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An Improved Method for the Recognition of Seismic Families: Application to the Garfagnana-Lunigiana Area, Italy
Language
English
Status
Published
JCR Journal
JCR Journal
Peer review journal
Yes
Title of the book
Issue/vol(year)
5/ 95 (2005)
Publisher
Seismological Society of America
Pages (printed)
1903-1915
Issued date
October 2005
Abstract
With the aim to find a more objective way to detect seismic families,
we applied a series of successive steps to constrain the results of a waveform similarity
analysis. The evaluation of similarity was carried out on the waveforms recorded
in the period 1999–2003 by the stations operating in the Garfagnana area,
located in northern Tuscany (Italy). The algorithm is based on the cross-correlation
technique applied in a process that overcomes the limit of one order of magnitude
between events to be compared through a bridging technique. In practice, if two
couples of events (A, B) and (B, C), each exceeding the correlation threshold, share
a common quake (B), then all three events are attributed to the same family even if
the match between A and C is below a value chosen as a reference for similarity.
To avoid any subjective choice of threshold for cross-correlation values, the results
from the computation algorithm are submitted to a routine that gives increasing
reliability to them if they are confirmed by the three components of the seismogram
and if the number of families detected by each station is confirmed by more recordings.
This latter constraint is made possible by the geometry of the recording network,
with interdistances between stations of the order of 40–50 km. The process finally
leads to the recognition of 27 families detected and confirmed by, on average, 3
stations that represent 40% of the recording capabilities. Since the performances of
the recording network have been very odd in the past, especially in the early years
of operation, the reliability of the detection is much higher, as in most cases the
stations that detected the families were the only ones to be effectively recording.
The methodology proved to be more efficient than other methods applied in the
past; moreover, the results could be probably improved even more if, instead of doing
a one-run process, it would be borne as a trial-and-error approach.
we applied a series of successive steps to constrain the results of a waveform similarity
analysis. The evaluation of similarity was carried out on the waveforms recorded
in the period 1999–2003 by the stations operating in the Garfagnana area,
located in northern Tuscany (Italy). The algorithm is based on the cross-correlation
technique applied in a process that overcomes the limit of one order of magnitude
between events to be compared through a bridging technique. In practice, if two
couples of events (A, B) and (B, C), each exceeding the correlation threshold, share
a common quake (B), then all three events are attributed to the same family even if
the match between A and C is below a value chosen as a reference for similarity.
To avoid any subjective choice of threshold for cross-correlation values, the results
from the computation algorithm are submitted to a routine that gives increasing
reliability to them if they are confirmed by the three components of the seismogram
and if the number of families detected by each station is confirmed by more recordings.
This latter constraint is made possible by the geometry of the recording network,
with interdistances between stations of the order of 40–50 km. The process finally
leads to the recognition of 27 families detected and confirmed by, on average, 3
stations that represent 40% of the recording capabilities. Since the performances of
the recording network have been very odd in the past, especially in the early years
of operation, the reliability of the detection is much higher, as in most cases the
stations that detected the families were the only ones to be effectively recording.
The methodology proved to be more efficient than other methods applied in the
past; moreover, the results could be probably improved even more if, instead of doing
a one-run process, it would be borne as a trial-and-error approach.
References
Aki, K. (1969). Analysis of the seismic coda of local earthquakes as scattered
waves, J. Geophys. Res. 74, no. 2, 615–631.
Aster, R. C., and J. Scott (1993). Comprehensive characterization of waveform
similarity in microearthquake data sets, Bull. Seism. Soc. Am.
83, 1307–1314.
Camassi, R., and M. Stucchi (1996). NT4.1, un catalogo parametrico di
terremoti di area italiana al di sopra della soglia del danno (a parametric
catalogue of damaging earthquakes in the Italian area). CNRGNDT,
Zincotecnica Nuova, 66 pp.
Cattaneo, M., P. Augliera, D. Spallarossa, and C. Eva (1997). Reconstruction
of a seismogenetic structures by multiples analysis: an example
of Western Liguria, Italy, Bull. Seism. Soc. Am. 87, 971–986.
Cattaneo, M., P. Augliera, D. Spallarossa, and V. Lanza (1999). A waveform
similarity approach to investigate seismicity patterns, Nat. Haz.
19, 123–138.
Deichmann, N., and M. Garcia-Fernandez (1992). Rupture geometry from
high-precision relative hypocenter location of microearthquake clusters,
Geophys. J. Int. 110, 501–517.
Ferretti, G., S. Solarino, and E. Eva (2002). Crustal structure of the Lunigiana-
Garfagnana area (Tuscany, Italy): seismicity, fault-plane solutions
and seismic tomography, Bollettino di Geofisica Teorica ed Applicata
43, no. 3–4, 221–238.
Got, J., J. Fre´chet, and F. W. Klein (1994). Deep fault plane geometry
inferred from multiplet relative relocation beneath the south flank of
Kilauea, J. Geophys. Res. 99, 15,375–15,386.
Joswig, M. (1995). Automated classification of local earthquake data in the
BUG small array, Geophys. J. Int. 120, 262–286.
Maurer, H., and N. Deichmann (1995). Microearthquake cluster detection
based on waveform similarities, with an application to the western
Swiss Alps, Geophys. J. Int. 123, 588–600.
Press, W. H., P. B. Flannery, S. A. Teukolsky, and W. T. Wetterling (1988).
Numerical recipes in C, in The Art of Scientific Computing, Cambridge
University Press, Cambridge, U.K.
Rautian, T. G., and V. I. Khalturin (1978). The use of the coda for determination
of the earthquake source spectrum, Bull. Seism. Soc. Am.
68, 923–948.
Schulte-Theis, H. (1995). Cluster analysis of European seismicity, Cahiers
Centre Europ. Geodyn. Seism. 12, 201–224.
Schulte-Theis, H., and M. Joswig (1993). Clustering and location of mining
induced seismicity in the Ruhr basin by automated master event comparison
based on Dynamic Waveform Matching (DWM), Comp.
Geosci. 19, 233–242.
Sherbaum, F., and J. Wendler (1986). Cross spectral analysis of Swabian
Jura (SW Germany) three component microearthquake recordings, J.
Geophys. 60, 157–166.
Smalley, R. F., J. L. Chatelain, D. L. Turcotte, and R. Pre´vot (1987). A
fractal approach to the clustering of the earthquakes: applications to
the seismicity of the New Hebrides, Bull. Seism. Soc. Am. 77, 1368–
1381.
Solarino, S., G. Ferretti, and C. Eva (2002). Seismicity of Garfagnana-
Lunigiana (Tuscany, Italy) as recorded by a network of semi-broadband
instruments, J. Seism. 6, 145–152.
Solarino, S. (2002). The September 7th, 1920 earthquake in Lunigiana-
Garfagnana (Tuscany, Italy): can instrumental data provide a reliable
location? in Proceedings of the XXVIII General Assembly of ESC,
Genova, Italy, 1–6 September 2002, CD-Rom.
Spottiswoode, S. M., and A. M. Milev (1998). The use of waveform similarity
to define planes of mining-induced seismic events, Tectonophysics
289, 51–60.
Waldhauser, F., and W. Ellsworth (2000). A double-difference earthquake
location algorithm: method and application to the northern Hayward
fault, California, Bull. Seism. Soc. Am. 90, no. 6, 1353–1368.
Zhizhin, M. N., A. D. Gvishiani, S. Bottard, B. Mohammadiour, and J.
Bonnin (1992). Classification of strong motion waveform from different
geological regions using syntactic pattern recognition scheme,
in Cahiers Centre Europ. Ge´odyn. Seism. 6, 33–42.
Zhizhin, M. N., A. D. Gvishiani, D. Rouland, J. Bonnin, and B. Mohammadioun
(1994). Identification of geological region for earthquakes
using syntactic pattern recognition of seismograms, Nat. Haz. 10,
139–147.
waves, J. Geophys. Res. 74, no. 2, 615–631.
Aster, R. C., and J. Scott (1993). Comprehensive characterization of waveform
similarity in microearthquake data sets, Bull. Seism. Soc. Am.
83, 1307–1314.
Camassi, R., and M. Stucchi (1996). NT4.1, un catalogo parametrico di
terremoti di area italiana al di sopra della soglia del danno (a parametric
catalogue of damaging earthquakes in the Italian area). CNRGNDT,
Zincotecnica Nuova, 66 pp.
Cattaneo, M., P. Augliera, D. Spallarossa, and C. Eva (1997). Reconstruction
of a seismogenetic structures by multiples analysis: an example
of Western Liguria, Italy, Bull. Seism. Soc. Am. 87, 971–986.
Cattaneo, M., P. Augliera, D. Spallarossa, and V. Lanza (1999). A waveform
similarity approach to investigate seismicity patterns, Nat. Haz.
19, 123–138.
Deichmann, N., and M. Garcia-Fernandez (1992). Rupture geometry from
high-precision relative hypocenter location of microearthquake clusters,
Geophys. J. Int. 110, 501–517.
Ferretti, G., S. Solarino, and E. Eva (2002). Crustal structure of the Lunigiana-
Garfagnana area (Tuscany, Italy): seismicity, fault-plane solutions
and seismic tomography, Bollettino di Geofisica Teorica ed Applicata
43, no. 3–4, 221–238.
Got, J., J. Fre´chet, and F. W. Klein (1994). Deep fault plane geometry
inferred from multiplet relative relocation beneath the south flank of
Kilauea, J. Geophys. Res. 99, 15,375–15,386.
Joswig, M. (1995). Automated classification of local earthquake data in the
BUG small array, Geophys. J. Int. 120, 262–286.
Maurer, H., and N. Deichmann (1995). Microearthquake cluster detection
based on waveform similarities, with an application to the western
Swiss Alps, Geophys. J. Int. 123, 588–600.
Press, W. H., P. B. Flannery, S. A. Teukolsky, and W. T. Wetterling (1988).
Numerical recipes in C, in The Art of Scientific Computing, Cambridge
University Press, Cambridge, U.K.
Rautian, T. G., and V. I. Khalturin (1978). The use of the coda for determination
of the earthquake source spectrum, Bull. Seism. Soc. Am.
68, 923–948.
Schulte-Theis, H. (1995). Cluster analysis of European seismicity, Cahiers
Centre Europ. Geodyn. Seism. 12, 201–224.
Schulte-Theis, H., and M. Joswig (1993). Clustering and location of mining
induced seismicity in the Ruhr basin by automated master event comparison
based on Dynamic Waveform Matching (DWM), Comp.
Geosci. 19, 233–242.
Sherbaum, F., and J. Wendler (1986). Cross spectral analysis of Swabian
Jura (SW Germany) three component microearthquake recordings, J.
Geophys. 60, 157–166.
Smalley, R. F., J. L. Chatelain, D. L. Turcotte, and R. Pre´vot (1987). A
fractal approach to the clustering of the earthquakes: applications to
the seismicity of the New Hebrides, Bull. Seism. Soc. Am. 77, 1368–
1381.
Solarino, S., G. Ferretti, and C. Eva (2002). Seismicity of Garfagnana-
Lunigiana (Tuscany, Italy) as recorded by a network of semi-broadband
instruments, J. Seism. 6, 145–152.
Solarino, S. (2002). The September 7th, 1920 earthquake in Lunigiana-
Garfagnana (Tuscany, Italy): can instrumental data provide a reliable
location? in Proceedings of the XXVIII General Assembly of ESC,
Genova, Italy, 1–6 September 2002, CD-Rom.
Spottiswoode, S. M., and A. M. Milev (1998). The use of waveform similarity
to define planes of mining-induced seismic events, Tectonophysics
289, 51–60.
Waldhauser, F., and W. Ellsworth (2000). A double-difference earthquake
location algorithm: method and application to the northern Hayward
fault, California, Bull. Seism. Soc. Am. 90, no. 6, 1353–1368.
Zhizhin, M. N., A. D. Gvishiani, S. Bottard, B. Mohammadiour, and J.
Bonnin (1992). Classification of strong motion waveform from different
geological regions using syntactic pattern recognition scheme,
in Cahiers Centre Europ. Ge´odyn. Seism. 6, 33–42.
Zhizhin, M. N., A. D. Gvishiani, D. Rouland, J. Bonnin, and B. Mohammadioun
(1994). Identification of geological region for earthquakes
using syntactic pattern recognition of seismograms, Nat. Haz. 10,
139–147.
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