle size and shape analyzer, and the morphological descriptors of each particle including equivalent diameter, sphericity, aspect ratio and convexity were extracted as the “particle morphome”. Various multivariate analyses were adopted to process the high-throughput data of particle morphome including analyses of alpha and beta diversities, similarity, correlation, network, redundancy, discretion and principal coordinate. The outcome of particle morphomics could estimate the morphological diversity and sketch the profile of morphological structure, which aided to develop a morphological fingerprint for specific particle samples. The distribution and properties of particle assemblages of specific morphology could also be evaluated by selecting particles with respect to filter criteria. More importantly, the particle morphomics may be extended to investigate and explain the biogeochemical and environmental processes involved with particle morphology if linked with external variables.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Particle morphomics by high-throughput dynamic image analysis

Sun

2019

Sci Rep

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“…For each ash component, the method developed by Leibrandt and Le Pennec (2015) was used to characterize their projected 2-D morphology. Optimized morphometry of volcanic ash can yields crucial information on fragmentation processes during explosive activity (e.g., Büttner et al, 1999;Dellino et al, 2012;Heiken, 1974;Lautze et al, 2012;Liu et al, 2015;Maria & Carey, 2007;Nurfiani & Bouvet de Maisonneuve, 2018;Riley et al, 2003;Schmith et al, 2017;Sheridan & Marshall, 1983;Taddeucci et al, 2002;Wohletz, 1983). Several series of Apparent Projected Shape of Ash (APASH) on a very high number of ash particles were carried out using the automatized morpho-grainsizer Morphologi G3 of Malvern, able to disperse the ash sample on a glass slide and measure both the size and the morphology of the particles.…”

Section: Ash Morphologymentioning

confidence: 99%

Evidences of Plug Pressurization Enhancing Magma Fragmentation During the September 2016 Basaltic Eruption at Piton de la Fournaise (La Réunion Island, France)

Thivet

Gurioli

Muro

et al. 2020

Geochem Geophys Geosyst

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In September 2016, Piton de la Fournaise volcano, well known for its effusive and Hawaiian fountaining activity, produced, at the end of the eruption, an unusual phase of pulsating ash and bomb emission. Integration of geophysical data, with textural and petrological analysis of the samples, allowed us to constrain the main factors that controlled this sudden shift in activity, potentially dangerous for the tourist population that usually approach these “gentle” eruptive sites. Volcanic tremor, lava discharge rates, fountain heights, and SO2 emission changed rapidly during the eruption. Grain size and componentry of the tephra beds evolved from unimodal all along the sequence to bimodal on the last day of the activity, reflecting the contribution of both Hawaiian fountaining at the main vent (Vent A) and transient explosive activity at the second vent (Vent B). Hawaiian fountaining produced highly vesicular and almost microlite‐free tephra (golden pumice and fluidal scoria) while transient explosive activity emitted denser and crystal‐rich tephra (sideromelane and tachylite scoria) sometimes mingled with vesicular fragments. Permeability measurements on lapilli and bomb‐sized samples reveal that golden pumice and fluidal scoria were more gas‐permeable than the sideromelane and tachylite ones, while textural and chemical analyses of the ash support the hypothesis that these sideromelane and tachylite components were inherited from the subsurface crystallization of the initial golden pumice and fluidal scoria components. We thus suggest that Vent B accumulated a plug of degassed, cooled, and low‐permeable magma, which modulated overpressure pulses under the late input of ascending magma.

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“…In order to solve this problem, objective methods using shape parameters have been used 4 – 14 . Shape parameters are defined to quantitatively represent ash shape by combining geometric values such as perimeter and area 8 – 14 . Once the parameters are defined, the shape of an ash particle can be classified by calculating the parameters in an objective way that is free from human bias.…”

Section: Introductionmentioning

confidence: 99%

“…Although instruments such as the Morphologi can calculate parameters automatically, we still have to choose the shape parameters to be used. Thus far, many shape parameters, including fractal dimension, have been used for better understanding and classifying ash shapes 8 – 14 . While quantitative analysis is objective once the parameters are decided, defining and selecting effective parameters is difficult.…”

Section: Introductionmentioning

confidence: 99%

Classification of volcanic ash particles using a convolutional neural network and probability

Dake

Noguchi

Otsuki

et al. 2018

Sci Rep

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Analyses of volcanic ash are typically performed either by qualitatively classifying ash particles by eye or by quantitatively parameterizing its shape and texture. While complex shapes can be classified through qualitative analyses, the results are subjective due to the difficulty of categorizing complex shapes into a single class. Although quantitative analyses are objective, selection of shape parameters is required. Here, we applied a convolutional neural network (CNN) for the classification of volcanic ash. First, we defined four basal particle shapes (blocky, vesicular, elongated, rounded) generated by different eruption mechanisms (e.g., brittle fragmentation), and then trained the CNN using particles composed of only one basal shape. The CNN could recognize the basal shapes with over 90% accuracy. Using the trained network, we classified ash particles composed of multiple basal shapes based on the output of the network, which can be interpreted as a mixing ratio of the four basal shapes. Clustering of samples by the averaged probabilities and the intensity is consistent with the eruption type. The mixing ratio output by the CNN can be used to quantitatively classify complex shapes in nature without categorizing forcibly and without the need for shape parameters, which may lead to a new taxonomy.

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Furthering the investigation of eruption styles through quantitative shape analyses of volcanic ash particles

Cited by 38 publications

References 50 publications

Particle morphomics by high-throughput dynamic image analysis

Particle morphomics by high-throughput dynamic image analysis

Evidences of Plug Pressurization Enhancing Magma Fragmentation During the September 2016 Basaltic Eruption at Piton de la Fournaise (La Réunion Island, France)

Classification of volcanic ash particles using a convolutional neural network and probability

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