Robust snow avalanche detection using supervised machine learning with infrasonic sensor arrays

Automated detection of snow avalanches is crucial to assess the effectiveness of avalanche control by explosions, and to monitor avalanche activity in a given area in view of avalanche forecasting. Several automated or semi-automated detection technologies have been developed in the past among which infrasound-based detection is the most promising for regional-scale avalanche monitoring. However, due to significant ambient noise content in infrasonic signals, e.g. from atmospheric processes or airplanes, fully automated and reliable avalanche detection has been very challenging. Signal processing is highly critical and strongly affects detection accuracy. Here, a robust detection method by using supervised machine learning is introduced. Machine learning algorithms can take into account multiple signal features and statistically optimize the classification task. We analyzed infrasound data with concurrent visual avalanche observations from the test site Lavin (Eastern Swiss Alps) for the winter of 2011–2012. A support vector machine was trained by using training data from the first half of the winter season and the accuracy was tested on data from the second half of the season. A significant reduction of false detections, from 65% to 10%, was achieved compared to a threshold-based classifier provided by the sensor manufacturer. The proposed method enables reliable assessment of the avalanche activity in the surroundings of the system and paves the way towards robust and fully automated avalanche detection using infrasonic systems.