Rock falls take place from steep rocky slopes, producing accumulation of rock fragments, highly variable in size, at their foot.
Our objectives are to
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.
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.
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).
Our research regarding 4D rockfall monitoring from photogrammetry data is being developed in close collaboration with X.Blanch and A.Eltner (Technische. Universität Dresden, Germany), A.Abellan (CREALP, Switzerland) and J.M.Azañon (Universidad de Granada), with whom we participated in two transference projects auspiced by the Patronato de la Alhambra y Generalife in Granada. Methodologies for the automatic identification of rockfalls from LiDAR or photogrammetric point clouds comparison is being carried out in collaboration with A.Puig and M.Salamó from the Departament de Matemàtica i Informàtica (UB), and from WAI Research Group, IMUB and UBICS Institutes, of the UB, with L.Blanco from Anufra—Soil and Water Consulting (Spain), and with M.Janeras and O.Pedraza from ICGC. On the other hand, research on rockfall monitoring from seismic signals is being developed in collaboration with M.Tapia from the Laboratori d’Estudis Geofísics Eduard Fontserè-LEGEF (Institut d’Estudis Catalans-IEA).