A multi-station volcano-tectonic earthquakes monitoring based on Transfer Learning techniques.

A multi-station volcano-tectonic earthquakes monitoring based on Transfer Learning techniques. Manuel Titos (1), Ligdamis Gutiérrez (2,3), Carmen Benítez (1), Pablo Rey Devesa (2,3), Ivan Koulakov (4) and Jesús. M. Ibáñez (2,3) Institutions associated: (1) CITIC, Department of Signal Processing, Telematic and Communications, University of Granada, 18071. Granada. Spain. (2) Department of Theoretical Physics and Cosmos. Science Faculty. Avd. Fuentenueva s/n. University of Granada. 18071. Granada. Spain. (3) Andalusian Institute of Geophysiscs. Campus de Cartuja. University of Granada. C/Profesor Clavera 12. 18071. Granada. Spain. (4) Laboratory for Seismic Forward and Inverse Problems, Institute of Petroleum Geology and Geophysics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Acknowledgment: This is a short text to acknowledge the contributions of specific colleagues, institutions, or agencies that aided the efforts of the authors. a) This work is part of the research by the Spanish FEMALE project (PID2019-106260GB-I00). FEMALE (Forecasting Volcanic Eruptions Using Signal Processing and Machine Learning Techniques on Seismic Signals) https://femalevolcanoes.es/ b) JMI and LG were partially funded by the Spanish project PROOF-FOREVER (EUR2022.134044). Keywords: Automatic volcanic monitoring, real-time monitoring, Artificial Intelligence, Transfer Learning, Recurrent Neural Networks, Temporal Convolutional Networks. Data availability statement: Seismic data from Bezymianny volcano (2017), Kamchatka, Russia. Contents: Seismic Data from Bezymianny volcano recorded at stations BZ01, BZ02, BZ06 and BZ10. The data represent the vertical component of the seismic signal, associated to the period analyzed in the study