A successful short-term volcanic eruption forecasting using seismic features: datasets and Sotware

Resumen

Successful Short-Term Volcanic Eruption Forecasting Using Seismic Features, Suplementary Material by Rey-Devesa (1,2), Benítez (3), Prudencio, Ligdamis Gutiérrez (1,2), Cortés (1,2), Titos (3), Koulakov (4,5), Zuccarello (6) and Ibáñez (1,2). <br> Institutions associated: (1) Department of Theoretical Physics and Cosmos. Science Faculty. Avd. Fuentenueva s/n. University of Granada. 18071. Granada. Spain. (2) Andalusian Institute of Geophysiscs. Campus de Cartuja. University of Granada. C/Profesor Clavera 12. 18071. Granada. Spain. (3) Department of Signal Theory, Telematics and Communication. University of Granada. Informatics and Telecommunication School. 18071. Granada. Spain. (4) Trofimuk Institute of Petroleum Geology and Geophysics SB RAS, Prospekt Koptyuga, 3, 630090 Novosibirsk, Russia (5) Institute of the Earth’s Crust SB RAS, Lermontova 128, Irkutsk, Russia (6) Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa (INGV-Pisa), via Cesare Battisti, 53, 56125, Pisa, Italy. <br> Acknowledgment: This study was partially supported by the Spanish FEMALE project (PID2019-106260GB-I00).<br> P. Rey-Devesa was funded by the Ministerio de Ciencia e Innovación del Gobierno de España (MCIN),<br> Agencia Estatal de Investigación (AEI), Fondo Social Europeo (FSE),<br> and Programa Estatal de Promoción del Talento y su Empleabilidad en I+D+I Ayudas para contratos predoctorales para la formación de doctores 2020 (PRE2020-092719).<br> Ivan Koulakov was supported by the Russian Science Foundation (Grant No. 20-17-00075).<br> Luciano Zuccarello was supported by the INGV Pianeta Dinamico 2021 Tema 8 SOME project (grant no. CUP D53J1900017001)<br> funded by the Italian Ministry of University and Research<br> “Fondo finalizzato al rilancio degli investimenti delle amministrazioni centrali dello Stato e allo sviluppo del Paese, legge 145/2018”.<br> English language editing was performed by Tornillo Scientific, UK. <br> Data availability statement: 1.- Seismic data from Kilauea, Augustine, Bezymianny (2007), and Mount St. Helens are available from the IRIS data repository (http://ds.iris.edu/seismon/index.phtml).<br> (An example of the Python code to access the data is described below.)<br> 2.- Seismic data from Bezymianny (2017-2018) are available from Ivan Koulakov (ivan.science@gmail.com) upon request.<br> 3.- Seismic data from Mt. Etna are available from INGV-Italy upon request (http://terremoti.ingv.it/en/help),<br> also available from the Zenodo data repository (https://doi.org/10.5281/zenodo.6849621). Access code in Python to download the records of Kilauea, Augustine and Mount St. Helens volcanoes, from the IRIS data repository. '''To access the raw signals please first install ObsPy and then execute following commands in a python console: ''' Example: from obspy.core import UTCDateTime<br> from obspy.clients.fdsn import Client<br> import obspy.io.mseed<br> client = Client('IRIS')<br> t1 = UTCDateTime('2006-01-10T00:00:00')<br> t2 = UTCDateTime('2006-01-12T00:00:00')<br> raw_data = client.get_waveforms(<br> network='AV',<br> station='AUH',<br> location='',<br> channel='HHZ',<br> starttime=t1,<br> endtime=t2) '''To further download station information execute: ''' xml = client.get_stations(network='AV',station='AUH', <br> channel='HHZ',starttime=t1,endtime=t2,level='response') ''' 'To scale the data using the station’s meta-data: ''' data = raw_data.remove_response(inventory=xml) ''' To filter, trim and plot the data execute: ''' data.write("Augustine.mseed", format="MSEED") data.filter('bandpass',freqmin=1.0,freqmax=20)<br> data.trim(t1+60,t2-60)<br> data.plot() Contents: 6 different Matlab codes. The principal code is called FeatureExtraction.<br> The codes rsac.m and ReadMSEEDFast.m are for reading different format of data. (Not developed by the group)<br> Seismic Data from Mt. Etna for using as an example.