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Task 3: Irpinia Fault System WP3.1 Seismic noise analysis and Green Functions. Project – DPC S5 High-resolution multi-disciplinary monitoring of active fault test-site areas in Italy. Vassallo Maurizio, Gaetano Festa, Antonella Bobbio, Piero Brondi. 24 March 2010 –INGV Rome. - PowerPoint PPT Presentation
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Task 3: Irpinia Fault SystemWP3.1 Seismic noise analysis and
Green Functions
Project – DPC S5High-resolution multi-disciplinary
monitoring of active fault test-site areas in Italy
Vassallo Maurizio, Gaetano Festa, Antonella Bobbio, Piero Brondi
24 March 2010 –INGV Rome
Broad-band ISNet stations and Ambient seismic Noise
RSF3
RDM3
PGN3
COL3
TEO3
Ambient seismic noise acquired for 18 months at 5 stations of ISNet equipped with broad-band velocimeters (Trillium 40)
ISNet dense seismic network:• A field laboratory to study the seismic source at small scales• An advanced infrastructure to test early warning procedures
Broad-band stations:RSF3 Rocca S. Felice (AV)TEO3 Teora (AV)RDM3 Ruvo del Monte(PZ)COL3 Colliano (SA)PGN3 Pignola (PZ)
Green functions from ambient seismic noise
and ФA and ФB are the seismic fields received by two sensors in A and B
When the propagation medium satisfies the equipartition principle for a complete diffuse wavefield we can compute the Green function GAB(t) between two recording points A and B by the cross-correlation functions of the respectively seismic fields:
GAB(t) can be reitrieved by the causal part (t > 0) and anticausal part (t<0) of the cross correlation function CAB(t)
Lobkis and Weaver (2001)
Where
Data collecting and processing
Phase 2: cross-correlation and stack
Continuous seismic data acquired for 18 months at 5 broad band stations of ISNet
Remove mean; remove trend; band pass filter and cut to
length 6 hours
time domain normalization (1-bit normalization)
Spectral Normalization
(spectral whitening)
Compute cross-correlation
Compute the stack of correlations
Phase 1: single station data preparation
Cross-correlations and stacks
Common low frequency signal
Higher stability of signal during
the spring and summer
• symmetric stack• energetic signal between -30 e 30 s
• asymmetric stack• energetic signal between -70 e 70 s
Distance between stations 27 km |One year of data (2009) | Distance between stations 40 km
small variations in the processed stacks processing
not effective
Processing to increase signal quality•Spectral whitening •Recoursive Butterworth filter between 0.1 Hz and 1 Hz•Stack of traces with high S/N
Whitening Recoursive filter
Dispersion analysis [0.1Hz-1Hz]
frequency (Hz)Gro
up v
eloc
ity (m
/s)
Period (s)Gro
up v
eloc
ity (k
m/s
)
PGN-RSF
PGN-TEO
Gro
up v
eloc
ity (k
m/s
)
Period (s)
Surface wave analysis of stacks•Velocity analysis for identification of phases •Picking for dispersion curves reconstruction
Velocity models•Comparison between picked dispersion curves and dispersion curves computed using the 1D velocity models of the area
Dispersion analysis [0.1Hz-1Hz]
Dispersion computed only for far stations pairs ,for near stations pairs low S/N in stack traces problems for velocity analysis
Dispersion analysis [10s-50s]
All stacks filtered in 0.05Hz-0.08 Hz
PGN-RSF0.01 Hz0.02 Hz0.03 Hz0.04 Hz0.05 Hz0.06 Hz0.07 Hz0.08 Hz0.09 Hz0.10 Hz
Time-dispersion analysis
Surface wave propagation
Frequency
Distance Period (s)
Gro
up v
eloc
ity (k
m/s
)
RDM-RSF
Period (s)
Gro
up v
eloc
ity (
km/s
)
RDM-PGN
Velocity analysis
Preliminary
results
Dispersion analysis [10s-50s]
Preliminary
results
Clear surface waves identified on stack traces for all broad band stations
Conclusions
• Data collections and processing 100% completed • Computed cross-Correlations and Green functions for
all pairs of broad band stations 100% completed• Velocity and dispersion analysis 80-90% completed • Inversion dispersion curves for reconstruction of
velocity models 30-40% completed
WP 3.1 activities24
months18
months12
months6
months
Data collection and processing
Green functions
Dispersion analysis
Velocity models
Time Schedule
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