<p>All active polygenetic volcanoes erupt magma sourced from a shallow crustal reservoir. &#160;Those chambers are complex entities that act as a collector of magma originating from deeper crustal sources. The geometry of those active storage systems depends on the rheology of the magma and on the rock properties of the host. Studying how the geometry influences the eruptive behaviour of a magma chamber has implications for our understanding of volcanic hazard.<br>We introduced a simple model where a magma reservoir is cooled by an overlying geothermal system and recharged by a deeper magma source. The geometry of the chamber is defined by its volume and aspect ratio. The model tracked changes in pressure, mixture enthalpy and composition, and implemented parameterisations of eruption, hydrothermal cooling, viscoelastic relaxation, and volatile leakage. The thermodynamic properties of the melt, crystals and water were computed using rhyolite-MELTS.<br>A large number of simulations sweeping our parameter space gave us insight into how the different magmatic processes trade off with respect to the geometry of the inclusion. An example of the complex control of geometry on the eruptive behaviour can be made regarding cooling and the effective compressibility of an ellipsoidal inclusion. On the one hand, a larger aspect ratio will favor eruptibility by offering a larger area for cooling therefore increasing the exsolution of water and pressure build up. On the other hand, a larger aspect ratio will work against eruptibility by decreasing the compressibility making it harder to build overpressures within the chamber. We found that a specific geometry is required in order for a chamber to erupt without any external stimuli (such as a large recharge event).<br>A limiting factor of our model is the assumption of a perfect mixing. Whereas, in reality, we would expect recharge, cooling and leakage to occur within specific regions of the chamber. In a model where mixing is not considered perfect, those processes would be a source of heterogeneity. We could conjecture that under the right conditions, eruptible regions would appear within the chamber. A model focusing more on the flows within the chamber might be able to give additional insights on the eruptive behaviour of magma chambers.</p>
<p>Sudden steam-driven eruptions at tourist volcanoes were the cause of 63 deaths at Mt Ontake (Japan) in 2014, and 22 deaths at Whakaari (New Zealand) in 2019. Warning systems that can anticipate these eruptions could provide crucial hours for evacuation or sheltering but these require reliable forecasting. Recently, machine learning has been used to extract eruption precursors from observational data and train forecasting models. However, a weakness of this data-driven approach is its reliance on long observational records that span multiple eruptions. As many volcano datasets may only record one or no eruptions, there is a need to extend these techniques to data-poor locales.</p><p>Transfer machine learning is one approach for generalising lessons learned at data-rich volcanoes and applying them to data-poor ones. Here, we tackle two problems: (1) generalising time series features between seismic stations at Whakaari to address recording gaps, and (2) training a forecasting model for Mt Ruapehu augmented using data from Whakaari. This required that we standardise data records at different stations for direct comparisons, devise an interpolation scheme to fill in missing eruption data, and combine volcano-specific feature matrices prior to model training.</p><p>We trained a forecast model for Whakaari using tremor data from three eruptions recorded at one seismic station (WSRZ) and augmented by data from two other eruptions recorded at a second station (WIZ). First, the training data from both stations were standardised to a unit normal distribution in log space. Then, linear interpolation in feature space was used to infer missing eruption features at WSRZ. Under pseudo-prospective testing, the augmented model had similar forecasting skill to one trained using all five eruptions recorded at a single station (WIZ). However, extending this approach to Ruapehu, we saw reduced performance indicating that more work is needed in standardisation and feature selection.</p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.