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Interpreting convexity and solidity using simplified geometries. (a) Synthetic ash shapes of equivalent area varying the number (green symbols), size (blue symbols), size and number (red symbols) or shape (orange symbols) of perimeter concavities. Note that digitisation of a curved outline results in values slightly < 1 for a fully compact circle; this effect is minimised by a high pixel density (square = 57,600 pxls/p; circle = 45425 pxls/p). (b) As (a), but with the fields of different ash samples from Fig. 8a (dashed lines) and Fig. 8b (shaded) superimposed for comparison. The shaded regions correspond to shards (red), vesicular particles (green), dense fragments (blue), and microcrystalline vesicular particles (orange). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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Accurate 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 tec...
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... intended to be a fully comprehensive analysis (for which sample sizes of ≥ 10 3 particles and information on the componentry as- semblage, including non-juvenile material, would be required), these preliminary results are consistent with gradual abrasion through low-intensity collisions, and therefore a dominance of comminu- tion processes over disruption and brittle breakage in this example [26]. Fractal analysis of the same data set also suggests that particle- particle interaction decreased the fine-scale perimeter complexity of individual particles [51]. Example 2. The clustergrams for EY2010 and G2011 samples show that, again, the affinity of FF varies between SLD and CVX (Fig. A1, supplementary information), suggesting that same SLD–CVX discrimination diagram is likely to be useful. Each ash particle within these two datasets has been manually classified as belonging to one of five components – glassy dense, glassy vesicular, glassy shard, microcrystalline vesicular, or microcrystalline dense – based on their external morphology and internal crystal and bubble textures. Fig. 8b shows that ash particles belonging to each component occupy distinct fields of the SLD–CVX diagram, and that corresponding components from EY2010 and G2011 overlap. Dense, vesicle-poor fragments (both glassy and microcrystalline; shown in blue) resemble ‘submarine’ glassy fragments from Fig. 8a, and accordingly have similar high values of solidity and convexity. ‘Bubbly’ grains of varying vesicularity (including glassy shards and glassy/microcrystalline vesicular particles) have consistently lower convexity values (CVX < 0.8), but, importantly, exhibit a much wider range of solidities (0.1 < SLD < 0.9). In detail, glassy shards comprise the lowest measured solidities (SLD < 0.6; shown in red), whilst vesicular grains – both glassy (green) and microcrystalline (orange) – are typically more compact (SLD > 0.6). Shards and vesicular particles can therefore be differentiated using solidity measurements, based on quantifiable differences in the size of concavities relative to the particle size. Lastly, microcrystalline vesicular particles form a distinctive cluster, characterised by very low convexity and high solidity (Fig. 8b). The presence of irregular, polylobate vesicles, which are often deformed around crystal boundaries, lengthens the particle perimeter considerably relative to the fully convex shape, whilst maintaining very compact forms. Compared to the reference dataset in Fig. 8a, ash particles from the 91–125 μ m size fraction of G2011 and EY2010 span a much broader range of shape parameter values. In particular, the range in solidity has more than doubled, reflecting greater variability in the size of perimeter-intersecting concavities relative to that of the particle. This is largely an effect of the difference in grain size class used between Fig. 8a and b, which will be explored further in Section 5.3. Whilst the range of particle sizes in the reference dataset from Maria and Carey [[51]; 1–2 φ or 250–500 μ m] are significantly larger than the size of constituent concavities (i.e., vesicles), the smaller particle sizes (3– 3.5 φ or 91–125 μ m; [49]) analysed for EY2010 and G2011 approach and overlap the distribution of vesicle sizes. Importantly, this obser- vation highlights the need to consider the interplay between grain size and bubbles size in controlling SP measurements of volcanic ash, particularly when selecting grain size class(es) for analysis (Section 5.3). Bubbles are an important control on ash particle morphology, particularly in determining their surface characteristics [49,51,56,57,68,75]. In 2-D, the intersection of vesicles with the exterior surfaces of ash particles produces concavities in the particle outline. For particles of a given size, the fraction of the total surface area composed of vesicle concavities will be controlled by the size and spatial distribution of bubbles in the melt prior to fragmentation (e.g. [49,56,68]). To examine further the relation between bubble size, abundance, particle size and particle shape parameters, we have created a series of synthetic ash particles comprising either squares or circles (of equal bubble-free area). We then systematically vary the size and abundance of perimeter-intersecting vesicles, and plot these synthetic ash particles on a SLD–CVX diagram (Fig. 10) for direct comparison with Fig. ...
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... summarise, the spatial distribution of particles on a SLD– CVX diagram is determined by the size, shape and abundance of perimeter-concavities. Morphological trends observed in natural ash samples can be reproduced using simplified synthetic ash shapes, whereby different ash ‘components’ can be described quantitatively in terms of their perimeter concavities. With some knowledge of what is controlling particle shape (e.g., vesicles), shape parameters can therefore be linked directly to specific morphologies. The synthetic ash shapes shown in Fig. 10 highlight the effect of concavity size on solidity; as the sizes of intersecting bubbles increase relative to the particle size, the difference in area between the particle and its convex hull increases accordingly. Physically, a solidity value of 0.5 corresponds to 50% of the convex hull area occupied by perimeter concavities. To place quantitative constraints on the relationship between ash particle shape and size, we consider the simplified geometry of circular bubbles intersecting square/circular particles (where the particle represents the interstice between two or more bubbles). We derive dimensionless formulae for solidity as a function of particle size, particle shape (squares and circles), bubble size and the number of bubbles (Fig. 11). We assume that the intersecting bubbles are (1) perfectly circular, (2) cut at their maximum 2-D cross-section, and (3) centred on the particle perimeter. When bubbles intersect square particles, solidity varies ...
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... Pumice clasts of sizes between 250 and 500 µm were cleaned under acetone to remove organic and adhering material and using an ultrasonic bath for no longer than 3-5 min to preserve the original morphological properties of the glass (e.g., Heiken and Wohletz 1985;Ersoy et al. 2006;Liu et al. 2015). The samples were dried overnight in an oven at ~ 60 °C and mounted in stainless-steel stubs using doublesided stick tape. ...
Hasandağ volcano (Central Anatolia, Turkey) has recently underwent an increase in local seismicity and fumarolic activity since 2013. In the past, this volcano has produced multiple large explosive eruptions during the last million years. The Belbaşhanı Pumice is the product of a sub-Plinian to Plinian eruption dated at ~ 417 ± 20.5 ka ( ⁴⁰ Ar/ ³⁹ Ar). Here, we present a complete volcanological study including stratigraphy, glass chemistry, pumice morphology, geochronology, and eruption source parameters with the associated uncertainties, to characterize the Belbaşhanı Pumice eruption. The eruption involved a column of 18–29 km in height, with the main dispersal axis towards the northeast. A pumice layer up to ~ 17-m-thick accumulated in proximal deposits along the Belbaşhanı path, and up to 2-m-thick in medial-distal areas (~ 18 km northeast from the vent). The high and tubular vesicularity of the pumice clasts indicates that the Belbaşhanı eruption was predominantly magmatic. The bulk volume of the Belbaşhanı Pumice fallout deposit has been estimated as 0.5 and 8 km ³ (with ~ 2 km ³ being the mean value), which corresponds to Volcanic Explosivity Index (VEI) of at least 4 and up to 6. Both isopach and isopleth maps indicate that the volcanic vent may have been located at the intersection of the Tuz Gölü fault and Ulukışla caldera, within the Hasandağ volcanic complex. The glass composition of Belbaşhanı Pumice confirms that the eruption belongs to the Hasandağ magmatic system. The reconstruction of the Belbaşhanı Pumice eruption represents an essential baseline in providing volcanological constraints for further investigations of tephra fallout hazard assessment in Central Anatolia, especially considering that a new Plinian eruption cannot be ruled out at Hasandağ volcano in the future. The chemical and geochronological datasets presented here could aid in refining tephrochronological correlations, with the goal of synchronizing paleoenvironmental and paleoclimatic records alongside archaeological sites.
... where P refers to the 2D perimeter of the sulfide and A refers to the 2D area of the sulfide. Form Factor therefore provides an efficient single descriptor of globule 'irregularity' as it accounts for both surface roughness and elongation 102,103 . We find that the FF distribution for sulfides is heavily skewed towards high values-that is, very smooth, circular outlines-with mode of FF = 0.7-0.8 ( Supplementary Fig. 4). ...
... We imaged individual sulfide globules at a magnification that yielded~10 5 pixels/sulfide (at a resolution of 1024 × 798 and 10 µs integration time) to ensure that uncertainties on sulfide area and shape measurements are independent of size. BSE images were thresholded using ImageJ software (http://imagej.nih.gov/ij/) and analysed using an open-source shape macro for ImageJ 103,168 . ...
Mafic magmas may experience multiple stages of sulfide saturation and resorption during ascent and differentiation. Quenched tephra erupted during the 2014–15 Holuhraun eruption preserve abundant evidence for sulfide resorption, offering a rare opportunity to explore the sulfide life cycle from nucleation to resorption. Specifically, we combine detailed textural and chemical analyses of sulfides and silicate melts with geochemical models of sulfide saturation and degassing. This integrative approach demonstrates that sulfides began nucleating in melts with ~8 wt% MgO, persisted during fractionation to 6.5 wt% MgO, before resorbing heterogeneously in response to sulfur degassing. Sulfides are preserved preferentially in confined geometries within and between crystals, suggesting that kinetic effects impeded sulfur loss from the melt and maintained local sulfide saturation on eruption. The proportion of sulfides exhibiting breakdown textures increases throughout the eruption, coincident with decreasing magma discharge, indicating that sulfide resorption and degassing are kinetically limited. Sulfides likely modulate the emission of sulfur and chalcophile elements to the atmosphere and surface environment, with implications for assessing the environmental impacts and societal hazards of basaltic fissure eruptions.
... y done by collecting qualitative or quantitative data on a single particle level using a variety of techniques. This includes using a binocular microscope (e.g., D'Oriano et al., 2014;Miwa et al., 2009;Pardo et al., 2014) to observe the gloss, color and shape, as well as the particles' surface and shape (Dellino & La Volpe, 1996;Dürig et al., 2021;E. J. Liu et al., 2015;Ross et al., 2022). More detailed observations including the internal microstructures are typically done using the Scanning Electron Microscope (e.g., Miwa et al., 2013;Pardo et al., 2020), whereas the chemical analyses are conducted with the electron microprobe (Pardo et al., 2014), mass spectrometers (Rowe et al., 2008), and measuremen ...
... rystal, altered material, juvenile, and lithic; Figure 1), and (c) metadata for each particle, such as the sample grain-size fraction, the number of magnifications used for image acquisition, amongst others. The shape features in the database have been used in previous studies Dellino & La Volpe, 1996;Dürig et al., 2018;Leibrandt & Le Pennec, 2015;E. J. Liu et al., 2015), and include those sensitive to particle-scale cavities (e.g., solidity), perimeter-based irregularities (e.g., convexity), and form (e.g., elongation; Liu et al., 2015). The textural features in VolcAshDB were obtained from calculations of the distribution of pixel intensities in grayscale across several particle regions based on the s ...
... The shape features in the database have been used in previous studies Dellino & La Volpe, 1996;Dürig et al., 2018;Leibrandt & Le Pennec, 2015;E. J. Liu et al., 2015), and include those sensitive to particle-scale cavities (e.g., solidity), perimeter-based irregularities (e.g., convexity), and form (e.g., elongation; Liu et al., 2015). The textural features in VolcAshDB were obtained from calculations of the distribution of pixel intensities in grayscale across several particle regions based on the so-called Gray-Level Cooccurrence Matrix (GLCM, Haralick et al., 1973). ...
Volcanic ash provides information that can help understanding the evolution of volcanic activity during the early stages of a crisis and possible transitions toward different eruptive styles. Ash consists of particles from a range of origins within the volcanic system and its analysis can be indicative of the processes driving the eruptive activity. However, classifying ash particles into different types is not straightforward. Diagnostic observations for particle classification are not standardized and vary across samples. Here we explore the use of machine learning (ML) to improve the classification accuracy and reproducibility. We use a curated database of ash particles (VolcAshDB) to optimize and train two ML‐based models: Extreme Gradient Boosting (XGBoost) that uses the measured physical attributes of the particles, from which predictions are interpreted by the SHapley Additive exPlanations (SHAP) method, and a Vision Transformer (ViT) that classifies binocular, multi‐focused, particle images. We find that the XGBoost has an overall classification accuracy of 0.77 (macro F1‐score), and specific features of color (hue_mean) and texture (correlation) are the most discriminant between particle types. Classification using the particle images and the ViT is more accurate (macro F1‐score of 0.93), with performances varying from 0.85 for samples of dome explosions, to 0.95 for phreatic and subplinian events. Notwithstanding the success of the classification algorithms, the training dataset is limited in number of particles, ranges of eruptive styles, and volcanoes. Thus, the algorithms should be tested further with additional samples, and it is likely that classification for a given volcano is more accurate than between volcanoes.
... Representative examples of independent shape parameters are solidity, axial ratio, sphericity, roundness and circularity. Research on particle shapes of materials is aimed at classifying the particles by differentiating the morphological types and constituents [39]. Circularity is a parameter that is generated from 2-d microscopic views and has been regularly computed instead of sphericity as its measurement is easier [40]. ...
... This appears to be relevant and it seemed to have allowed the standard deviations to benefit from such reduced range. It is important to highlight that shape descriptors if chosen wisely, present quantitative, reproducible and significant details of the particle morphology [39]. Circularity and solidity characteristics of particles are known to be the most significant morphological attributes [41]. ...
Quantitative characterisation of morphology and shape parameters of pozzolanic materials, as a fundamental problem of characterisation of pozzolanic materials, has received significant consideration in literature. Thus far, previous research works have not paid much attention to the circularity, roundness and solidity of pozzolanic materials including waste brick powder (WBP). This research makes a significant contribution on identification of circularity, roundness and solidity of WBP particles under milling conditions using quantitative image analysis. In particular, the goal was to interrogate the ball milling treatment variables for generating WBP using scanning electron microscopy (SEM) and image analysis. Under the milling conditions of changing sample masses introduced in ball mill, the average circularity values for the specimens were approximately 0.6 whilst the average solidity values for the specimens were approximately 0.71. Moreover, the average roundness values for the specimens were nearly 0.51. It was shown that the trends of shape parameters of WBP under changing fineness levels were not significant. The values of circularity, solidity and roundness in this study therefore collaborate to support the discoveries of hidden shape characteristics of WBP specimens and can tackle the overall behaviour of cement-based composites containing WBP. Quantitative image analysis was therefore observed to be capable of inheriting detailed information from SEM micrographs and remains one of the most outstanding approaches of generating shape parameters.
... Finally, image analysis techniques have been employed to capture particle attributes in a systematic and relatively fast manner. These include analyses of particles' shape (Liu et al. 2015;Dürig et al. 2018), grain-size , textural complexity of their surface (Ersoy et al. 2006), and/or color (Yamanoi et al. 2008). ...
... Effects of the image type on shape analysis It is well-documented that the results of measurements of particle shape are significantly influenced by the image resolution (Liu et al. 2015;Saxby et al. 2020;Ross et al. 2022) and the method that is used to capture the particle contour. Notably, the measurement of the apparent 2D projected shape of the particles using the SEM or optical microscope, are different from those obtained from the 2D cross-sectional shape of the same particles (Liu et al. 2015;Buckland et al. 2018;Nurfiani and Bouvet de Maisonneuve 2018;Edwards et al. 2021;Comida et al. 2022). ...
... Effects of the image type on shape analysis It is well-documented that the results of measurements of particle shape are significantly influenced by the image resolution (Liu et al. 2015;Saxby et al. 2020;Ross et al. 2022) and the method that is used to capture the particle contour. Notably, the measurement of the apparent 2D projected shape of the particles using the SEM or optical microscope, are different from those obtained from the 2D cross-sectional shape of the same particles (Liu et al. 2015;Buckland et al. 2018;Nurfiani and Bouvet de Maisonneuve 2018;Edwards et al. 2021;Comida et al. 2022). The difference is also found in the values of Solidity and Convexity we obtained, which are higher (meaning smoother particle contours) than those reported by Liu et al. (2015) who investigated the ash from the same eruption (although not the same exact samples) using 2D cross-sectional shape by SEM (Fig. 7). ...
Volcanic ash provides unique pieces of information that can help to understand the progress of volcanic activity at the early stages of unrest, and possible transitions towards different eruptive styles. Ash contains different types of particles that are indicative of eruptive styles and magma ascent processes. However, classifying ash particles into its main components is not straightforward. Diagnostic observations vary depending on the magma composition and the style of eruption, which leads to ambiguities in assigning a given particle to a given class. Moreover, there is no standardized methodology for particle classification, and thus different observers may infer different interpretations. To improve this situation, we created the web-based platform Volcanic Ash DataBase (VolcAshDB). The database contains > 6,300 multi-focused high-resolution images of ash particles as seen under the binocular microscope from a wide range of magma compositions and types of volcanic activity. For each particle image, we quantitatively extracted 33 features of shape, texture, and color, and petrographically classified each particle into one of the four main categories: free crystal, altered material, lithic, and juvenile. VolcAshDB ( https://volcash.wovodat.org ) is publicly available and enables users to browse, obtain visual summaries, and download the images with their corresponding labels. The classified images could be used for comparative studies and to train Machine Learning models to automatically classify particles and minimize observer biases.
... The characteristics of these pyroclastic particles, in particular in the ash size range, are influenced by the fragmentation processes (e.g. brittle vs. ductile) and magma properties (composition, viscosity, temperature, bubbles, and crystals) [Maria and Carey 2002;Noguchi et al. 2008;Mattsson 2010;Miwa et al. 2013;Liu et al. 2015;Nurfiani and Bouvet de Maisonneuve 2018;Ross et al. 2022], as well as by pyroclast transport [Manga et al. 2011;Kueppers et al. 2012;Jordan 2013;Liu et al. 2015;Hornby et al. 2020]. Despite maar eruptions being driven by magma-water interaction, distinguishing the factors controlling the characteristics of the pyroclasts from maar eruptions remains a challenge. ...
... The characteristics of these pyroclastic particles, in particular in the ash size range, are influenced by the fragmentation processes (e.g. brittle vs. ductile) and magma properties (composition, viscosity, temperature, bubbles, and crystals) [Maria and Carey 2002;Noguchi et al. 2008;Mattsson 2010;Miwa et al. 2013;Liu et al. 2015;Nurfiani and Bouvet de Maisonneuve 2018;Ross et al. 2022], as well as by pyroclast transport [Manga et al. 2011;Kueppers et al. 2012;Jordan 2013;Liu et al. 2015;Hornby et al. 2020]. Despite maar eruptions being driven by magma-water interaction, distinguishing the factors controlling the characteristics of the pyroclasts from maar eruptions remains a challenge. ...
... We investigated particles from the ash-dominated proximal deposits at six maars, where particle modification by secondary processes such as transport is minimized [e.g. Liu et al. 2015;Hornby et al. 2020]. Although previous studies have reported on the physical volcanology and the geochemistry of the LVF [Carn 2000;Carn and Pyle 2001], none have investigated the pyroclast products from the Maar complex. ...
The Lamongan Volcanic Field (LVF), East Java, Indonesia, has experienced numerous maar eruptions, producing varied properties and morphologies of ash particles. This study conducted textural, morphometric, and geochemical analyses of the juvenile particles to elucidate the factors governing their heterogeneous characteristics. Two distinct types of juvenile ash were identified: A (black and brown ash) and B (orange-brown ash), reflecting different fragmentation processes. The blocky to slightly elongate shapes of juvenile A across heterogenous basaltic compositions (resulting in variable textures, rheological properties, and/or cooling histories) highlight the phreatomagmatic process as the primary control of their shape. In contrast, the irregular-fluidal shapes of juvenile B particles indicate magmatic fragmentation of basaltic andesite magma. This study reveals that variable magma properties yield diverse ash components, yet fragmentation dynamics govern pyroclast shapes in the LVF maar complex. Our integrated approach emphasizes the importance of considering multiple variables when interpreting heterogeneous volcanic ash deposits.
... The grain shape and texture analyses of tephra contributed to inferring with the eruption style such as external water participation (e.g., Wohletz and Heiken, 1992;Miwa et al., 2015;Dürig et al., 2021). Statistical analysis and machine learning techniques provide new insights into the characterizations and classifications of the eruption (Leibrandt and Le Pennec, 2015;Liu et al., 2015;Shoji et al., 2018). Thus, the observation and description of volcanic stratigraphy are fundamental tasks for volcanologists to obtain details and histories of eruptions. ...
As volcanic stratigraphy provides important information about volcanic activities, such as the eruption style, duration, magnitude, and their time sequences, its observation and description are fundamental tasks for volcanologists. Since outcrops are often obscured in nature, the first task would be identifying stratigraphic exposures in many cases. This identification/selection process has depended on humans and has difficulties in terms of time and effort consumption and in biases resulting from expertise levels. To address this issue, we present an approach that utilizes supervised machine learning with fine-tuning and forms the backbone to automatically extract the areas of stratigraphic exposures in visible images of volcanic outcrops. This study aimed to develop an automated method for identifying exposed stratigraphy. This method will aid in planning subsequent field investigations and quickly outputting results. We used U-Net and LinkNet, convolutional neural network architectures developed for image segmentation. Our dataset comprised 75 terrestrial outcrop images and their corresponding images with manually masked stratigraphic exposure areas. Aiming to recognize stratigraphic exposures in various situations, the original images include unnecessary objects such as sky and vegetation. Then, we compared 27 models with varying network architectures, hyperparameters, and training techniques. The highest validation accuracy was obtained by the model trained using the U-Net, fine-tuning, and ResNet50 backbone. Some of our trained U-Net and LinkNet models successfully excluded the sky and had difficulties in excluding vegetation, artifacts, and talus. Further surveys of reasonable training settings and network structures for obtaining higher prediction fidelities in lower time and effort costs are necessary. In this study, we demonstrated the usability of image segmentation algorithms in the observation and description of geological outcrops, which are often challenging for non-experts. Such approaches can contribute to passing accumulated knowledge on to future generations. The autonomous detection of stratigraphic exposures could enhance the output from the vast collection of remote sensing images obtained not only on Earth but also on other planetary bodies, such as Mars.
... This range makes cross-comparison between different studies and deposits challenging and the frequent use of alternative nomenclature (e.g., roundness vs. circularity vs. form factor) further hinders useful comparisons. The use of shape parameters for juvenile volcanic pyroclasts, associated statistical tests and classifications, and the protocols for data collection have been extensively reviewed and this will not be repeated here (Leibrandt and Le Pennec, 2015;Liu et al., 2015;Dürig et al., 2021;Comida et al., 2022;Ross et al., 2022;Benet et al., 2023). The detailed work of Liu et al. (2015) leads to the recommendation of the following bounded (i.e., scaled from 0 to 1) shape descriptors: solidity, convexity, and axial ratio. ...
... The use of shape parameters for juvenile volcanic pyroclasts, associated statistical tests and classifications, and the protocols for data collection have been extensively reviewed and this will not be repeated here (Leibrandt and Le Pennec, 2015;Liu et al., 2015;Dürig et al., 2021;Comida et al., 2022;Ross et al., 2022;Benet et al., 2023). The detailed work of Liu et al. (2015) leads to the recommendation of the following bounded (i.e., scaled from 0 to 1) shape descriptors: solidity, convexity, and axial ratio. Solidity (SLD) is a measure of the irregularities and roughness on a particle scale (i.e., the morphological roughness) and is expressed as: ...
... where A p is the pyroclast area and A H is the area of the bounding convex hull. Convexity (CVX) is a measure of the small-scale cavities or protrusions on the particle surface (i.e., the textural roughness) and is expressed as: Schematic illustration of the recommended shape descriptors as defined by Liu et al. (2015). where P P is the pyroclast perimeter and P H is the perimeter of the bounding convex hull. ...
Pyroclastic density currents (PDCs) are hazardous and destructive phenomena that pose a significant threat to communities living in the proximity of active volcanoes. PDCs are ground-hugging density currents comprised of high temperature mixtures of pyroclasts, lithics, and gas that can propagate kilometres away from their source. The physical properties of the solid particles, such as their grain size distribution, morphology, density, and componentry play a crucial role in determining the dynamics and impact of these flows. The modification of these properties during transport also records the causative physical processes such as deposition and particle fragmentation. Understanding these processes from the study of deposits from PDCs and related co-PDC plumes is essential for developing effective hazard assessment and risk management strategies. In this article, we describe the importance and relevance of the physical properties of PDC deposits and provide a perspective on the challenges associated with their measurement and characterization. We also discuss emerging topics and future research directions such as electrical charging, granular rheology, ultra-fine ash and thermal and surface properties that are underpinned by the characterization of pyroclasts and their interactions at the micro-scale. We highlight the need to systematically integrate experiments, field observations, and laboratory measurements into numerical modelling approaches for improving our understanding of PDCs. Additionally, we outline a need for the development of standardised protocols and methodologies for the measurement and reporting of physical properties of PDC deposits. This will ensure comparability, reproducibility of results from field studies and also ensure the data are sufficient to benchmark future numerical models of PDCs. This will support more accurate simulations that guide hazard and risk assessments.
... Secondary electron images of the different particles were collected, the surface textures were described, and their projected shapes were analyzed using ImageJ, an open-source software program (Schneider et al., 2012). For each particle, we calculated five shape parameters (convexity, solidity, convexity index, form factor, and elongation), as defined in Liu et al. (2015). The same particles analyzed for ϕ = −1 were then embedded in resin and polished, to investigate the groundmass texture of the different components after collecting digital backscattered-electron images. ...
Volcanic hazards associated with lava flows advancing on snow cover are often underrated, although sudden explosions related to different processes of lava-snow/ice contact can occur rapidly and are only preceded by small, easily underrated precursors. On 16 March 2017, during a mildly effusive and explosive eruption at Mount Etna, Italy, a slowly advancing lava lobe interacted with the snow cover to produce a sudden, brief sequence of explosions. White vapor, brown ash, and coarse material were suddenly ejected, and the products struck a group of people, injuring some of them. The proximal deposit formed a continuous mantle of ash, lapilli, and decimeter-sized bombs, while the ballistic material travelled up to 200 m from the lava edge. The deposit was estimated to have a mass of 7.1 ± 0.8 × 104 kg, which corresponds to a volume of 32.0 ± 3.6 m3 of lava being removed by the explosion. Data related to the texture and morphology of the ejected clasts were used to constrain a model of lava-snow interaction. The results suggest that the mechanism causing the explosions was the progressive build-up of pressure due to vapor accumulation under the lava flow, while no evidence was found for the occurrence of fuel-coolant interaction processes. Although these low-intensity explosions are not particularly frequent, the data set collected provides, for the first time, quantitative information about the processes involved and the associated hazard and suggests that mitigation measures should be established to prevent potentially dramatic accidents at worldwide volcanoes frequented by tourists and with fairly easy access, such as Etna.
... When numbers of particles are analyzed for fractal distribution, following the initial mathematical methodology of Mandelbrot (1983) and Turcotte (1989), particle shape is commonly assumed to be spherical (e.g., Jaupart 1998 andKueppers et al. 2006a). However, pyroclasts exhibit highly irregular geometric shapes, and certain size fractions can deviate very substantially from the spherical assumption (Liu et al. 2015). To avoid this challenge, we follow here the approach of Perugini and Kueppers (2012) in using the cumulative weight fractions for fractal distribution analysis. ...
The stratovolcano Mt. Pelée, Martinique, exhibits eruptive styles ranging from dome formation to sustained, highly violent explosive activity. Historical eruptions have produced lava domes and pyroclastic density currents, collectively termed Peléan activity. In pre-colonial times, several Plinian eruptions took place. Here, we explore physical controls on the proportions of fine particles produced—i.e., the fragmentation efficiency—during primary fragmentation. Samples were collected from ignimbrites from the 1929–1932 and 1902–1905 Peléan eruptions and the P1 (1300 CE), P2 (280 CE), and P3 (79 CE) Plinian eruptions. All samples are andesitic in bulk composition and contain a rhyolitic groundmass glass. The Peléan materials are more crystalline and less porous than their Plinian counterparts, a consequence of more extensive outgassing during dome formation. Representative blocks were cored and experimentally fragmented following rapid decompression (> 1 GPa·s−1 from initial pressure between 5 and 20 MPa). Dry sieving allowed for determining grain size distributions, from which the fractal dimensions, Df, were calculated as a quantification of fragmentation efficiency. Our results indicate different behaviors for Peléan and Plinian samples. While fragmentation efficiency is positively correlated with applied potential energy for Peléan samples, this relationship is not observed for the Plinian samples, possibly due to syn-fragmentation gas escape above a certain porosity. The rapid decompression experiments were designed to minimize secondary fragmentation by shear along the walls or impact while preserving the entirety of produced materials. Thus, our experimental grainsize data are physically linked to sample textures and overpressure. By comparison with natural pyroclastic products—commonly incompletely preserved—we can approach quantitatively constraining the energetic conditions underlying individual eruptions.
