Mustac, Marija
Description
One of the most important aspects of seismology is explaining the
generation of seismic waves during earthquakes. The first
mathematical models of earthquakes involved shear faulting, where
deformation of rocks surrounding the fault increases the stress
level, causes rock fracturing and results in radiation of elastic
waves. Over the years, a large number of earthquakes that cannot
be explained only with shear faulting have been observed. Hence,
the...[Show more] mathematical model of seismic sources evolved into a seismic
moment tensor (MT), which also includes isotropic and compensated
linear vector dipole components. Although uncertainties in MT
inversions are important for estimating solution robustness, they
are rarely available. Furthermore, noise in the data can alter
the waveform and cause spurious non-double-couple components.
In this thesis, I address these issues using Bayesian
hierarchical inversion, a relatively novel technique in
seismology. Its probabilistic approach gives an ensemble of
solutions instead of just one best-fit solution, thus, it can be
used to estimate MT uncertainties. The algorithm developed as a
part of this thesis uses waveform data of regional earthquakes
and explosions with moderate magnitudes to compute the centroid
location and the seismic moment tensor. The algorithm includes a
sophisticated treatment of data noise utilising an empirical
noise covariance matrix, and including the level of noise as an
unknown in the inversion. As a result, the model complexity is
determined by the data themselves.
There are two major groups of events for which the Bayesian
approach can be of great importance, and to which the algorithm
has been applied. The first one is seismic events in complex
geological environments, such as volcanic and geothermal areas. A
significant number of these events are expected to have source
processes that require the full MT. The second group is
explosions, where the algorithm can be valuable for nuclear
proliferation. The feasibility of the approach is initially
demonstrated on synthetic data contaminated with noise. It is
shown that the empirical covariance matrix improves the location
estimate. This is followed by application to a well-studied
earthquake from Long Valley caldera, a volcanic environment in
California, where a statistically significant isotropic component
of the source is confirmed.
The method was further improved to include multiple noise
parameters that determine the fit on each record, and in turn
weight the stations' contribution in the inversion. Subsequently,
I have analysed several earthquakes from a geothermal field in
California, The Geysers. The double-couple components of the
sources agree well with the regional stress field, but the
non-double-couple components show a variety of values.
Finally, the method is applied to the 2013 Democratic People's
Republic of Korea nuclear explosion. Since the paths to the
recording stations in the region traverse significantly different
crustal structures, a linear inversion was initially conducted to
create a composite structural model that better explained the
oceanic raypaths. The Bayesian inversion shows exceptionally low
uncertainties in the moment tensor solution for this event,
characterising it as a crack mechanism, which explains the
non-isotropic radiation as a result of material damage.
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