Snow Avalanche Dynamics
Published December 1, 2022 | Version v0.01
Dataset Restricted

Phenomenology of Avalanche Recordings from Distributed Acoustic Sensing

Authors/Creators

  • 1. WSL, Swiss Federal Institute for Forest, Snow and Landscape Research, Birmensdorf, Switzerland
  • 2. ETH Zürich
  • 3. SLF, WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland

Description

This is the electronic supplemental data for the publication entitled 

"Phenomenology of Avalanche Recordings from Distributed Acoustic Sensing"

submitted to the Journal of Geophysical Research (JGR): Earth Surface. 

The main_jupyter_notebook.ipynb shows an example workflow on how to read the data and utilize the Bayesian Gaussian Mixture Model on the extracted features to predict different classes (/clusters) within the avalanche recordings. 

The pre-print will be available on ESSOAr (currently processing submission, as of Dec1., 2022): https://doi.org/10.1002/essoar.10512949.1

 

Requirements

Code was written in python 3.9.13 (from conda-forge), and the following packages are required (the version in the brackets are for which the code was tested):

  • jupyter (versions see below)
    • jupyter                       1.0.0
      jupyter_client             7.3.5
      jupyter_console         6.4.3 
      jupyter_core              4.11.2
      jupyter_server           1.18.1
      jupyterlab                  3.4.4 
      jupyterlab_pygments 0.1.2
      jupyterlab_server       2.15.2
      jupyterlab_widgets     1.0.0
  • numpy (1.23.3)
  • pandas (1.4.4)
  • scipy (1.9.3)
  • matplotlib (versions see below)
    • matplotlib-base     3.5.2
    • matplotlib-inline    0.1.6
  • cmocean (2.0  from channel conda-forge)
  • sklearn (scikit-learn) (1.1.3)

Notes

Funding Acknowledgements: - ETH Zurich: ETH-01 16-2 (Patrick Paitz) - Swiss National Science Foundation: CRSK-2_190683 (Fabian Walter) - Swiss National Science Foundation: PP00P2_157551/2 (Fabian Walter) - Swiss National Science Foundation: 206021_113069/1 (Betty Sovilla)

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Additional details

Related works

Is supplemented by
Preprint: 10.1002/essoar.10512949.1 (DOI)