Joint probabilistic multi-zonal trans-dimensional inversion on properties of near-surface layers from dispersion and ellipticity curves
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Date
2021
Publication Type
Conference Paper
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Abstract
This research focuses on the validation and application of a novel Bayesian approach to infer near-surface layered velocity models from dispersion and ellipticity curves. The inversion method relies on a varying number of layers, and it is formulated in the trans-dimensional Bayesian framework. The number of layers is treated as the model complexity governed by the law of parsimony. The model space is explored by a Markov chain Monte Carlo algorithm producing an ensemble of models drawn from the posterior probability of model parameters. The inversion method introduces also a multi-zonal formulation of the prior, allowing to include additional information to the inversion (e.g., from well logs). In this contribution, we focus on a validation of the inversion method using a synthetic test with a velocity gradient. We apply the approach to a Swiss site and show a comparison of the predicted theoretical and empirical amplification functions.
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Publication status
published
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Book title
6th IASPEI / IAEE International Symposium: Effects of Surface Geology on Seismic Motion - ESG6 Extended Abstracts
Journal / series
Volume
Pages / Article No.
Publisher
Japan Association for Earthquake Engineering
Event
6th IASPEI / IAEE International Symposium: Effects of Surface Geology on Seismic Motion (ESG6)
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Methods
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Geographic location
Date collected
Date created
Subject
inversion; Bayesian; dispersion curves; velocity profile; site characterization
Organisational unit
02818 - Schweiz. Erdbebendienst (SED) / Swiss Seismological Service (SED)