Repositorio
Institucional

Becerra Yoma, Nestor

Wuth, Jorge

Pinto, Andres

de Celis, Nicolas

Celis, Jorge

Huenupan, Fernando

Janos Fustos-Toribio, Ivo

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

ELSEVIER

JOURNAL OF VOLCANOLOGY AND GEOTHERMAL RESEARCH

en

Locating sources of volcano-seismic event is very relevant to monitor and comprehend volcanic processes. Ordinary estimation of source seismic events is based on phase picking. The most accurate procedure of phase selection is the visual inspection of the records by experts, who employ local characteristics for phase detection and comparison with observed signals from other stations. This activity is highly time demanding, which in turn is a strong motivation to automatize the epicenter estimation process. However, automatic phase picking in volcano signals is highly inaccurate because of the short distances between the event epicenters and the seismograph stations. In this paper, an end-to-end based LSTM (Long-Short Term Memory) scheme is proposed to address the problem of volcano event localization without any a priori model relating phase picking with localization estimation. 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 to justify the use of this neural network. The results presented in this paper show that the LSTM based architecture provided a success rate, i.e., an error smaller than 1.0 km, 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 (Convolutional Neural Network). The results presented suggest that the approach proposed here for automatic volcano event epicenter localization can be applied to other geophysics problems.

artículo original

The research reported in this paper was funded by grant ANID/FONDEF ID19I-10397 and ID20I-10212.

La investigación reportada en este artículo fue financiada con las subvenciones ANID/FONDEF ID19I-10397 y ID20I-10212.

10.1016/j.jvolgeores.2022.107615

PDF

Volcano event epicenter estimation

LSTM

End-to-end processing

SEISMIC EVENTS

PICKING

CLASSIFICATION

Geociencias

Multidisciplinario

Estimación del epicentro del evento volcánico

LSTM

Procesamiento de extremo a extremo

EVENTOS SÍSMICOS

SELECCION

CLASIFICACIÓN

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

artículo original

version publicada

0

metadata

metadata

arXiv:2110.14594v1

ANID-FONDEF ID19I-10397

ANID-FONDEF ID20I-10212

ANID FONDEF ID19I-10397

ANID FONDEF ID20I-10212

WOS:000833539100005