Overcoming data scarcity for probabilistic eruption forecasting at data-limited volcanoes

Burgos, Vanesa

doi:10.32657/10356/166192

Cited by 1 publication

(2 citation statements)

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“…We are also grateful to Constanza Jorquera Flores for the insightful meetings about Melimoyu. Previous versions of this manuscript are published online in the form of thesis (Burgos, 2022) and preprint (Burgos et al, 2023).…”

Section: Acknowledgmentsmentioning

confidence: 99%

“…Analogue volcanoes have been typically defined based on location, tectonic setting, morphology, magma type, eruption style, or a combination of these factors for 1) assessing local and regional volcanic hazards (e.g., Newhall, 1982;Mastin et al, 2009;Jenkins et al, 2012b;Sandri et al, 2012;Newhall and Pallister, 2015;Lindsay and Robertson, 2018;Tierz et al, 2020;Tennant et al, 2021); 2) estimating eruption frequency-Magnitude (f-M) relationship (e.g., Solow, 2001;Rodado et al, 2011;Jenkins et al, 2012a;2022;Runge et al, 2014;Whelley et al, 2015;Sheldrake and Caricchi, 2017;Hayes et al, 2022); 3) conducting probabilistic eruption forecasts (e.g., Marzocchi et al, 2004;Bebbington, 2014;Sheldrake, 2014;Bebbington and Jenkins, 2022), and 4) identifying unrest patterns (e.g., Acocella et al, 2015;Newhall et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Identifying analogues for data-limited volcanoes using hierarchical clustering and expert knowledge: a case study of Melimoyu (Chile)

Burgos,

Jenkins,

Bono Troncoso

et al. 2023

Front. Earth Sci.

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Determining the eruption frequency-Magnitude (f-M) relationship for data-limited volcanoes is challenging since it requires a comprehensive eruption record of the past eruptive activity. This is the case for Melimoyu, a long-dormant and data-limited volcano in the Southern Volcanic Zone (SVZ) in Chile with only two confirmed Holocene eruptions (VEI 5). To supplement the eruption records, we identified analogue volcanoes for Melimoyu (i.e., volcanoes that behave similarly and are identified through shared characteristics) using a quantitative and objective approach. Firstly, we compiled a global database containing 181 variables describing the eruptive history, tectonic setting, rock composition, and morphology of 1,428 volcanoes. This database was filtered primarily based on data availability into an input dataset comprising 37 numerical variables for 438 subduction zone volcanoes. Then, we applied Agglomerative Nesting, a bottom-up hierarchical clustering algorithm on three datasets derived from the input dataset: 1) raw data, 2) output from a Principal Component Analysis, and 3) weighted data tuned to minimise the dispersion in the absolute probability per VEI. Lastly, we identified the best set of analogues by analysing the dispersion in the absolute probability per VEI and applying a set of criteria deemed important by the local geological service, SERNAGEOMIN, and VB. Our analysis shows that the raw data generate a low dispersion and the highest number of analogues (n = 20). More than half of these analogues are in the SVZ, suggesting that the tectonic setting plays a key role in the clustering analysis. The eruption f-M relationship modelled from the analogue’s eruption data shows that if Melimoyu has an eruption, there is a 49% probability (50th percentile) of it being VEI≥4. Meanwhile, the annual absolute probability of a VEI≤1, VEI 2, VEI 3, VEI 4, and VEI≥5 eruption at Melimoyu is 4.82 × 10−4, 1.2 × 10−3, 1.45 × 10−4, 9.77 × 10−4, and 8.3 × 10−4 (50th percentile), respectively. Our work shows the importance of using numerical variables to capture the variability across volcanoes and combining quantitative approaches with expert knowledge to assess the suitability of potential analogues. Additionally, this approach allows identifying groups of analogues and can be easily applied to other cases using numerical variables from the global database. Future work will use the analogues to populate an event tree and define eruption source parameters for modelling volcanic hazards at Melimoyu.

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Section: Acknowledgmentsmentioning

confidence: 99%