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

TL;DR

This paper introduces a parallelized ABC framework with deep metric-learning to infer volcanic eruption parameters from tephra deposits, overcoming the challenge of lacking informative summary statistics.

Contribution

It develops a nested MPI parallelization and a deep metric-learning based distance to improve ABC inference for complex volcanic eruption models.

Findings

Successfully inferred eruption parameters from real data

Enhanced computational efficiency through MPI parallelization

Demonstrated effectiveness of deep metric-learning for model discrepancy

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.