Global optimization for data assimilation in landslide tsunami models

•We develop a generic data assimilation framework for landslide tsunami models.•The data assimilation problem is posed in a global optimization framework.•Parallel and efficient global optimization algorithms are developed.•We assess the identifiability of model parameters for a landslide tsunami mo...

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Bibliographic Details
Published in:Journal of computational physics 2020-02, Vol.403, p.109069, Article 109069
Main Authors: Ferreiro-Ferreiro, A.M., García-Rodríguez, J.A., López-Salas, J.G., Escalante, C., Castro, M.J.
Format: Article
Language:English
Subjects:
Algorithms
Avalanches
Computational physics
Computer simulation
Coupling
Data assimilation
Finite volume method
Finite volume methods
Global optimization
Heuristic methods
Landslides
Multilayers
Nonlinear programming
Optimization
Parallel computing
Simulated annealing
Submarine avalanches
Tsunamis
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Summary:•We develop a generic data assimilation framework for landslide tsunami models.•The data assimilation problem is posed in a global optimization framework.•Parallel and efficient global optimization algorithms are developed.•We assess the identifiability of model parameters for a landslide tsunami model.•The developed machinery is tested with real laboratory data. The goal of this article is to make automatic data assimilation for a landslide tsunami model, given by the coupling between a non-hydrostatic multi-layer shallow-water and a Savage-Hutter granular landslide model for submarine avalanches. The coupled model is discretized using a positivity preserving second-order path-conservative finite volume scheme. Then, the data assimilation problem is posed in a global optimization framework. Later, multi-path parallel metaheuristic stochastic global optimization algorithms are developed. More precisely, a multi-path Simulated Annealing algorithm is compared with a multi-path hybrid global optimization algorithm based on coupling Simulated Annealing with gradient local searchers.
ISSN:0021-9991
1090-2716
DOI:10.1016/j.jcp.2019.109069