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Constraining landslide characteristics with Bayesian inversion of field and seismic data

  • Moretti, L Université de Paris, Paris, France
  • Mangeney, A INRIA-J. L. Lions, France
  • Walter, F Laboratory of Hydraulics, Hydrology and Glaciology VAW, ETH Zurich, Switzerland
  • Capdeville, Y Laboratoire de Planétologie et Géodynamique de Nantes, F-44300 Nantes, France
  • Bodin, T ORCID Univ Lyon, ENS de Lyon, CNRS, UMR 5276 LGL-TPE, F-69622 Villeurbanne, France
  • Stutzmann, E Université de Paris, Paris, France
  • Le Friant, A Université de Paris, Paris, France
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  • 2020-2-19
Published in:
  • Geophysical Journal International. - Oxford University Press (OUP). - 2020, vol. 221, no. 2, p. 1341-1348
Geochemistry and Petrology
Geophysics
English 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 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.
Language
  • English
Open access status
green
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https://sonar.ch/global/documents/66958
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