Rock falls take place from steep rocky slopes, producing accumulation of rock fragments, highly variable in size, at their foot.

Our objectives are to

  1. improve the methodologies based on Machine Learning for the automatic detection of rockfalls in a variety of lithologies to analyse their effect on magnitude-frequency relationships. To compare new rockfall datasets with databases from other international institutions to expand knowledge on the instability processes involved.
  2.  improve the monitoring of rockfalls through aerial and sub-daily terrestrial photogrammetry, LiDAR, seismic signals and GNSS-RTK to correlate with the evolution of triggering factors like precipitation, thermal oscillations, vibrations, etc.
  3. develop procedures for the automatic identification of rockfalls from seismic signals based on STA/LTA methods, to obtain robust results on quasi-real time, as the basis of early warning systems.

Study sites include Puigcercós and Castellfollit de la Roca (Pyrenees), Montserrat (Ebro Basin), and Alhambra (Betic Cordillera).


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