EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Identification and characterization of rockfalls using seismic signals, LiDAR, and imagery. Advances on real-time detection

Bixen Telletxea1, Mar Tapia2, Marta Guinau1, Manuel J. Royán1, Pere Roig Lafon1, Xabier Blanch1, Giorgi Khazaradze1, Emma Suriñach1,2, Gloria Furdada1, David Garcia-Sellés1, Antonio Abellán3, and Joan Manuel Vilaplana1
Bixen Telletxea et al.
  • 1Grup RISKNAT, Institut Geomodels, University of Barcelona, Faculty of Earth Sciences, Department of Earth and Ocean Dynamics, Barcelona, Spain (;;;;; gkhaz
  • 2Laboratori d’Estudis Geofísics Eduard Fontserè, Institut d’Estudis Catalans, Barcelona, Spain (
  • 3Centre de Recherche sur l'Environnement Alpin (CREALP), Sion, CH1950, Valais, Switzerland (

Seismic sensors installed in areas prone to rockfalls provide a continuous record of the phenomenon, allowing real-time detection and characterization. Detection of small scale rockfalls (< 0.001 m3), that might be precursors of larger events, can be worthwhile for early warning systems of rockfalls. However, seismic signals are closely dependent on the characteristics of the event and on the geotechnical characteristics of the ground, making the detection of small rockfalls complex and requiring detailed in-situ analyzes. For this reason, an experiment was carried out on the UB experimental site (Puigcercós Cliff, Catalonia, NE Spain) on 6th-7th of June 2013, where 21 rocks with volumes ranging from 0.0015 m3 to 0.0004 m3 were thrown from the top of the cliff (200 m long and 27 m high) and the seismic signals were registered with three 3D short period seismic sensors located at different distances from the rock wall: 57 m, 67 m, and 107 m.

The recorded seismic signals have a frequency content between 10-30 Hz, and the duration of the peak amplitudes varied between 0.3 and 0.6 s. Based on these characteristics, different phases of the dynamics of the rockfalls were identified, including main impacts, rebounds, flights, rolling and final stop of the events. The furthest station recorded the lowest frequency and amplitude values, limiting our ability to detect those blocks smaller than 0.0015 m3. Comparing the results with the nearest station, seismic attenuation phenomena is detectable even at distances of 50 m.

After the experiment, a permanent seismic station was installed in the area, at 107 m from the cliff. Using LiDAR and 2D imagery monitoring, two naturally triggered rockfalls were identified on 30th and 31st August 2017 (0.28 m3 and 0.25 m3 respectively). Based on the results from the experiment and an automatic detection system, these main events and prior minor events have been found in the continuous seismic records of this permanent station. The characteristics of these natural detachments differ partially from the artificially triggered rockfalls during the experiment, since the geometry of the seismic signals is different. The observed shapes of the natural detachments are similar to that of granular flows, much more continuous than the sharp shapes that were observed in the isolated blocks of the experiment. This shows the possibility of incorporating seismic stations for the automatic detection and initial characterization of rockfalls and its effectiveness in detecting frequencies of occurrence.

In order to evaluate the possibility of estimating rockfall volumes, diverse energy ratios (Es/Ep) were calculated. However, precise volume estimation is not possible. Nevertheless, the combination of seismic data with LiDAR and photographic techniques allows accurate new volume calculations of rockfalls to be incorporated progressively into the study of rockfalls.

ACKNOWLEDGMENTS: The authors would like to acknowledge the financial support from CHARMA (CGL2013-40828-R) and PROMONTEC (CGL2017-84720-R AEI/FEDER, UE) projects, Spanish MINEICO. We are also thankful to Origens UNESCO Global Geopark.

How to cite: Telletxea, B., Tapia, M., Guinau, M., Royán, M. J., Roig Lafon, P., Blanch, X., Khazaradze, G., Suriñach, E., Furdada, G., Garcia-Sellés, D., Abellán, A., and Vilaplana, J. M.: Identification and characterization of rockfalls using seismic signals, LiDAR, and imagery. Advances on real-time detection, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13149,, 2021.