Journal metrics

Journal metrics

  • IF value: 3.176 IF 3.176
  • IF 5-year value: 3.108 IF 5-year 3.108
  • CiteScore value: 3.06 CiteScore 3.06
  • SNIP value: 0.978 SNIP 0.978
  • SJR value: 1.421 SJR 1.421
  • IPP value: 2.88 IPP 2.88
  • h5-index value: 13 h5-index 13
  • Scimago H index value: 13 Scimago H index 13
Discussion papers | Copyright
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 03 May 2018

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 Matthias Heck et al.
  • 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 14km 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.

Download & links
Matthias Heck et al.
Interactive discussion
Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Matthias Heck et al.
Matthias Heck et al.
Total article views: 399 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
340 50 9 399 6 6
  • HTML: 340
  • PDF: 50
  • XML: 9
  • Total: 399
  • BibTeX: 6
  • EndNote: 6
Views and downloads (calculated since 03 May 2018)
Cumulative views and downloads (calculated since 03 May 2018)
Viewed (geographical distribution)
Total article views: 399 (including HTML, PDF, and XML) Thereof 398 with geography defined and 1 with unknown origin.
Country # Views %
  • 1
No saved metrics found.
No discussed metrics found.
Latest update: 21 Jul 2018
Publications Copernicus
Special issue
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...