Phenomenology of Avalanche Recordings from Distributed Acoustic Sensing

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)