Journal cover Journal topic
Earth Surface Dynamics An interactive open-access journal of the European Geosciences Union

Journal metrics

  • IF value: 2.649 IF 2.649
  • IF 5-year<br/> value: 2.688 IF 5-year
    2.688
  • CiteScore<br/> value: 2.64 CiteScore
    2.64
  • SNIP value: 0.628 SNIP 0.628
  • SJR value: indexed SJR
    indexed
  • IPP value: 1.689 IPP 1.689
  • h5-index value: 6 h5-index 6
https://doi.org/10.5194/esurf-2018-36
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
03 May 2018
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Earth Surface Dynamics (ESurf).
Automatic detection of avalanches using a combined array classification and localization
Matthias Heck1, Alec van Herwijnen1, Conny Hammer2, Manuel Hobiger2, Jürg Schweizer1, and Donat Fäh2 1WSL Institute for Snow and Avalanche Research SLF, Davos
2Swiss Seismological Service SED, ETH Zurich, Zurich
Abstract. We use a seismic monitoring system to automatically determine the avalanche activity at a remote field site near Davos, Switzerland. By using a recently developed approach based on hidden Markov models (HMMs), a machine learning algorithm, we were able to automatically identify avalanches in continuous seismic data by providing as little as one single training event. Furthermore, we implemented an operational method to provide near real-time classification results. For the 2016–2017 winter period 117 events were automatically identified. False classified events such as airplanes and local earthquakes were filtered using a new approach containing two additional classification steps. In a first step, we implemented a second HMM based classifier at a second array 14 km away to automatically identify airplanes and earthquakes. By cross-checking the results of both arrays we reduced the amount of false classifications by about 50 %. In a second step, we used multiple signal classifications (MUSIC), an array processing technique to determine the direction of the source. Although avalanche events have a moving source character only small changes of the source direction are common for snow avalanches whereas false classifications had large changes in the source direction and were therefore dismissed. From the 117 detected events during the 4 month period we were able to identify 90 false classifications based on these two additional steps. The obtained avalanche activity based on the remaining 27 avalanche events was in line with visual observations performed in the area of Davos.
Citation: Heck, M., van Herwijnen, A., Hammer, C., Hobiger, M., Schweizer, J., and Fäh, D.: Automatic detection of avalanches using a combined array classification and localization, Earth Surf. Dynam. Discuss., https://doi.org/10.5194/esurf-2018-36, in review, 2018.
Matthias Heck et al.
Matthias Heck et al.
Matthias Heck et al.

Viewed

Total article views: 142 (including HTML, PDF, and XML)

HTML PDF XML Total BibTeX EndNote
115 22 5 142 3 3

Views and downloads (calculated since 03 May 2018)

Cumulative views and downloads (calculated since 03 May 2018)

Viewed (geographical distribution)

Total article views: 142 (including HTML, PDF, and XML)

Thereof 142 with geography defined and 0 with unknown origin.

Country # Views %
  • 1

Saved

Discussed

Latest update: 19 May 2018
Publications Copernicus
Download
Short summary
In this work we combined the results of the automatic classification of continuous seismic data recorded at two arrays to identify avalanches. By using an additional post-processing step based on the localization of avalanches, we were able to identify 27 avalanches during the winter period 2016–2017. Comparing these results with visually identified avalanches, we were able to reconstruct the main avalanche periods of the winter season.
In this work we combined the results of the automatic classification of continuous seismic data...
Share