Аннотация
The increasing number of seismic networks with high density of stations
offers an ever larger amount of three-component recordings of earthquakes
in a wide range of magnitudes. The analysis of these data can provide
detailed information on both the propagation medium and the seismic
source. In particular, the S-wave velocity is a key parameter for
the understanding of the compositional and physical state of the
lithosphere. On the other hand this requires a tool for identifying
the seismic phase. The S-phase can be identified by a change in amplitude
and frequency content of the signal with respect to the P-phase.
The precise identification of S-phase is generally made difficult
by the interference of P-coda waves, the arrival of converted phases
generated beneath the recording site or the S-wave splitting. These
factors can lead the operator to misidentify the phase or, very often,
to abandon reading itself. In this study, we propose a data processing
technique aimed at univocally identifying the arrival-time of the
S-phase by using three component recordings available at all stations
of a seismic network. The proposed technique provides an additional
support to the operators to be used for both the analysis of a single
event or for the massive, quasi-automatic analysis of huge datasets.
The technique is based on the combination of a polarization detector
mainly used in passive seismology and the move-out and stack analysis
of trace gathers as for the velocity analysis in exploration seismics.
The processing consists of four main steps. The first consists in
P-phase picking and event location. The second step is the setting-up
polarization detector: we rotate the three-component seismograms
into the ray-coordinate system (L,Q,T), using theoretical backazimuths
and incidence angles from P-phase polarizations. In the new system
we calculate the directivity D, which is defined as the normalized
angle between the P-phase polarization L and the actual polarization
direction, the rectilinearity R and the ratio between transverse
and total energy H. The product of the squares of the three filter
operators yields the characteristic function (CF) for S-wave detection,
with which we weight the transverse component traces. The third step
deals with the seismic section analyses. Once the CF has been defined,
the waveforms are displayed in common receiver gathers as a function
of hypocentral distance. On each section we evaluate the lateral
coherence of S-phase through a linear velocity analysis. The resulting
S-wave velocity can be used to compute a reference pick for the S-phase
at each station. In the fourth step an automatic picker is used around
the reference value. In the present study, we apply our S-wave detection-picking
approach to a dataset of 5675 three component, ground velocity recordings
of 626 local earthquakes with magnitude ML (0.1, 3.2), which occurred
in southern Italy and were recorded by the Irpinia Seismic Network
in the period December 2007 to March 2010. To assess the performance
of the proposed methodology, we compare the residuals of the automatic
and the theoretical arrivals with the residuals between the manual
readings and the theoretical arrivals. The dispersion of the residual
distributions obtained from the refined picking is consistent with
the dispersion obtained from the manual picks, while the total number
of available readings is increased.
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