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dc.contributor.author
Heck, Matthias
dc.contributor.author
Van Herwijnen, Alec
dc.contributor.author
Hammer, Conny
dc.contributor.author
Hobiger, Manuel
dc.contributor.author
Schweizer, Jürg
dc.contributor.author
Fäh, Donat
dc.date.accessioned
2019-06-13T08:08:39Z
dc.date.available
2019-06-13T02:15:53Z
dc.date.available
2019-06-13T08:08:39Z
dc.date.issued
2019
dc.identifier.other
10.5194/esurf-7-491-2019
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/347173
dc.identifier.doi
10.3929/ethz-b-000347173
dc.description.abstract
We used continuous data from a seismic monitoring system to automatically determine the avalanche activity at a remote field site above Davos, Switzerland. The approach is based on combining a machine learning algorithm with array processing techniques to provide an operational method capable of near real-time classification. First, we used a recently developed method based on hidden Markov models (HMMs) to automatically identify events in continuous seismic data using only a single training event. For the 2016–2017 winter period, this resulted in 117 events. Second, to eliminate falsely classified events such as airplanes and local earthquakes, we implemented an additional HMM-based classifier at a second array 14 km away. By cross-checking the results of both arrays, we reduced the number of classifications by about 50 %. In a third and final step we used multiple signal classification (MUSIC), an array processing technique, to determine the direction of the source. As snow avalanches recorded at our arrays typically generate signals with small changes in source direction, events with large changes were dismissed. From the 117 initially detected events during the 4-month period, our classification workflow removed 96 events. The majority of the remaining 21 events were on 9 and 10 March 2017, in line with visual avalanche observations in the Davos region. Our results suggest that the classification workflow presented could be used to identify major avalanche periods and highlight the importance of array processing techniques for the automatic classification of avalanches in seismic data.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Copernicus Publications
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Automatic detection of avalanches combining array classification and localization
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2019-06-03
ethz.journal.title
Earth Surface Dynamics
ethz.journal.volume
7
en_US
ethz.journal.issue
2
en_US
ethz.pages.start
491
en_US
ethz.pages.end
503
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Göttingen
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00003 - Schulleitung und Dienste::00022 - Bereich VP Forschung & Wirtschaftsbez. / Domain VP Research & Corporate Relations::02818 - Schweiz. Erdbebendienst (SED) / Swiss Seismological Service (SED)
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00003 - Schulleitung und Dienste::00022 - Bereich VP Forschung & Wirtschaftsbez. / Domain VP Research & Corporate Relations::02818 - Schweiz. Erdbebendienst (SED) / Swiss Seismological Service (SED)
ethz.date.deposited
2019-06-13T02:15:54Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2019-06-13T08:08:54Z
ethz.rosetta.lastUpdated
2019-06-13T08:08:54Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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