Constraining landslide characteristics with Bayesian inversion of field and seismic data

Laurent Moretti (1) , A. Mangeney (2, 1) , F. Walter (3) , Yann Capdeville (4) , T. Bodin (5) , E. Stutzmann (1) , A Le friant (1)

1 IPGP (UMR_7154) - Institut de Physique du Globe de Paris
2 LJLL (UMR_7598) - Laboratoire Jacques-Louis Lions
3 Laboratory of Hydraulics, Hydrology and Glaciology
4 LPG - Laboratoire de Planétologie et Géodynamique [UMR 6112]
5 LGL-TPE - Laboratoire de Géologie de Lyon - Terre, Planètes, Environnement

Using a fully nonlinear Bayesian approach based on forward modelling of granular flow, we invert for landslide parameters (volume, release geometry and rheology) from different kinds of observations. Synthetic tests show that the runout distance and the deposit area by themselves do not constrain landslide parameters. Better constraints on landslide parameters are obtained from the thickness distribution of the landslide deposits, as well as from the force history applied by the landslide to the ground, which contains information on the landslide dynamics. Therefore, inverting force histories calculated from seismic broad-band records is an important alternative to inverting thickness distributions of landslide deposits, which are usually difficult to obtain. We test the method on the 1997 Boxing Day debris avalanche on Montserrat Island, which involved 40 − 50 Mm3. The Bayesian inversion and granular flow model provide good estimates for volume, release geometry and effective friction coefficient. This study thus underlines the value of broad-band seismic records as observations to monitor landslides and validation for their numerical flow models..

Domaines: Géophysique [physics.geo-ph]

Laurent Moretti, A. Mangeney, F. Walter, Yann Capdeville, T. Bodin, et al.. Constraining landslide characteristics with Bayesian inversion of field and seismic data. Geophysical Journal International, 2020, 221 (2), pp.1341-1348. ⟨10.1093/gji/ggaa056⟩. ⟨hal-02545925⟩