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

TL;DR

This paper introduces an end-to-end LSTM approach for volcano epicenter localization that outperforms traditional phase picking and CNN methods, achieving nearly 49% accuracy with errors under 1 km.

Contribution

It presents a novel LSTM-based model that directly estimates volcano epicenters without relying on prior phase picking models, improving localization accuracy.

Findings

LSTM achieved a 48.5% success rate with errors under 1 km.

The method outperforms automatic phase picking and CNN-based approaches.

End-to-end LSTM approach enhances volcano event localization accuracy.

Abstract

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.