English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Bayesian inversions followed by estimations of rare event probabilities areoften needed to analyse groundwater hazards. Instead of focusing on theposterior distribution of model parameters, the main interest lies then in thedistribution of a specific quantity of interest contingent upon theseparameters. To address the associated methodological challenges, we introduce atwo-stage Sequential Monte Carlo approach. In the first stage, it generatesparticles that approximate the posterior distribution; in the second stage, itemploys subset sampling techniques to assess the probability of the rare eventof interest. By considering two hydrogeological problems of increasingcomplexity, we showcase the efficiency and accuracy of the resultingPostRisk-SMC method for rare event probability estimation related togroundwater hazards. We compare the performance of the PostRisk-SMC method witha traditional Monte Carlo approach that relies on Markov chain Monte Carlosamples. We showcase that our estimates align with those of the traditionalmethod, but the coefficients of variation are notably lower for the samecomputational budget when targeting more rare events. Furthermore, we highlightthat the PostRisk-SMC method allows estimating rare event probabilitiesapproaching one in a billion using less than one hundred thousand forwardsimulations. Even if the presented examples are related to groundwater hazards,the methodology is well-suited for addressing a wide range of topics in thegeosciences and beyond.

Rare event probability estimation for groundwater inverse problems with a two-stage Sequential Monte Carlo approach