Constraining landslide characteristics with Bayesian inversion of field and seismic data. Issue 2 (19th February 2020)

• Record Type: Journal Article
• Authors: Moretti, L; Mangeney, A; Walter, F; Capdeville, Y; Bodin, T; Stutzmann, E; Le Friant, A
• Is Part Of: Geophysical journal international. Volume 221:Issue 2(2020)
• Page Start: 1341
• Page End: 1348
• Publication Date: 2020-02-19
• Subjects: Friction -- Geomorphology -- Atlantic Ocean -- Numerical modelling -- Waveform inversion -- Volcano seismology; Geophysics -- Periodicals; 550
• DOI: 10.1093/gji/ggaa056
• Languages: English
• ISSNs: 0956-540X

Abstract

SUMMARY: 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 Mm 3 . 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.