# Attention-Based Reconstruction of Full-Field Tsunami Waves from Sparse Tsunameter Networks

arXiv: https://arxiv.org/abs/2411.12948 · HTML: https://mlsanity.com/paper/2411.12948/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 54 references that are in the map.
- First posted on arXiv 20 Nov 2024.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Data-Driven Sparse Sensor Placement for Reconstruction](https://mlsanity.com/paper/1701.07569/) (2017)
- [Learning of Sea Surface Height Interpolation from Multi-variate Simulated Satellite Observations](https://mlsanity.com/paper/2310.07626/) (2023)
- [Reconstruction of Fields from Sparse Sensing: Differentiable Sensor Placement Enhances Generalization](https://mlsanity.com/paper/2312.09176/) (2023)

## Built on by (most cited first, 1 of 1)

- [Reduced Order Modeling for Tsunami Forecasting with Bayesian Hierarchical Pooling](https://mlsanity.com/paper/2512.19804/) (2025)

## Abstract (arXiv)

We investigate the potential of an attention-based neural network architecture, the Senseiver, for sparse sensing in tsunami forecasting. Specifically, we focus on the Tsunami Data Assimilation Method, which generates forecasts from tsunameter networks. Our model is used to reconstruct high-resolution tsunami wavefields from extremely sparse observations, including cases where the tsunami epicenters are not represented in the training set. Furthermore, we demonstrate that our approach significantly outperforms the Linear Interpolation with Huygens-Fresnel Principle in generating dense observation networks, achieving markedly improved accuracy.