# End-to-end LSTM based estimation of volcano event epicenter localization

arXiv: https://arxiv.org/abs/2110.14594 · HTML: https://mlsanity.com/paper/2110.14594/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 51 references that are in the map.
- First posted on arXiv 27 Oct 2021.

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

- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networks](https://mlsanity.com/paper/1909.09586/) (2019)
- [Towards Fast and Accurate Streaming End-to-End ASR](https://mlsanity.com/paper/2004.11544/) (2020)
- [End-to-End ASR-free Keyword Search from Speech](https://mlsanity.com/paper/1701.04313/) (2017)
- [A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets](https://mlsanity.com/paper/1905.07286/) (2019)
- [End-To-End Speech Recognition Using A High Rank LSTM-CTC Based Model](https://mlsanity.com/paper/1903.05261/) (2019)

## Built on by (most cited first, 2 of 2)

- [SeisLM: a Foundation Model for Seismic Waveforms](https://mlsanity.com/paper/2410.15765/) (2024)
- [Exploring Challenges in Deep Learning of Single-Station Ground Motion Records](https://mlsanity.com/paper/2403.07569/) (2024)

## Abstract (arXiv)

In this paper, an end-to-end based LSTM scheme is proposed to address the problem of volcano event localization without any a priori model relating phase picking with localization estimation. It is worth emphasizing that automatic phase picking in volcano signals is highly inaccurate because of the short distances between the event epicenters and the seismograph stations. LSTM was chosen due to its capability to capture the dynamics of time varying signals, and to remove or add information within the memory cell state and model long-term dependencies. A brief insight into LSTM is also discussed here. The results presented in this paper show that the LSTM based architecture provided a success rate, i.e., an error smaller than 1.0Km, equal to 48.5%, which in turn is dramatically superior to the one delivered by automatic phase picking. Moreover, the proposed end-to-end LSTM based method gave a success rate 18% higher than CNN.