Bayesian Self-Adapting Fault Slip Inversion With Green's Functions Uncertainty and Application on the 2016 M(w)7.1 Kumamoto Earthquake
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10421011" target="_blank" >RIV/00216208:11320/20:10421011 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=egPX8xSK9Q" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=egPX8xSK9Q</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1029/2019JB018703" target="_blank" >10.1029/2019JB018703</a>
Alternative languages
Result language
angličtina
Original language name
Bayesian Self-Adapting Fault Slip Inversion With Green's Functions Uncertainty and Application on the 2016 M(w)7.1 Kumamoto Earthquake
Original language description
Kinematic finite-extent models of earthquake sources can be determined by inverse modeling of observed waveforms and/or geodetic data. Such models are subject to significant uncertainty as a result of inaccurate observations and imperfect physical description of the complex properties of the Earth's crust. For slip inversions of large earthquakes, the major source of uncertainty is related to the uncertainty of Green's functions due to the imperfect description of the crustal model and selected parameterization of the source model. To account for both, we introduce an effective nonlinear Bayesian slip inversion with transdimensional source parameterization, including analytical representation of uncertainties of Green's functions. Our nonlinear slip inversion method relies on a self-adapting spatial parametrization of the slip distribution by means of a varying number of spline control points on the assumed fault. For the temporal parameterization, it utilizes the regularized Yoffe function with spatially varying rise time and rupture velocity. Rake angle is also treated as an unknown spatially dependent parameter. The Green's function uncertainties are included using full covariance matrices. The posterior probability density function is sampled by the transdimensional Markov chain Monte Carlo algorithm with parallel tempering. The performance of our slip inversion method is demonstrated on a synthetic test from the Source Inversion Validation project and real-data inversion of the 2016 M(w)7.1 Kumamoto earthquake. In the latter test, we infer an ensemble of similar to 7,300,000 possible rupture models, representing samples of the posterior probability density, and inspect which features of these models are reliable and which are rather artifacts.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10500 - Earth and related environmental sciences
Result continuities
Project
<a href="/en/project/GC18-06716J" target="_blank" >GC18-06716J: BAIES - Bayesian Inference of Earthquake Source parameters: kinematic and dynamic finite fault models</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Geophysical Research: Solid Earth
ISSN
2169-9313
e-ISSN
—
Volume of the periodical
125
Issue of the periodical within the volume
3
Country of publishing house
US - UNITED STATES
Number of pages
32
Pages from-to
e2019JB018703
UT code for WoS article
000530895800032
EID of the result in the Scopus database
2-s2.0-85082322425