Data Science and Mass Movement Seismology: Towards the Next Generation of Debris Flow Warning

October 24, 2022
In Progress
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Mobilized during heavy precipitation, debris flows are sediment-water mixtures moving down steep mountain torrents in an uncontrolled fashion. The triggering mechanisms of this severe natural hazard are difficult to quantify, because sediment mobilization occurs in high catchment areas with limited access. In this project we propose to monitor debris flow formation and preparatory mass
movements like rockfalls and landslides using seismic data. To tackle the longstanding challenge of automatic and reliable mass movement detection with real-time seismic data, we propose innovative machine learning approaches. A specific goal is to develop transferable algorithms to detect debris flows at arbitrary sites and thus to exploit existing seismic infrastructure in Alpine terrain.

The project focuses on seismic records from Illgraben, an active debris flow torrent in Switzerland. Using a four-year seismic record we aim to monitor sediment production and deposition, which precondition the catchment for debris flow occurrence. This will allow for a better understanding of debris flow triggering by precipitation, snow melt, temperature variations and other environmental
forcings. The next step of transferable debris flow detection requires unsupervised machine learning algorithms and thus significant improvement to existing supervised approaches.

Debris flow hazard is expected to grow during future climate change and as a result of increasing population pressure in mountain regions. Our project addresses the need to warn against this threat. The possibility to detect events remotely via seismic techniques that do not require instrumentation in poorly accessible terrain aims to significantly increase warning time and thus our capabilities to protect human lives and infrastructure.



SDSC Team:
Francois Kamper
Nathanaël Perraudin
Michele Volpi

PI | Partners:

Swiss Federal Institute for Forest, Snow and Landscape Research:

  • Dr. Fabian Walter
  • Dr. Patrick Paitz

More info

ETH Zurich, Computer Engineering and Networks Laboratory:

  • Prof. Dr. Lothar Thiele

More info

EPFL, Civil and Environmental Engineering:

  • Prof. Dr. Olga Fink

More info



The main goal of the project is the development of a machine learning model capable of detecting debris-flows from seismic data. A major motivating factor is that such models can be used in the design (or aid) of early-warning systems, helping to protect human lives and infrastructure. Furthermore, since seismic coverage is growing at an accelerated rate, these models can be applied to a variety of locations without the need  for  installing additional infrastructure.

Proposed Approach

A random forest has been shown to be successful in predicting debris-flows from seismic data in the Illgraben region, however this model does not generalize well to data from other seismic networks. Our approach is to train several machine-learning based debris-flow detectors to the Illgraben data and utilize domain adaption techniques to improve generalization to other seismic networks. For this purpose we are investigating ideas from supervised, positive-unlabeled, semi-supervised  and unsupervised learning,


With the advent new technologies, seismometer coverage is growing at an accelerated pace.  The proposed project breaks the ground for turning already installed seismometers into mass movement detectors. While we focus on the analysis of seismic data, future extensions with real-time data of meteorological conditions influencing debris flow formation will improve detection accuracy.

Figure 1. (A) Illgraben catchment. The torrent parts in the upper catchment and its lower parts are circled in blue and green. The channel length in the upper catchment (blue oval) is around 2.5 km. Triangles denote selected seismometer locations and red star the highest in-torrent detection point for debris flows. (B) Debris flow front (ca. 3 m flow depth) on lower part of Illgraben. The largest boulders have diameters of several meters. (C) Continuous classification of seismic data in 2017 using the Random Forest Algorithm (Chmiel et al., 2021). Most time windows are correctly classified as noise (beige pixels). Debris flows are identified by detection in the upper catchment (blue pixels) followed by detections in the lower part (green pixels). Requiring consecutive detections for alarm declaration suppresses false positives. (D) Debris flow seismograms showing the front propagation from the highest stations (blue) towards Rhône Valley stations (green). (E) Seismograms of local earthquake and visually confirmed rockfall.




  • Paitz, P., Chmiel, M., Husmann, L., Volpi, M., Kamper, F., and Walter, F.: Towards an unsupervised generic seismic detector for hazardous mass-movements: a data-driven approach, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-6300, https://doi.org/10.5194/egusphere-egu23-6300, 2023.
  • Paitz, P., Chmiel, M., Husmann, L., Volpi, M., Kamper, F., and Walter, F.: Generic seismic mass-movement detection leveraging unsupervised statistical learning methods.  Machine Learning in Geo-, Ocean and Space Sciences. IUGG General Assembly 2023. Berlin, Germany, 11-20 July 2023, IUGG23-0742.

Additional resources


  1. Coussot, P., & Meunier, M. (1996). Recognition, classification and mechanical description of debris flows. Earth-Science Reviews, 40(3-4), 209-227.
  2. Bahavar, M., Allstadt, K. E., Van Fossen, M., Malone, S. D., & Trabant, C. (2019). Exotic seismic events catalog (ESEC) data product. Seismological Research Letters, 90(3), 1355-1363
  3. Wenner, M., Hibert, C., van Herwijnen, A., Meier, L., and Walter, F.: Near-real-time automated classification of seismic signals of slope failures with continuous random forests, Natural Hazards and Earth System Sciences, 21, 339–361, 2021
  4. Chmiel, M., Walter, F., Wenner, M., Zhang, Z., McArdell, B. W., & Hibert, C. (2021). Machine Learning
    improves debris flow warning. Geophysical Research Letters, 48(3), e2020GL090874.


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