2022
DOI: 10.1007/s00445-022-01543-x
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Analog experiments in volcanology: towards multimethod, upscaled, and integrated models

Abstract: has been published in BULLETIN OF VOLCANOLOGY. The final version of this manuscript is available at the 'Peer-reviewed publication DOI' link on the right-hand side of this webpage.

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Cited by 5 publications
(3 citation statements)
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“…A scientifically rigorous understanding of volcanic systems needs analogous and/or numerical models to support or explain natural observations (Mader et al, 2004;Fagents et al, 2013). Future efforts should include expanding the use of numerical models in the SVZ (e.g., Gutiérrez and Parada, 2010;Amigo, 2013;Córdoba et al, 2015;Castruccio and Contreras, 2016;Reckziegel et al, 2016Reckziegel et al, , 2019Castruccio et al, 2017;Bertin, 2017 andBertin et al, 2019;Ruz et al, 2020), machine learning (e.g., Witsil and Johnson, 2020;Boschetty et al, 2022;Ardid et al, 2023), and other AI algorithms, using multimethod quantification of multiphase processes and upscaling experiments to near-natural scales (Poppe et al, 2022). All these techniques require a fundamental field-based knowledge on the geological evolution, deposits, eruptive behaviour, and hazards of active volcanoes for accurate representations of physical phenomena.…”
Section: Further Considerationsmentioning
confidence: 99%
“…A scientifically rigorous understanding of volcanic systems needs analogous and/or numerical models to support or explain natural observations (Mader et al, 2004;Fagents et al, 2013). Future efforts should include expanding the use of numerical models in the SVZ (e.g., Gutiérrez and Parada, 2010;Amigo, 2013;Córdoba et al, 2015;Castruccio and Contreras, 2016;Reckziegel et al, 2016Reckziegel et al, , 2019Castruccio et al, 2017;Bertin, 2017 andBertin et al, 2019;Ruz et al, 2020), machine learning (e.g., Witsil and Johnson, 2020;Boschetty et al, 2022;Ardid et al, 2023), and other AI algorithms, using multimethod quantification of multiphase processes and upscaling experiments to near-natural scales (Poppe et al, 2022). All these techniques require a fundamental field-based knowledge on the geological evolution, deposits, eruptive behaviour, and hazards of active volcanoes for accurate representations of physical phenomena.…”
Section: Further Considerationsmentioning
confidence: 99%
“…For example, Corbi et al (2019), show how machine learning can predict earthquakes in analogue models, which could potentially be used for real earthquake forecasting. Likewise, modellers can use a variety of methods to simulate volcanic processes (Poppe et al, 2022), (submarine) landslides, and associated tsunamis (e.g., Berndt et al, 2009;McFall and Fritz, 2016). The output of such interdisciplinary analyses can serve to improve the existing hazard and risk assessments.…”
Section: Natural Hazardsmentioning
confidence: 99%
“…Viscous multiphase flows involving two fluids and a granular material occur in such diverse scenarios as mud and debris flows, methane venting from sediments, degassing of volatiles from magma, and the processing of granular and particulate systems in the food, pharmaceutical, and chemical industries [1][2][3][4][5][6][7][8][9][10][11][12][13] . The presence of the granular material introduces solid friction as a governing force in the dynamics, alongside viscosity, capillarity, and gravity.…”
mentioning
confidence: 99%