Automatically Detecting Avalanche Events in Passive Seismic Data

Abstract

During the 2010-2011 winter season, we deployed seven geophones on a mountain outside of Davos, Switzerland and collected over 100 days of seismic data containing 385 possible avalanche events (33 confirmed slab avalanches). In this article, we describe our efforts to develop a pattern recognition workflow to automatically detect snow avalanche events from passive seismic data. Our initial workflow consisted of frequency domain feature extraction, cluster-based stratified subsampling, and 100 runs of training and testing of 12 different classification algorithms. When tested on the entire season of data from a single sensor, all twelve machine learning algorithms resulted in mean classification accuracies above 84%, with seven classifiers reaching over 90%. We then experimented with a voting based paradigm that combined information from all seven sensors. This method increased overall accuracy and precision, but performed quite poorly in terms of classifier recall. We, therefore, decided to pursue other signal preprocessing methodologies. We focused our efforts on improving the overall performance of single sensor avalanche detection, and employed spectral flux based event selection to identify events with significant instantaneous increases in spectral energy. With a threshold of 90% relative spectral flux increase, we correctly selected 32 of 33 slab avalanches and reduced our problem space by nearly 98%. When trained and tested on this reduced data set of only significant events, a decision stump classifier achieved 93% overall accuracy, 89.5% recall, and improved the precision of our initial workflow from 2.8% to 13.2%.