14 citations as recorded by crossref.

1. Autoencoder-based feature extraction for the automatic detection of snow avalanches in seismic data A. Simeon et al. https://doi.org/10.5194/gmd-18-8751-2025
2. Prediction of natural dry-snow avalanche activity using physics-based snowpack simulations S. Mayer et al. https://doi.org/10.5194/nhess-23-3445-2023
3. Automatic classification of avalanche and non-avalanche events in seismic data using machine learning . Hemant et al. https://doi.org/10.1007/s12040-026-02895-0
4. When it comes to Earth observations in AI for disaster risk reduction, is it feast or famine? A topical review M. Kuglitsch et al. https://doi.org/10.1088/1748-9326/acf601
5. Timing and identification of potential snow avalanche types: a case study of the central Tianshan Mountains J. Hao et al. https://doi.org/10.1007/s10346-021-01766-7
6. Evaluating the performance of an operational infrasound avalanche detection system at three locations in the Swiss Alps during two winter seasons S. Mayer et al. https://doi.org/10.1016/j.coldregions.2019.102962
7. Church bells and ground motions J. Diaz https://doi.org/10.1007/s10950-020-09935-2
8. Phenomenology of Avalanche Recordings From Distributed Acoustic Sensing P. Paitz et al. https://doi.org/10.1029/2022JF007011
9. Estimation of Avalanche Development and Frontal Velocities Based on the Spectrogram of the Seismic Signals Generated at the Vallée de la Sionne Test Site E. Suriñach et al. https://doi.org/10.3390/geosciences10030113
10. Sensitivity of modeled snow stability data to meteorological input uncertainty B. Richter et al. https://doi.org/10.5194/nhess-20-2873-2020
11. Seismic location and tracking of snow avalanches and slush flows on Mt. Fuji, Japan C. Pérez-Guillén et al. https://doi.org/10.5194/esurf-7-989-2019
12. Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland C. Pérez-Guillén et al. https://doi.org/10.5194/nhess-22-2031-2022
13. Effect of snowfall on changes in relative seismic velocity measured by ambient noise correlation A. Guillemot et al. https://doi.org/10.5194/tc-15-5805-2021
14. Near-real-time automated classification of seismic signals of slope failures with continuous random forests M. Wenner et al. https://doi.org/10.5194/nhess-21-339-2021