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| Titel |
Computation of synthetic seismograms in a 3 dimensional Earth and inversion of eigenfrequency and Q quality factor datasets of normal modes |
| VerfasserIn |
Julien Roch, Eric Clévédé, Genevieve Roult |
| Konferenz |
EGU General Assembly 2010
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 12 (2010) |
| Datensatznummer |
250042027
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| Zusammenfassung |
| The 26 December 2004 Sumatra-Andaman event is the third biggest earthquake
that has never been recorded but the first recorded with high quality
broad-band seismometers. Such an earthquake offered a good opportunity for
studying the normal modes of the Earth and particularly the gravest ones
(frequency lower than 1 mHz) which provide important information on deep
Earth. The splitting of some modes has been carefully analyzed. The
eigenfrequencies and the Q quality factors of particular singlets have
been retrieved with an unprecedented precision. In some cases, the
eigenfrequencies of some singlets exhibit a clear shift when compared to
the theoretical eigenfrequencies. Some core modes such as the 3S2 mode
present an anomalous splitting, that is to say, a splitting width much
larger than the expected one. Such anomalous splitting is presently
admitted to be due to the existence of lateral heterogeneities in the
inner core. We need an accurate model of the whole Earth and a method to
compute synthetic seismograms in order to compare synthetic and observed
data and to explain the behavior of such modes. Synthetic seismograms are
computed by normal modes summation using a perturbative method developed
up to second order in amplitude and up to third order in frequency (HOPT
method). The last step consists in inverting both eigenfrequency and Q
quality factor datasets in order to better constrain the deep Earth
structure and especially the inner core. In order to find models of
acceptable data fit in a multidimensional parameter space, we use the
neighborhood algorithm method which is a derivative-free search method. It
is particularly well adapted in our case (non linear problem) and is easy
to tune with only 2 parameters. Our purpose is to find an ensemble of
models that fit the data rather than a unique model. |
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