Debris flows at Illgraben, Switzerland – From seismic wiggles to machine learning

Abstract

AbstractWhere debris flows can impinge on the built environment, early detection of flow events is necessary in order to issue warnings and initiate countermeasures like road or train track closure. In this contribution, previously published work at Illgraben, Switzerland, is compiled, which shows how seismic measurements can be used for debris flow monitoring and warning. The advantage of the seismic approach is that debris flow signals can be detected at large distances eliminating the need for installations within or close to torrents, which are usually difficult to access. It is shown that seismic data contain important information about debris flows, including initiation, propagation, and particle sizes. However, machine learning algorithms, which are tuned with example data rather than physical principles, so far offer the best performance for debris flow detection with continuous real‐time data. Such algorithms pave the way for a new class of warning systems, based on data science techniques rather than in‐torrent instrumentation.