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| Titel |
Bayesian Inference in Linear Mixed Models for Distribution of Fault slip for the M6.0 Parkfield 2004 Event |
| VerfasserIn |
Vahid Rezanezhad, Matthias Holschneider, Li Feng Wang |
| Konferenz |
EGU General Assembly 2013
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 15 (2013) |
| Datensatznummer |
250075698
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| Zusammenfassung |
| Coseismic surface displacements are in general related with a spatial
slip distribution on a fault surface by linear integral equations.
Parametric expansion of the fault slip distribution by a finite number
of known basis functions yields a set of observation equations
expressed in a simple vector form. We use a linear mixed model
formulation together with a Bayesian approach to infer the slip
distribution together with its uncertainties from surface displacement
observations. Linear mixed models are able to handle an extraordinary
range of complications in regression-type analyses. Their most use is
to account for within subject correlation in longitudinal data
analysis. They are also the standard tool for smoothing spatial count
data. In particular, the classical geostatistical approach called
Kriging can be cast as a linear mixed model by adding random effects
to the traditional linear model that we have done this approach in
this work. The smoothness is estimated using a K-fold cross
validation. We apply the method to the M6.0 Parkfield 2004 Event. |
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