Fully probabilistic seismic source inversion – Part 1: Efficient parameterisation
Seismic source inversion is a non-linear problem in seismology where not just the earthquake parameters themselves but also estimates of their uncertainties are of great practical importance. Probabilistic source inversion (Bayesian inference) is very adapted to this challenge, provided that...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2014-11-01
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| Series: | Solid Earth |
| Online Access: | http://www.solid-earth.net/5/1055/2014/se-5-1055-2014.pdf |
| Summary: | Seismic source inversion is a non-linear problem in seismology where
not just the earthquake parameters themselves but also estimates of
their uncertainties are of great practical importance. Probabilistic
source inversion (Bayesian inference) is very adapted to this
challenge, provided that the parameter space can be chosen small
enough to make Bayesian sampling computationally feasible. We
propose a framework for PRobabilistic Inference of Seismic source Mechanisms
(<i>PRISM</i>) that parameterises and samples earthquake depth, moment
tensor, and source time function efficiently by using information
from previous non-Bayesian inversions. The source time function is
expressed as a weighted sum of a small number of empirical
orthogonal functions, which were derived from a catalogue
of >1000 source time functions (STFs) by a principal component analysis. We use
a likelihood model based on the cross-correlation misfit between
observed and predicted waveforms. The resulting ensemble of
solutions provides full uncertainty and covariance information for
the source parameters, and permits propagating these source
uncertainties into travel time estimates used for seismic
tomography. The computational effort is such that routine, global
estimation of earthquake mechanisms and source time functions from
teleseismic broadband waveforms is feasible. |
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| ISSN: | 1869-9510 1869-9529 |