# Fast Information Streaming Handler (FisH): A Unified Seismic Neural Network for Single Station Real-Time Earthquake Early Warning

arXiv: https://arxiv.org/abs/2408.06629 · HTML: https://mlsanity.com/paper/2408.06629/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 38 references that are in the map.
- First posted on arXiv 13 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Retentive Network: A Successor to Transformer for Large Language Models](https://mlsanity.com/paper/2307.08621/) (2023)
- [Bayesian-Deep-Learning Estimation of Earthquake Location from Single-Station Observations](https://mlsanity.com/paper/1912.01144/) (2019)
- [A Machine-Learning Approach for Earthquake Magnitude Estimation](https://mlsanity.com/paper/1911.05975/) (2019)

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

- [Earth Science Foundation Models: From Perception to Reasoning and Discovery](https://mlsanity.com/paper/2605.12542/) (2026)

## Abstract (arXiv)

Existing EEW approaches often treat phase picking, location estimation, and magnitude estimation as separate tasks, lacking a unified framework. Additionally, most deep learning models in seismology rely on full three-component waveforms and are not suitable for real-time streaming data. To address these limitations, we propose a novel unified seismic neural network called Fast Information Streaming Handler (FisH). FisH is designed to process real-time streaming seismic data and generate simultaneous results for phase picking, location estimation, and magnitude estimation in an end-to-end fashion. By integrating these tasks within a single model, FisH simplifies the overall process and leverages the nonlinear relationships between tasks for improved performance. The FisH model utilizes RetNet as its backbone, enabling parallel processing during training and recurrent handling during inference. This capability makes FisH suitable for real-time applications, reducing latency in EEW systems. Extensive experiments conducted on the STEAD benchmark dataset provide strong validation for the effectiveness of our proposed FisH model. The results demonstrate that FisH achieves impressive performance across multiple seismic event detection and characterization tasks. Specifically, it achieves an F1 score of 0.99/0.96. Also, FisH demonstrates precise earthquake location estimation, with location error of only 6.0km, a distance error of 2.6km, and a back-azimuth error of 19deg. The model also exhibits accurate earthquake magnitude estimation, with a magnitude error of just 0.14. Additionally, FisH is capable of generating real-time estimations, providing location and magnitude estimations with a location error of 8.06km and a magnitude error of 0.18 within a mere 3 seconds after the P-wave arrives.