Landslide-Tsurrogate v1.0: a computationally efficient framework for probabilistic tsunami hazard assessment applied to Mayotte (France)

Abstract

Landslide-Tsurrogate v1.0 is an open-source Python and MATLAB tool that helps scientists quickly estimate the tsunami hazards generated by submarine landslides. Instead of running thousands of heavy deterministic numerical simulations, the software builds surrogate models that reproduce the main results with a fraction of the computational cost. The method relies on a mathematical approach called generalized polynomial chaos expansion, which efficiently explores how uncertain landslide parameters affect tsunami generation. Users can perform sensitivity analyses, identify the most influential parameters, and quantify the variability of possible outcomes. The tool includes a Jupyter Notebook User Manual and interactive MATLAB and Jupyter Notebook interfaces, making it easy to understand the methodology, set up the surrogate simulations and visualize the results. The Landslide-Tsurrogate v1.0 model's performance is demonstrated through a real-world test case involving five zones in Mayotte (France). For this application, the surrogate models achieve convergence with only 135 deterministic simulations per zone and produce probabilistic results in less than 2 s within the user-friendly interfaces used on a basic laptop, demonstrating the computational efficiency of the approach. Beyond this example, the framework can be applied to any coastal region prone to submarine landslides. By combining physical modeling, statistical analysis, and user-friendly design, Landslide-Tsurrogate v1.0 enables faster and more transparent probabilistic tsunami hazard assessments.