# Generalized Neural Networks for Real-Time Earthquake Early Warning

arXiv: https://arxiv.org/abs/2312.15218 · HTML: https://mlsanity.com/paper/2312.15218/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 55 references that are in the map.
- First posted on arXiv 23 Dec 2023.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Bayesian-Deep-Learning Estimation of Earthquake Location from Single-Station Observations](https://mlsanity.com/paper/1912.01144/) (2019)
- [PhaseLink: A Deep Learning Approach to Seismic Phase Association](https://mlsanity.com/paper/1809.02880/) (2018)
- [Earthquake Phase Association with Graph Neural Networks](https://mlsanity.com/paper/2209.07086/) (2022)
- [A Machine-Learning Approach for Earthquake Magnitude Estimation](https://mlsanity.com/paper/1911.05975/) (2019)
- [Real-time Earthquake Early Warning with Deep Learning: Application to the 2016 Central Apennines, Italy Earthquake Sequence](https://mlsanity.com/paper/2006.01332/) (2020)

## Abstract (arXiv)

Deep learning enhances earthquake monitoring capabilities by mining seismic waveforms directly. However, current neural networks, trained within specific areas, face challenges in generalizing to diverse regions. Here, we employ a data recombination method to create generalized earthquakes occurring at any location with arbitrary station distributions for neural network training. The trained models can then be applied to various regions with different monitoring setups for earthquake detection and parameter evaluation from continuous seismic waveform streams. This allows real-time Earthquake Early Warning (EEW) to be initiated at the very early stages of an occurring earthquake. When applied to substantial earthquake sequences across Japan and California (US), our models reliably report earthquake locations and magnitudes within 4 seconds after the first triggered station, with mean errors of 2.6-6.3 km and 0.05-0.17, respectively. These generalized neural networks facilitate global applications of real-time EEW, eliminating complex empirical configurations typically required by traditional methods.