A scenario-based approach for immediate post-earthquake rockfall impact assessment and case study

Different approaches exist to describe the seismic triggering of rockfalls. Statistical approaches rely on the analysis of local terrain properties and their empirical correlation with observed rockfalls. Conversely, deterministic, or physically based approaches, rely on the modeling of individual trajectories of boulders set in motion by seismic shaking. They require different data and allow various interpretations and applications of their results. Here, we present a new method for earthquake-triggered rockfall scenario assessment making use of ground shaking estimates. Its key inputs are the locations of likely initiation points of rockfall trajectories, namely, rockfall sources, obtained by statistical analysis of digital topography [1,2,3].

In the approach proposed here [4], ground shaking maps corresponding to a specific earthquake are used to suppress the probability of activation of sources at locations with low ground shaking, while enhancing that in areas of high shaking close to the epicenter. Rockfall trajectories are calculated from the probabilistic source map by three-dimensional kinematic modeling using the software STONE [5,6]. Here, we apply the method to the 1976 MI = 6.5 Friuli earthquake, for which an accurate inventory of seismically-triggered rockfalls exists [7].

We suggest that using peak ground acceleration as a modulating parameter to suppress/enhance rockfall source probability the model reasonably reproduces observations. Calibration of the method is peculiar of the area, but it is expected to be valid for future earthquake-induced rockfalls in the same area, for similar seismic events. The method was previously applied at different scales and with different assumptions across Italy [8], in particular at national scale using maps of maximum expected ground shaking with different return times [9].

Results of this work [4] allow for a preliminary impact evaluation before field observations become available. We suggest that the framework may be suitable for rapid rockfall impact assessment as soon as ground-shaking estimates (from empirical ShakeMap [10] or from physical models of wave’s propagation) are available after a seismic event.

References

[1] Alvioli et al., Engineering Geology (2021). https://doi.org/10.1016/j.enggeo.2021.106301

[2] Alvioli et al., Geomatics, Natural Hazards and Risk (2022). https://doi.org/10.1080/19475705.2022.2131472

[3] Pokharel et al., Bulletin of Engineering Geology and the Environment (2023). https://doi.org/10.1007/s10064-023-03174-8

[4] Alvioli et al., Landslides (2023). https://doi.org/10.1007/s10346-023-02127-2

[5] Guzzetti et al., Computers & Geosciences (2002). https://doi.org/10.1016/S0098-3004(02)00025-0

[6] Valagussa et al., Engineering Geology (2014). https://doi.org/10.1016/j.enggeo.2014.07.009

[7] Govi, Bulletin Int. Assoc. Engineering Geology (1977). https://doi.org/10.1007/BF02592650

[8] https://frasi-project.irpi.cnr.it

[9] Alvioli et al., Geomorphology (2023). https://doi.org/10.1016/j.geomorph.2023.108652

[10] Worden et al., (2020). ShakeMap 4 Manual, USGS. https://doi.org/10.5066/F7D21VPQ