Introduction
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Figure 1
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[2] Observations with the small aperture RUKSA array (Figure 1)
were carried out in 1999 in the Petrozavodsk region (Karelia,
Russia) within the framework of the SVEKALAPKO international
experiment [Bock, 2001]. Small aperture seismic
arrays (sometimes referred to as seismic antennas) have been
widely used since the 1980s as an effective tool for the location
of seismic events of various origins, primarily, for the
monitoring of the Non-Proliferation Treaty. Data of seismic
antennas are also effectively used for the study of seismic noise
properties and seismic wave scattering, as well as for the
recording and identification of local seismic events. One of the
immediate tasks involved in the analysis of array records is the
determination of the structure of the medium in the area of the
temporary array site and, in particular, the construction of a 1-D
velocity model of the crust and the upper mantle. The method of
the receiver function [Langston, 1979; Vinnik, 1977; Vinnik and Kosarev,
1981] developed for the analysis of three-component
seismic records is effective for the detection of main interfaces
or zones of higher velocity gradients but fails to reliably
determine absolute values of velocities in model layers. The
inverse problem of the velocity structure determination from a
known receiver function can be solved by invoking additional
information on the parameters to be determined [Ammon,
1990; Kosarev et al., 1987]. To
reduce the ambiguity of the inversion, Julia et al.
[2000] performed joint inversion of the receiver
function and group velocities of Rayleigh waves in the Arabia
region. Using joint inversion of
P and
S wave receiver
functions, Vinnik et al. [2004] constructed a
3-D model of the crust and upper mantle (down to 150 km) under the
Tien Shan. Residuals of
P and
S traveltimes were included in
the inversion in (L. P. Vinnik et al., in press, 2006). In the
present paper,
Ps traveltimes from the 410- and 660-km
boundaries and Rayleigh phase velocities are used as additional
optimization constraints.
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