Distance-learning For Approximate Bayesian Computation To Model a Volcanic Eruption

Lorenzo Pacchiardi, Pierre Künzli, Marcel Schöngens, Bastien Chopard and Ritabrata Dutta ()
Additional contact information
Lorenzo Pacchiardi: University of Oxford
Pierre Künzli: University of Geneva
Marcel Schöngens: Six Group AG
Bastien Chopard: University of Geneva
Ritabrata Dutta: Warwick University

Sankhya B: The Indian Journal of Statistics, 2021, vol. 83, issue 1, No 13, 288-317

Abstract: Abstract Approximate Bayesian computation (ABC) provides us with a way to infer parameters of models, for which the likelihood function is not available, from an observation. Using ABC, which depends on many simulations from the considered model, we develop an inferential framework to learn parameters of a stochastic numerical simulator of volcanic eruption. Moreover, the model itself is parallelized using Message Passing Interface (MPI). Thus, we develop a nested-parallelized MPI communicator to handle the expensive numerical model with ABC algorithms. ABC usually relies on summary statistics of the data in order to measure the discrepancy model output and observation. However, informative summary statistics cannot be found for the considered model. We therefore develop a technique to learn a distance between model outputs based on deep metric-learning. We use this framework to learn the plume characteristics (eg. initial plume velocity) of the volcanic eruption from the tephra deposits collected by field-work associated with the 2450 BP Pululagua (Ecuador) volcanic eruption.

Keywords: Volcanic eruption; Numerical model; Approximate Bayesian computation; Nested parallelization; MPI; Distance learning; Primary 62F15; Secondary 62M45, 62P35, 68W15 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s13571-019-00208-8

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