Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/10432
Authors: Falsaperla, S.* 
Langer, H.* 
Ferrazzini, V.* 
Title: First results from pattern classification applied to seismic data recorded at Piton de la Fournaise (La Réunion)
Issue Date: 6-Apr-2016
Publisher: MISCELLANEA INGV
URI: http://hdl.handle.net/2122/10432
Keywords: Pattern classification
seismic data
Piton de la Fournaise
Subject Classification04. Solid Earth::04.06. Seismology::04.06.06. Surveys, measurements, and monitoring 
Abstract: Data mining tools were tested within the WP 5 - Task 5.1 “Characterization of the threatening phenomena from space and ground” of the European MEDiterrranean Supersite Volcanoes (MED­SUV) project to tackle various classification and pattern recognition problems. These methods were successfully exploited for the identification of impending volcanic activity at Mt. Etna (Italy). Benefiting from the positive experiences acquired, we explored the application of one of these tools to seismic data recorded at Piton de la Fournaise (La Réunion) volcano, which the WP 7 “Pilot Phase - Validation and transfer of project outcome” of MED-SUV identified as ideal test site for the validation of innovative concepts for early-warning purposes. Our case study analyzes the time span from 2014 to 2015, during which episodes of lava fountains and lava flows occurred at the Dolomieu Crater. Their duration ranged from a few hours to about two months. For this application, we processed two years of continuous seismic data, providing a specific tuning of the software. We present our preliminary results considering the frequency content of the background seismic radiation at the broadband 3C station RVL, which was located close to the base of the Dolomieu cone and to the eruptive centers. Results of pattern classification applied to seismic data recorded during eruptive episodes at Mt. Etna are also presented for comparison.
Appears in Collections:Conference materials

Files in This Item:
File Description SizeFormat 
MISCELLANEA_INGV31_abstract2016_Piton_MEDSUV.pdfAbstract404.32 kBAdobe PDFView/Open
Show full item record

Page view(s)

25
Last Week
1
Last month
0
checked on Oct 17, 2018

Download(s)

57
checked on Oct 17, 2018

Google ScholarTM

Check