Statistics – Computation
Scientific paper
Jan 2005
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2005georl..3201303p&link_type=abstract
Geophysical Research Letters, Volume 32, Issue 1, CiteID L01303
Statistics
Computation
18
Exploration Geophysics: Computational Methods: Seismic, Seismology: Earthquake Ground Motions And Engineering Seismology, Seismology: Earthquake Interaction, Forecasting, And Prediction (1217, 1242), Seismology: Surface Waves And Free Oscillations
Scientific paper
Seismic noise contains information on the local S-wave velocity structure, which can be obtained from the phase velocity dispersion curve by means of array measurements. The H/V ratio from single stations also contains information on the average S-wave velocity and the total thickness of the sedimentary cover. A joint inversion of the two data sets therefore might allow constraining the final model well. We propose a scheme that does not require a starting model because of usage of a genetic algorithm. Furthermore, we tested two suitable cost functions for our data set, using a-priori and data driven weighting. The latter one was more appropriate in our case. In addition, we consider the influence of higher modes on the data sets and use a suitable forward modeling procedure. Using real data we show that the joint inversion indeed allows for better fitting the observed data than using the dispersion curve only.
Milkereit Claus
Parolai Stefano
Picozzi Matteo
Richwalski S. M.
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