Iterative data-driven construction of surrogates for an efficient Bayesian identification of oil spill source parameters from image contours

Identifying the source of an oil spill is an essential step in environmental forensics. The Bayesian approach allows to estimate the source parameters of an oil spill from available observations. Sampling the posterior distribution, however, can be computationally prohibitive unless the forward mode...

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Bibliographic Details
Published in:Computational geosciences 2024-08, Vol.28 (4), p.681-696
Main Authors: El Mohtar, Samah, Le Maître, Olivier, Knio, Omar, Hoteit, Ibrahim
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Bayesian analysis
Bayesian theory
Chemical spills
Conditional probability
Construction
Earth and Environmental Science
Earth Sciences
Earthquakes
Environmental cleanup
Environmental Sciences
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Identification
Mathematical Modeling and Industrial Mathematics
Mathematics
Oil spills
Original Paper
Parameter estimation
Parameter identification
Parameter uncertainty
Parameters
Performance assessment
Polynomials
Probability theory
Soil Science & Conservation
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Summary:Identifying the source of an oil spill is an essential step in environmental forensics. The Bayesian approach allows to estimate the source parameters of an oil spill from available observations. Sampling the posterior distribution, however, can be computationally prohibitive unless the forward model is replaced by an inexpensive surrogate. Yet the construction of globally accurate surrogates can be challenging when the forward model exhibits strong nonlinear variations. We present an iterative data-driven algorithm for the construction of polynomial chaos surrogates whose accuracy is localized in regions of high posterior probability. Two synthetic oil spill experiments, in which the construction of prior-based surrogates is not feasible, are conducted to assess the performance of the proposed algorithm in estimating five source parameters. The algorithm successfully provided a good approximation of the posterior distribution and accelerated the estimation of the oil spill source parameters and their uncertainties by an order of 100 folds.
ISSN:1420-0597
1573-1499
DOI:10.1007/s10596-024-10288-9