Phenomenology of Avalanche Recordings from Distributed Acoustic Sensing

This is the <strong>electronic supplemental data </strong>for the publication entitled <strong>"Phenomenology of Avalanche Recordings from Distributed Acoustic Sensing"</strong> submitted to the Journal of Geophysical Research (JGR): Earth Surface. The <strong><em>main_jupyter_notebook.ipynb </em></strong>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 <strong>Requirements</strong> 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<br> jupyter_client 7.3.5<br> jupyter_console 6.4.3 <br> jupyter_core 4.11.2<br> jupyter_server 1.18.1<br> jupyterlab 3.4.4 <br> jupyterlab_pygments 0.1.2<br> jupyterlab_server 2.15.2<br> 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)