Flowy: High performance probabilistic lava emplacement prediction

Moritz Sallermann; Amrita Goswami; Alejandro Peña-Torres; Rohit Goswami

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arXiv · 2405.20144

Flowy: High performance probabilistic lava emplacement prediction

Moritz Sallermann·Amrita Goswami·Alejandro Peña-Torres·Rohit Goswami

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Abstract

Lava emplacement is a complex physical phenomenon, affected by several factors. These include, but are not limited to features of the terrain, the lava settling process, the effusion rate or total erupted volume, and the probability of effusion from different locations. One method, which has been successfully employed to predict lava flow emplacement and forecast the inundated area and final lava thickness, is the MrLavaLoba method from Vitturi et al. The MrLavaLoba method has been implemented in their code of the same name. Here, we introduce Flowy, a new computational tool that implements the MrLavaLoba method in a more efficient manner. New fast algorithms have been incorporated for all performance critical code paths, resulting in a complete overhaul of the implementation. When compared to the MrLavaLoba code, Flowy exhibits a significant reduction in runtime -- between 100 to 400 times faster -- depending on the specific input parameters. The accuracy and the probabilistic convergence of the model outputs are not compromised, maintaining high fidelity in generating possible lava flow paths and deposition characteristics. We have validated Flowy's performance and reliability through comprehensive unit-testing and a real-world eruption scenario. The source code is freely available on GitHub, facilitating transparency, reproducibility and collaboration within the geoscientific community.

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BibTeX RIS

Moritz Sallermann, Amrita Goswami, Alejandro Peña-Torres, Rohit Goswami. 2024-05-30. Flowy: High performance probabilistic lava emplacement prediction. https://arxiv.org/abs/2405.20144

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