classification criteria may not be possible, and (2) that information extracted from binocular images, with their inclusion of color and texture in addition to the shape, can be a valuable tool to separate between the different types (Yamanoi et al. 2008; Miwa et al. 2015). Note that we used PCA as an exploratory tool. To assess the full potential of the features to classify particles, machine learning models have been proved to perform best, as they account for non-linearities and are not variance-driven (Verdhan 2020).

## VolcAshDB Web platform

VolcAshDB is an open-access, web-based platform that hosts the curated dataset of the high-resolution, multi-focused images that we have discussed hereto. Each image is linked to: (1) a summary label of the main type, subtype and some of the special characteristics, (2) the measured physical features of shape, color and textures, and (3) the metadata such as the image magnification, the grain-size or the sample collector. Users can browse through the whole image dataset, or use filters to only visualize particles according to their type, activity type, or volcanoes. The images, their classification and the 33 measured features can be downloaded from the web site in various file formats at https://volcash.wovodat.org/database/catalogue. We have also created an app for data visualization at https://volcash.wovodat.org/analytic. Users can visualize basic relationships of the features, as well as their distributions depending on the activity type, particle type, amongst others. The graphs are interactive to allow users choosing a specific volcano, activity type, sample and feature for display.

The database content of the platform is stored in a server, using the database manager MongoDB, as it is cost-effective, flexible and can handle many data types. The server infrastructure to receive and process the browser's requests is located under WOVOdat (Newhall et al. 2017), which is a comprehensive global database on volcanic unrest (https://www.wovodat.org/). The backend uses several technologies, including JavaScript Object Notation (JSON), which holds the database, and the open-source libraries Node.js and Flask to execute tasks, such as opening a file on the computer's file system. The frontend, where the user interacts with the app, uses the open-source JavaScript library React.

## Discussion

### Limitations of the current database

VolcAshDB contains data for about > 6,300 particles from 12 samples and five activity types, for which we obtained the main types and some sub-types proportions, special

characteristics, and a list of quantitatively measured features. However, the petrologic classification of each particle has been conducted by only one observer, and hence the classification could be biased, although we used diagnostic observations from the literature as a basis for classification (Fig. 6). To improve this in the future, classification should result from the aggregated knowledge and experience of various experts in the field. This could be accomplished via workshops and publications where several researchers classify the same particles. In addition, expert elicitation (Aspinall and Blong 2015) would allow to treat the problem in probabilistic terms. This approach has been successfully done in other volcanological studies dealing with highly uncertain situations. Another limitation is that each particle has been classified as belonging 100% to a given type, which implies 100% certainty. A more robust classification could include a percentage of a given particle to belong to a given class without these being mutually exclusive. For instance, if a particle exhibits four out of five fresh-like features, and the weights are equally distributed, the particle could be assigned 80% of probability of being juvenile. A third limitation of the database is that the componentry per eruption is obtained from one grain-size fraction collected at one sampling site. More representative componentry should account for various grain-size fractions and samples collected at strategic distances from the vent (Eychenne et al. 2013). Other limitations of the database are the range of activity types and magma compositions. We currently have not yet incorporated ash particles from vulcanian or strombolian eruptions, or from phreatomagmatic events driven by water-magma interactions. In terms of magma compositions, we are also lacking andesites. A future goal of the database is to make it more complete by incorporating data from our own samples, but also to make the platform open for any user to upload ash image samples that would be classified into the different types so that the database could grow by the community as it is the case for WOVOdat (Costa et al. 2019). If the uploaded images are multi-focused, high-resolution (e.g., above 1,500 pixels per millimeter (pxls/mm), and taken under the same white plate, these could be normalized (as explained in section “Image processing”) and added in our curated multi-focused dataset. Binocular images consisting in the “standard” single-focus binocular images, cross-sectional and external particle SEM images will be maintained in a separate repository, and their use for statistical analysis will require additional curation efforts.

### Applications to comparative studies

Notwithstanding the limitations highlighted above, our results show that the ash samples from different volcanoes and activity types have distinctive particle main type proportions. This means that we can make meaningful comparisons

<figure><img src="image_20.png" /></figure>
classification criteria may not be possible, and (2) that information extracted from binocular images, with their inclusion of color and texture in addition to the shape, can be a valuable tool to separate between the different types (Yamanoi et al. 2008; Miwa et al. 2015). Note that we used PCA as an exploratory tool. To assess the full potential of the features to classify particles, machine learning models have been proved to perform best, as they account for non-linearities and are not variance-driven (Verdhan 2020).

## VolcAshDB Web platform

VolcAshDB is an open-access, web-based platform that hosts the curated dataset of the high-resolution, multi-focused images that we have discussed hereto. Each image is linked to: (1) a summary label of the main type, subtype and some of the special characteristics, (2) the measured physical features of shape, color and textures, and (3) the metadata such as the image magnification, the grain-size or the sample collector. Users can browse through the whole image dataset, or use filters to only visualize particles according to their type, activity type, or volcanoes. The images, their classification and the 33 measured features can be downloaded from the web site in various file formats at https://volcash.wovodat.org/database/catalogue. We have also created an app for data visualization at https://volcash.wovodat.org/analytic. Users can visualize basic relationships of the features, as well as their distributions depending on the activity type, particle type, amongst others. The graphs are interactive to allow users choosing a specific volcano, activity type, sample and feature for display.

The database content of the platform is stored in a server, using the database manager MongoDB, as it is cost-effective, flexible and can handle many data types. The server infrastructure to receive and process the browser's requests is located under WOVOdat (Newhall et al. 2017), which is a comprehensive global database on volcanic unrest (https://www.wovodat.org/). The backend uses several technologies, including JavaScript Object Notation (JSON), which holds the database, and the open-source libraries Node.js and Flask to execute tasks, such as opening a file on the computer's file system. The frontend, where the user interacts with the app, uses the open-source JavaScript library React.

## Discussion

### Limitations of the current database

VolcAshDB contains data for about > 6,300 particles from 12 samples and five activity types, for which we obtained the main types and some sub-types proportions, special

characteristics, and a list of quantitatively measured features. However, the petrologic classification of each particle has been conducted by only one observer, and hence the classification could be biased, although we used diagnostic observations from the literature as a basis for classification (Fig. 6). To improve this in the future, classification should result from the aggregated knowledge and experience of various experts in the field. This could be accomplished via workshops and publications where several researchers classify the same particles. In addition, expert elicitation (Aspinall and Blong 2015) would allow to treat the problem in probabilistic terms. This approach has been successfully done in other volcanological studies dealing with highly uncertain situations. Another limitation is that each particle has been classified as belonging 100% to a given type, which implies 100% certainty. A more robust classification could include a percentage of a given particle to belong to a given class without these being mutually exclusive. For instance, if a particle exhibits four out of five fresh-like features, and the weights are equally distributed, the particle could be assigned 80% of probability of being juvenile. A third limitation of the database is that the componentry per eruption is obtained from one grain-size fraction collected at one sampling site. More representative componentry should account for various grain-size fractions and samples collected at strategic distances from the vent (Eychenne et al. 2013). Other limitations of the database are the range of activity types and magma compositions. We currently have not yet incorporated ash particles from vulcanian or strombolian eruptions, or from phreatomagmatic events driven by water-magma interactions. In terms of magma compositions, we are also lacking andesites. A future goal of the database is to make it more complete by incorporating data from our own samples, but also to make the platform open for any user to upload ash image samples that would be classified into the different types so that the database could grow by the community as it is the case for WOVOdat (Costa et al. 2019). If the uploaded images are multi-focused, high-resolution (e.g., above 1,500 pixels per millimeter (pxls/mm), and taken under the same white plate, these could be normalized (as explained in section “Image processing”) and added in our curated multi-focused dataset. Binocular images consisting in the “standard” single-focus binocular images, cross-sectional and external particle SEM images will be maintained in a separate repository, and their use for statistical analysis will require additional curation efforts.

### Applications to comparative studies

Notwithstanding the limitations highlighted above, our results show that the ash samples from different volcanoes and activity types have distinctive particle main type proportions. This means that we can make meaningful comparisons

<figure><img src="image_23.png" /></figure>
between samples and quantify their similarity at specific var-
iables (e.g., % of juvenile content). It is possible to compare
between eruptions by their proportion of juvenile, altered
material, and lithic components and visualize their distribu-
tions using a ternary diagram (Fig. 14). Such ternary dia-
gram shows that samples from activity that does not directly
involve magma (phreatic) or only to a limited extent (dome
explosions) plot closer (along the isoline of 0% of juvenile
content), than those from lava fountaining, subplinian, and
plinian samples (between the isolines 20–100% of juvenile
content). By incorporating different grain-sizes, more sam-
ples per eruption at different distances from the vent, and
including Strombolian and Vulcanian eruptive styles, parti-
cles features, such as shape, texture and color, could be used
to apply statistical clustering and quantify their similarity
in the context of analogue volcanoes. Users could plot the
proportions of the ash samples that they are studying and
compare with the eruptions we have produced in the data-
base. It should be also possible to plot a time series of ash
samples that visually shows how the volcano may be going
from a mainly phreatic phase towards a magmatic eruption
as for instance reported in ashes from Shinmoe-dake erup-
tion in 2011 (Suzuki et al. 2013).

<figure><img src="image_21.png" /><figcaption>**Fig. 14** Ternary diagram of the particles’ proportions normalized to 1 excluding free crystals, and separated by eruptive activity types. Samples from activity types with limited or absent involvement of magma are near the isoline Juvenile 0%, whereas magmatic eruptions transition towards the juvenile apex. This diagram could be used in comparative studies to identify whether certain areas are characteristic to a given eruptive activity type, which could be used for petrologic monitoring (Suzuki et al. 2013). Reported componentry data belongs to the 0φ–1φ (1000–500 µm) grain-size fraction, except for Kelud (Subplinian) samples that belong to 1φ–2φ (500–250 µm). As noted in the main text, more representative componentry per eruption should include various grain-size fractions and collection of samples at various distances from the vent</figcaption></figure>

Our expectation is that external datasets will be imported
to VolcAshDB to examine their relationship with the ash
particle features we have compiled qualitatively (e.g., plots)
or quantitatively by using statistical tools, e.g., dendro-
grams (Dürig et al. 2020). Alternatively, users can download
locally the datasets currently in VolcAshDB and compare on
their own environment. If such datasets are acquired with a
similar methodological protocol and standardized statistical
tests (Dürig et al. 2021), the data from VolcAshDB can help
researchers with classifying their own particles from any
given volcano and eruption they are studying.

Applications for automatic classification
and machine learning

The particle features we extracted capture specific values
of subpopulations that may be associated with the particle
main types, as shown by multimodality in the histograms,
and by the formation of distinctive clusters in the 3D prin-
cipal components' space. These features could be used to
train machine learning (ML) models, such as decision trees,
random forest and boosted trees (Benet et al. 2024) com-
monly used for multi-class classification via supervised
learning. The images of particles could also be used for
training models such as deep neural networks or Vision
Transformers (Benet et al. 2024). Developing accurate ML
models would allow automatic particle classification that
would be standardized and in principle independent of the
observer, and thus the ash componentry between different
eruptions can be compared in an objective and reproducible
manner. Object classification via image observations is a
problem that has been addressed by applying machine learn-
ing in several fields, e.g., plants based on their leaves (Aakif
and Khan 2015), variable stars (Hosenie et al. 2019), and
also in volcanic ash for classifying into their shapes (Shoji
et al. 2018). These often require a large amount of standard-
ized and widely representative data about particle features,
images, or even chemical composition and the database that
we have started could be the basis for such an endeavor.

Conclusions

Volcanic ash provides critical insights to the state of a vol-
cano, but requires classifying the particles into different
types and there is no standardized methodology to do so.
In this contribution we developed a standardized method-
ology to obtain high-quality images of a large number of
particles and classified the particles following the diag-
nostic observations from previous studies. We additionally
measured a range of physical parameters from the particle
images related to their shape, texture, and color. The whole
dataset includes 6,304 ash particles from a range of activity
between samples and quantify their similarity at specific var-
iables (e.g., % of juvenile content). It is possible to compare
between eruptions by their proportion of juvenile, altered
material, and lithic components and visualize their distribu-
tions using a ternary diagram (Fig. 14). Such ternary dia-
gram shows that samples from activity that does not directly
involve magma (phreatic) or only to a limited extent (dome
explosions) plot closer (along the isoline of 0% of juvenile
content), than those from lava fountaining, subplinian, and
plinian samples (between the isolines 20–100% of juvenile
content). By incorporating different grain-sizes, more sam-
ples per eruption at different distances from the vent, and
including Strombolian and Vulcanian eruptive styles, parti-
cles features, such as shape, texture and color, could be used
to apply statistical clustering and quantify their similarity
in the context of analogue volcanoes. Users could plot the
proportions of the ash samples that they are studying and
compare with the eruptions we have produced in the data-
base. It should be also possible to plot a time series of ash
samples that visually shows how the volcano may be going
from a mainly phreatic phase towards a magmatic eruption
as for instance reported in ashes from Shinmoe-dake erup-
tion in 2011 (Suzuki et al. 2013).

<figure><img src="image_24.png" /><figcaption>**Fig. 14** Ternary diagram of the particles’ proportions normalized to 1 excluding free crystals, and separated by eruptive activity types. Samples from activity types with limited or absent involvement of magma are near the isoline Juvenile 0%, whereas magmatic eruptions transition towards the juvenile apex. This diagram could be used in comparative studies to identify whether certain areas are characteristic to a given eruptive activity type, which could be used for petrologic monitoring (Suzuki et al. 2013). Reported componentry data belongs to the 0φ–1φ (1000–500 µm) grain-size fraction, except for Kelud (Subplinian) samples that belong to 1φ–2φ (500–250 µm). As noted in the main text, more representative componentry per eruption should include various grain-size fractions and collection of samples at various distances from the vent</figcaption></figure>

Our expectation is that external datasets will be imported
to VolcAshDB to examine their relationship with the ash
particle features we have compiled qualitatively (e.g., plots)
or quantitatively by using statistical tools, e.g., dendro-
grams (Dürig et al. 2020). Alternatively, users can download
locally the datasets currently in VolcAshDB and compare on
their own environment. If such datasets are acquired with a
similar methodological protocol and standardized statistical
tests (Dürig et al. 2021), the data from VolcAshDB can help
researchers with classifying their own particles from any
given volcano and eruption they are studying.

Applications for automatic classification
and machine learning

The particle features we extracted capture specific values
of subpopulations that may be associated with the particle
main types, as shown by multimodality in the histograms,
and by the formation of distinctive clusters in the 3D prin-
cipal components’ space. These features could be used to
train machine learning (ML) models, such as decision trees,
random forest and boosted trees (Benet et al. 2024) com-
monly used for multi-class classification via supervised
learning. The images of particles could also be used for
training models such as deep neural networks or Vision
Transformers (Benet et al. 2024). Developing accurate ML
models would allow automatic particle classification that
would be standardized and in principle independent of the
observer, and thus the ash componentry between different
eruptions can be compared in an objective and reproducible
manner. Object classification via image observations is a
problem that has been addressed by applying machine learn-
ing in several fields, e.g., plants based on their leaves (Aakif
and Khan 2015), variable stars (Hosenie et al. 2019), and
also in volcanic ash for classifying into their shapes (Shoji
et al. 2018). These often require a large amount of standard-
ized and widely representative data about particle features,
images, or even chemical composition and the database that
we have started could be the basis for such an endeavor.

Conclusions

Volcanic ash provides critical insights to the state of a vol-
cano, but requires classifying the particles into different
types and there is no standardized methodology to do so.
In this contribution we developed a standardized method-
ology to obtain high-quality images of a large number of
particles and classified the particles following the diag-
nostic observations from previous studies. We additionally
measured a range of physical parameters from the particle
images related to their shape, texture, and color. The whole
dataset includes 6,304 ash particles from a range of activity
types which constitutes VolcAshDB, a public web-based platform that allows users to browse and download various data types. We believe such platform and dataset are useful in comparative studies and the basis for Machine Learning algorithms towards a systematic ash particle classification.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s00445-023-01695-4.

**Acknowledgements** D.B. is very grateful to Charles Tran, Khai Truong and Bui Quang for their invaluable help on the development of the VolcAshDB web-app and the MongoDB collection, to Edwin Tan for support on the Gekko cluster, to Caroline Bouvet de Maisonneuve for insightful discussions, and to Vanesa Burgos for help on the PCA. The manuscript has greatly benefited from thorough revisions by Hannah Buckland, an anonymous reviewer, and the editor Julia Eychenne. This research was supported by the Earth Observatory of Singapore via its funding from the National Research Foundation Singapore and the Singapore Ministry of Education under the Research Centres of Excellence initiative. F.C. acknowledges also support by a Chaire d'Excellence grant by the Université Paris Cité.

**Authors' contributions** Benet led the development, acquired and processed the data, and wrote the original manuscript of the project, which was supervised and revised by Costa. Widiwijayanti and Benet led the development of front-end and the back-end of the web-platform. Pallister, Pedreros, Allard, Humaida, Aoki and Maeno provided some samples. The final version of the manuscript has been read, reviewed and approved by all co-authors.

**Data availability** The curated dataset of particle features described in this paper are available at https://volcash.wovodat.org/catalogue.

**Code availability** The Python program for feature extraction and for PCA, as well as the Java Script code for the front-end of the web are publicly accessible at the github repository https://github.com/dbenentu/Volcash-Project. The Python code was developed using its version 3.9. Data wrangling and analysis was done using Numpy v.1.21.6, Pandas v. 1.3.5, image processing using Scikit-image v.0.19.2 and opencv v.3.4.2, and plotting by matplotlib v.3.5.1 and seaborn v.0.11.2.

## Declarations

**Competing interest** The authors declare no competing interests.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

## References

Aakif A, Khan MF (2015) Automatic classification of plants based on their leaves. Biosys Eng 139:66–75. https://doi.org/10.1016/j.biosystemseng.2015.08.003

Aiuppa A (2015) Volcanic-gas monitoring. Cambridge University Press, pp 81–96

Alvarado GE, Mele D, Dellino P, de Moor JM, Avard G (2016) Are the ashes from the latest eruptions (2010–2016) at Turrialba volcano (Costa Rica) related to phreatic or phreatomagmatic events? J Volcanol Geoth Res 327:407–415. https://doi.org/10.1016/j.jvolgfores.2016.09.003

Andronico D, Lo Castro MD, Sciotto M, Spina L (2013) The 2010 ash emissions at the summit craters of Mt Etna: Relationship with seismo-acoustic signals. J Geophys Res: Solid Earth 118(1):51–70. https://doi.org/10.1029/2012JB009895

Andronico D, Scollo S, Lo Castro MD, Cristaldi A, Lodato L, Taddeucci J (2014) Eruption dynamics and tephra dispersal from the 24 November 2006 paroxysm at South-East Crater, Mt Etna, Italy. J Volcanol Geotherm Res 274(November 2006):78–91. https://doi.org/10.1016/j.jvolgeores.2014.01.009

Angkasa SS, Ohba T, Imura T, Setiawan I, Rosana MF (2019) Tephra-stratigraphy and ash componenty studies of proximal volcanic products at mount Tangkuban Parahu, Indonesia: An insight to holocene volcanic activity. Indones J Geosci 6(3):235–253. https://doi.org/10.17014/ijog.6.3.235-253

Aspinall W, Blong R (2015) Volcanic Risk Assessment. In The Encyclopedia of Volcanoes (Second Edi). Elsevier Inc. https://doi.org/10.1016/B978-0-12-385938-9.00070-5

Aspinall W, Cooke RM (1998) Expert Judgement and the Montserrat Volcano Eruption. Proc 4th Int Conf Probabilistic Safety Assess Manag PSAM4 3:2113–2118

Bardelli F, Giuli G, Di BF et al (2020) Spectroscopic study of volcanic ashes. J Hazard Mater 400:123213. https://doi.org/10.1016/j.jhazmat.2020.123213

Battaglia J, Hidalgo S, Bernard B, Steele A, Arellano S, Acuña K (2019) Autopsy of an eruptive phase of Tungurahua volcano (Ecuador) through coupling of seismo-acoustic and SO 2 recordings with ash characteristics. Earth Planet Sci Lett 511:223–232. https://doi.org/10.1016/j.epsl.2019.01.042

Bebbington MS, Jenkins SF (2019) Intra-eruption forecasting. Bull Volcanol 81:1–7

Benet D, Costa F, Pedreros G, Cardona C (2021) The volcanic ash record of shallow magma intrusion and dome emplacement at Nevados de Chillán Volcanic complex, Chile. J Volcanol Geotherm Res 417. https://doi.org/10.1016/j.jvolgeores.2021.107308

Benet D, Costa F, Widiwijayanti C (2024) Volcanic ash classification through Machine Learning. Geochemistry, Geophysics, Geosystems. https://doi.org/10.1029/2023GC011224

Bernard A, Knittel U, Weber B, Weis D, Albrecht A, Hattori K, Klein J, Oles D (1996) Petrology and geochemistry of the 1991 eruption products of Mount Pinatubo. In: Fire and mud: eruptions and lahars of Mount Pinatubo, Philippines, pp 767–797

Buckland HM, Eychenne J, Rust AC, Cashman KV (2018) Relating the physical properties of volcanic rocks to the characteristics of ash generated by experimental abrasion. J Volcanol Geoth Res 349:335–350. https://doi.org/10.1016/j.jvolgeores.2017.11.017

Cannata CB, Rosa RD, Donato P, Taddeucci J (2014) Ash Features from Ordinary Activity at Stromboli Volcano. Int J Geosci 05(11):1361–1382. https://doi.org/10.4236/ijg.2014.511111

Carey SN, Sigurdsson H (1982) Influence of particle aggregation on deposition of distal tephra from the May 18, 1980, eruption of Mount St. Helens volcano. J Geophys Res 87:7061–7072
types which constitutes VolcAshDB, a public web-based platform that allows users to browse and download various data types. We believe such platform and dataset are useful in comparative studies and the basis for Machine Learning algorithms towards a systematic ash particle classification.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s00445-023-01695-4.

**Acknowledgements** D.B. is very grateful to Charles Tran, Khai Truong and Bui Quang for their invaluable help on the development of the VolcAshDB web-app and the MongoDB collection, to Edwin Tan for support on the Gekko cluster, to Caroline Bouvet de Maisonneuve for insightful discussions, and to Vanesa Burgos for help on the PCA. The manuscript has greatly benefited from thorough revisions by Hannah Buckland, an anonymous reviewer, and the editor Julia Eychenne. This research was supported by the Earth Observatory of Singapore via its funding from the National Research Foundation Singapore and the Singapore Ministry of Education under the Research Centres of Excellence initiative. F.C. acknowledges also support by a Chaire d'Excellence grant by the Université Paris Cité.

**Authors contributions** Benet led the development, acquired and processed the data, and wrote the original manuscript of the project, which was supervised and revised by Costa. Widiwijayanti and Benet led the development of front-end and the back-end of the web-platform. Pallister, Pedreros, Allard, Humaida, Aoki and Maeno provided some samples. The final version of the manuscript has been read, reviewed and approved by all co-authors.

**Data availability** The curated dataset of particle features described in this paper are available at https://volcash.wovodat.org/catalogue.

**Code availability** The Python program for feature extraction and for PCA, as well as the Java Script code for the front-end of the web are publicly accessible at the github repository https://github.com/dbenet-ntu/Volcash-Project. The Python code was developed using its version 3.9. Data wrangling and analysis was done using Numpy v.1.21.6, Pandas v. 1.3.5, image processing using Scikit-image v.0.19.2 and opencv v.3.4.2, and plotting by matplotlib v.3.5.1 and seaborn v.0.11.2.

## Declarations

**Competing interest** The authors declare no competing interests.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

## References

Aakif A, Khan MF (2015) Automatic classification of plants based on their leaves. Biosys Eng 139:66–75. https://doi.org/10.1016/j.biosystemseng.2015.08.003

Aiuppa A (2015) Volcanic-gas monitoring. Cambridge University Press, pp 81–96

Alvarado GE, Mele D, Dellino P, de Moor JM, Avard G (2016) Are the ashes from the latest eruptions (2010–2016) at Turrialba volcano (Costa Rica) related to phreatic or phreatomagmatic events? J Volcanol Geoth Res 327:407–415. https://doi.org/10.1016/j.jvolg-eores.2016.09.003

Andronico D, Lo Castro MD, Sciotto M, Spina L (2013) The 2010 ash emissions at the summit craters of Mt Etna: Relationship with seismo-acoustic signals. J Geophys Res: Solid Earth 118(1):51–70. https://doi.org/10.1029/2012JB009895

Andronico D, Scollo S, Lo Castro MD, Cristaldi A, Lodato L, Taddeucci J (2014) Eruption dynamics and tephra dispersal from the 24 November 2006 paroxysm at South-East Crater, Mt Etna, Italy. J Volcanol Geotherm Res 274(November 2006):78–91. https://doi.org/10.1016/j.jvolgeores.2014.01.009

Angkasa SS, Ohba T, Imura T, Setiawan I, Rosana MF (2019) Tephra-stratigraphy and ash componenty studies of proximal volcanic products at mount Tangkuban Parahu, Indonesia: An insight to holocene volcanic activity. Indones J Geosci 6(3):235–253. https://doi.org/10.17014/ijog.6.3.235-253

Aspinall W, Blong R (2015) Volcanic Risk Assessment. In The Encyclopedia of Volcanoes (Second Edi). Elsevier Inc. https://doi.org/10.1016/B978-0-12-385938-9.00070-5

Aspinall W, Cooke RM (1998) Expert Judgement and the Montserrat Volcano Eruption. Proc 4th Int Conf Probabilistic Safety Assess Manag PSAM4 3:2113–2118

Bardelli F, Giuli G, Di BF et al (2020) Spectroscopic study of volcanic ashes. J Hazard Mater 400:123213. https://doi.org/10.1016/j.jhazmat.2020.123213

Battaglia J, Hidalgo S, Bernard B, Steele A, Arellano S, Acuña K (2019) Autopsy of an eruptive phase of Tungurahua volcano (Ecuador) through coupling of seismo-acoustic and SO 2 recordings with ash characteristics. Earth Planet Sci Lett 511:223–232. https://doi.org/10.1016/j.epsl.2019.01.042

Bebbington MS, Jenkins SF (2019) Intra-eruption forecasting. Bull Volcanol 81:1–7

Benet D, Costa F, Pedreros G, Cardona C (2021) The volcanic ash record of shallow magma intrusion and dome emplacement at Nevados de Chillán Volcanic complex, Chile. J Volcanol Geotherm Res 417. https://doi.org/10.1016/j.jvolgeores.2021.107308

Benet D, Costa F, Widiwijayanti C (2024) Volcanic ash classification through Machine Learning. Geochemistry, Geophysics, Geosystems. https://doi.org/10.1029/2023GC011224

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Chouet B (2003) Volcano seismology. Pure Appl Geophys 160(3–4):739–788. https://doi.org/10.1007/PL00012556

Cioni R, Pistolesi M, Bertagnini A, Bonadonna C, Hoskuldsson A, Scateni B (2014) Insights into the dynamics and evolution of the 2010 Eyjafjallajökull summit eruption (Iceland) provided by volcanic ash textures. Earth Planetary Sci Lett 394(May 2010):111–123. https://doi.org/10.1016/j.epsl.2014.02.051

Cioni R, Sbrana A, Vecci R (1992) Morphologic features of juvenile pyroclasts from magmatic and phreatomagmatic deposits of Vesuvius. J Volcanol Geotherm Res 51(1–2):61–78

Comida PP, Ross P-S, Zimanowski B, Büttner R, Dürig T (2023) Controls on juvenile ash morphologies in lava fountains: insights from laboratory experiments. Bull Volcanol 85(4):1–24. https://doi.org/10.1007/s00445-023-01637-0

Comida PP, Ross PS, Dürig T, White JDL, Lefebvre N (2022) Standardized analysis of juvenile pyroclasts in comparative studies of primary magma fragmentation: 2. Choice of size fraction and method optimization for particle cross-sections. Bull Volcanol 84(1):1–24. https://doi.org/10.1007/s00445-021-01517-5

Costa F, Widiwijayanti C, Nang TZW, Fajiculay E, Espinosa-Ortega T, Newhall C (2019) WOVODat – the global volcano unrest database aimed at improving eruption forecasts. Disaster Prev Manag: Int J 28(6):738–751. https://doi.org/10.1108/DPM-09-2019-0301

D'Oriano C, Bertagnini A, Cioni R, Pompilio M (2014) Identifying recycled ash in basaltic eruptions. Sci Rep 4. https://doi.org/10.1038/srep05851

D'Oriano C, Bertagnini A, Pompilio M (2011) Ash erupted during normal activity at Stromboli ( Aeolian Islands, Italy) raises questions on how the feeding system works. Bull Volcanol 73(5):471–477. https://doi.org/10.1007/s00445-010-0425-0

D'Oriano C, Del Carlo P, Andronico D, Cioni R, Gabellini P, Cristaldi A, Pompilio M (2022). Syn-eruptive processes during the January–February 2019 ash-rich emissions cycle at Mt. Etna (Italy): implications for petrological monitoring of volcanic ash. Front Earth Sci 10(February 2019). https://doi.org/10.3389/feart.2022.824872

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Dellino P, La Volpe L (1996) Image processing analysis in reconstructing fragmentation and transportation mechanisms of pyroclastic deposits. The case of Monte Pilato-Rocche Rosse eruptions, Lipari ( Aeolian islands, Italy). J Volcanol Geotherm Res 71(1):13–29. https://doi.org/10.1016/0377-0273(95)00062-3

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Doyle EEH, McClure J, Paton D, Johnston DM (2014) Uncertainty and decision making: Volcanic crisis scenarios. Int J Disaster Risk Reduction 10(PA):75–101. https://doi.org/10.1016/j.ijdrr.2014.07.006

Druitt TH (2014) New insights into the initiation and venting of the Bronze-Age eruption of Santorini (Greece), from component analysis. Bull Volcanol 76(2):1–21. https://doi.org/10.1007/s00445-014-0794-x

Dürig T, Bowman MH, White JDL, Murch A, Mele D, Verolino A, Dellino P (2018) Particle shape analyzer Partisan - An open

source tool for multi-standard two-dimensional particle morphometry analysis. Ann Geophys 61(6). https://doi.org/10.4401/ag-7865

Dürig T, Mele D, Dellino P, Zimanowski B (2012) Comparative analyses of glass fragments from brittle fracture experiments and volcanic ash particles. Bull Volcanol 74(3):691–704. https://doi.org/10.1007/s00445-011-0562-0

Dürig T, Ross PS, Dellino P, White JDL, Mele D, Comida PP (2021) A review of statistical tools for morphometric analysis of juvenile pyroclasts. Bull Volcanol 83(11). https://doi.org/10.1007/s00445-021-01500-0

Dürig T, Schmidt LS, White JDL, Bowman MH (2020) DendroScan: an open source tool to conduct comparative statistical tests and dendrogrammatic analyses on particle morphometry. Sci Rep 10(1):1–14. https://doi.org/10.1038/s41598-020-78698-0

Dzurisin D (2006) Volcano deformation: new geodetic monitoring techniques. Springer Science & Business Media

Edwards MJ, Eychenne J, Pioli L (2021) Formation and dispersal of ash at open conduit basaltic volcanoes: lessons from Etna. Front Earth Sci 9:709657

Ersoy O (2010) Surface area and volume measurements of volcanic ash particles by SEM stereoscopic imaging. J Volcanol Geoth Res 190(3–4):290–296. https://doi.org/10.1016/j.jvolgeores.2009.12.006

Ersoy O, Chinga G, Aydar E, Gourgaud A, Evren Cubukcu H, Ulusoy I (2006) Texture discrimination of volcanic ashes from different fragmentation mechanisms: A case study, Mount Nemrut stratovolcano, eastern Turkey. Comput Geosci 32(7):936–946. https://doi.org/10.1016/j.cageo.2005.10.013

Eychenne J, Le Pennec JL, Ramón P, Yepes H (2013) Dynamics of explosive paroxysms at open-vent andesitic systems: High-resolution mass distribution analyses of the 2006 Tungurahua fall deposit (Ecuador). Earth Planet Sci Lett 361:342–355. https://doi.org/10.1016/j.epsl.2012.11.002

Eychenne J, Houghton BF, Swanson DA, Carey RJ, Swavely L (2015) Dynamics of an open basaltic magma system: The 2008 activity of the Halema'uma'u Overlook vent, Kīlauea Caldera. Earth Planet Sci Lett 409:49–60. https://doi.org/10.1016/j.epsl.2014.10.045

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Fisher RV, Schmincke HU (1984) Pyroclastic Rocks. Springer, Berlin, Heidelberg

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Gaunt HE, Bernard B, Hidalgo S, Proaño A, Wright H, Mothes P, Criollo E, Kueppers U (2016) Juvenile magma recognition and eruptive dynamics inferred from the analysis of ash time series: The 2015 reawakening of Cotopaxi volcano. J Volcanol Geoth Res 328:134–146. https://doi.org/10.1016/j.jvolgeores.2016.10.013

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Geshi N, Iguchi M, Shinohara H (2016) Phreatomagmatic eruptions of 2014 and 2015 in Kuchinoerabujima Volcano triggered by a shallow intrusion of magma. J Nat Dis Sci 37(2):67–78

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Verdhan V (2020) Supervised learning with python. Okänd. Irland: Apress

Verolino A, White JDL, Brenna M (2018) Eruption dynamics at Pahvant Butte volcano, Utah, western USA: insights from ash-sheet dispersal, grain size, and geochemical data. Bull Volcanol 80(11). https://doi.org/10.1007/s00445-018-1256-7

Watanabe K, Danhara T, Watanabe K, Terai K, Yamashita T (1999) Juvenile volcanic glass erupted before the appearance of the 1991 lava dome, Unzen volcano, Kyushu, Japan. J Volcanol Geotherm Res 89(1–4):113–121

White JDL, Houghton BF (2006) Primary volcaniclastic rocks. Geology 34(8):677–680. https://doi.org/10.1130/G22346.1

Yaguchi M, Ohba T, Hirayama Y, Numanami N (2022) Volcanic Ash from the June 17, 1962 Eruption of Yakedake volcano: Stereo microscopic, XRD, and water-soluble components analyses. 17:257–262. https://doi.org/10.20965/jdr.2022.p0257

Yamanoi Y, Takeuchi S, Okumura S, Nakashima S, Yokoyama T (2008) Color measurements of volcanic ash deposits from three different styles of summit activity at Sakurajima volcano, Japan: Conduit processes recorded in color of volcanic ash. J Volcanol Geoth Res 178(1):81–93. https://doi.org/10.1016/j.jvolgeores.2007.11.013