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Slip distribution inversion by trans-dimensional Monte Carlo sampling: application to the 2009 L’Aquila Earthquake (Central Italy)
Author(s)
Type
Poster session
Language
English
Obiettivo Specifico
3.1. Fisica dei terremoti
Status
Unpublished
Conference Name
Issued date
December 15, 2009
Conference Location
San Francisco, USA
Abstract
Non-uniform slip distribution on a fault plane from geodetic data is usually
estimated in two steps. First, the geometric fault parameters are inferred by
non -linear inversion assuming a uniform slip on a rectangular fault. A second
analysis, based on linear inversion techniques, infers the slip distribution on
an arbitrary subdivision of the fault plane into patches. Two main concerns
arise. First, the fault geometry determined under the assumption of a uniform
slip i s not guaranteed to properly represent the fault geometry for a spatially
variable slip distribution. Moreover, an arbitrary fault subdivision into patches
u nrelated to the observed data could bias the model resolution, introducing
spurious features.
In recent years, the availability of large coverage data, such as DInSAR
images, improved mapping the coseismic displacements. The large amount of
geodetic da ta from the area surrounding earthquake faults allows for improving
the slip models and refining the knowledge of earthquake dynamics. Less
attention has been given to the development of new inversion algorithms that
can resolve the main concerns above. In particular, the question is whether
the data themselves ca n constrain the slip model complexity, i.e., the unknown
number and distribution of the fault patches needed to fit the observations. The
reversible jump Mar kov chain Monte Carlo (RJMCMC) algorithm has been recently
introduced in the geosciences to solve a variety of non linear inverse
problems. RJMCMC combines a classical Markov chain Monte Carlo method
with the ability to shift between models with a different number of unknowns.
A posterior probability distribution of the num ber of unknowns is obtained at
the end of the Markov chain, so that the model resolution is determined by the
observed data.
In this study, we apply a RJMCMC method to the Mw 6.3 L’Aquila earthquake
that occurred on April 6th 2009 in Central Italy. Three DInSAR images,
mapping the c oseismic displacement, are inverted to constrain not only the slip
distribution but also the number of unknowns (i.e., the number of fault patches)
and the ge ometry of non-rectangular patches.
estimated in two steps. First, the geometric fault parameters are inferred by
non -linear inversion assuming a uniform slip on a rectangular fault. A second
analysis, based on linear inversion techniques, infers the slip distribution on
an arbitrary subdivision of the fault plane into patches. Two main concerns
arise. First, the fault geometry determined under the assumption of a uniform
slip i s not guaranteed to properly represent the fault geometry for a spatially
variable slip distribution. Moreover, an arbitrary fault subdivision into patches
u nrelated to the observed data could bias the model resolution, introducing
spurious features.
In recent years, the availability of large coverage data, such as DInSAR
images, improved mapping the coseismic displacements. The large amount of
geodetic da ta from the area surrounding earthquake faults allows for improving
the slip models and refining the knowledge of earthquake dynamics. Less
attention has been given to the development of new inversion algorithms that
can resolve the main concerns above. In particular, the question is whether
the data themselves ca n constrain the slip model complexity, i.e., the unknown
number and distribution of the fault patches needed to fit the observations. The
reversible jump Mar kov chain Monte Carlo (RJMCMC) algorithm has been recently
introduced in the geosciences to solve a variety of non linear inverse
problems. RJMCMC combines a classical Markov chain Monte Carlo method
with the ability to shift between models with a different number of unknowns.
A posterior probability distribution of the num ber of unknowns is obtained at
the end of the Markov chain, so that the model resolution is determined by the
observed data.
In this study, we apply a RJMCMC method to the Mw 6.3 L’Aquila earthquake
that occurred on April 6th 2009 in Central Italy. Three DInSAR images,
mapping the c oseismic displacement, are inverted to constrain not only the slip
distribution but also the number of unknowns (i.e., the number of fault patches)
and the ge ometry of non-rectangular patches.
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