AI- and HPC–Driven Tsunami Decision Support for the Spanish TEWS: Atlantic Results and Western Mediterranean Extension

Tsunami Early Warning Systems (TEWS) in the NEAM region (North-East Atlantic, the Mediterranean, and connected seas) operate under strict time constraints, particularly for near-field events where coastal impact may occur within a few minutes. In the NEAM region, operational chains typically use decision matrices and precomputed scenario databases. In Spain, the TEWS is operated by the Instituto Geográfico Nacional (IGN), and this work is carried out jointly with IGN to support operational decision-making. These established tools can be reinforced with rapid products that provide early indicators of coastal impact within some minutes or even seconds of the first source estimate. One example is the use of Faster-Than-Real-Time (FTRT) simulations, already implemented in the current system.

Here we present a workflow in which neural-network surrogates are trained on large sets of physics-based tsunami scenarios, enabling fast inference of coastal impact metrics. The Tsunami-HySEA code is used to generate large-scale simulation sets, providing the data required by models designed for near-instant inference on standard CPUs. The surrogates models learn to map solid Earth earthquake source descriptors (capturing some uncertainty in fault parameters) to warning-relevant coastal metrics, focusing on maximum wave height and first-arrival time at multiple sites. Once trained, the models deliver predictions within seconds, facilitating rapid updates as source estimates evolve. Model interpretability is assessed using SHAP values, confirming how each input influences the predictions. The results confirm that the patterns follow the physical principles of tsunami generation and propagation. In an operational workflow, model results are fed into an automated reporting layer that produces tables, maps and graphics for Civil Protection within seconds, enabling rapid situational updates as source estimates evolve.

We first report initial results for Atlantic sources affecting SW Spain. Approximately 250,000 HySEA simulations covering multiple Atlantic fault segments, focal mechanisms and magnitudes were used to train models. The results for forecast points along the Huelva–Cádiz coast show good agreement with observed patterns of maximum wave height and meet operational speed requirements, with errors remaining within the acceptable range for TEWS procedures. We then describe the extension of the methodology to the Western Mediterranean, covering the Spanish Mediterranean coast and the Balearic Islands. This extension involves defining and parameterising multiple tsunamigenic fault systems, assembling and controlling the quality of high-resolution topo-bathymetric datasets, and designing robust training and validation strategies.

A practical limitation is that, despite comprehensive coverage of the targeted fault systems, rare source realisations or parameter combinations may fall outside the effective support of the training distribution, which can reduce reliability of point predictions. To handle such cases in operations, we complement deterministic estimates with threshold exceedance probabilities, enabling risk-aware decisions while preserving consistency with established TEWS procedures.