A properly organized seismic network is a valuable tool formonitoring seismic zones and seismic risk assessment. This paper proposes a method for evaluating the performance in hypocenter location of a seismic network by numerical simulation (SNES, SeismicNetwork Evaluation through Simulation). The SNESmethod needs as input: the location of the stations that make the network and their noisiness, the velocity model used in the location routine and a law that allows to estimate the variance in the time residuals. This method gives, as function of magnitude, hypocentral depth and confidence level, the spatial distribution of: number of active stations in the location procedure and relative azimuthal gaps and confidence intervals in hypocentral parameters regarding both the geometry of the seismic network and the use of an inadequate velocity model. In recent years, the RSNC-INGV (Rete Sismica Nazionale Centralizzata dell’Istituto Nazionale di Geofisica e Vulcanologia, Italy) has been greatly increased. In fact, since 2009 it receive signals from 305 seismic stations with 258 wideband threecomponent sensors. Following the description of the SNES method and of the algorithms used, the results of its application to RSNC-INGV will be given. This application has allowed to indentify the noisiness of the RSNC-INGV and to evaluate the goodness of the velocity model used it the location routine. Furthermore, the SNES method has allowed to quantify the location performance of the RSNCINGV and to identify some seismogenic areas on the national territory that are currently not enough covered. The upgrading of the network in these areas could be optimized using the SNES technique.

D’Alessandro, A., Luzio, D., D’Anna, G., Mangano, G. (2010). Valutazione della performance di localizzazione della RSNC-INGV tramite simulazione numerica. QUADERNI DI GEOFISICA, 2010-06-24(83), 1-33.

Valutazione della performance di localizzazione della RSNC-INGV tramite simulazione numerica

D'ALESSANDRO, Antonino;LUZIO, Dario;
2010-01-01

Abstract

A properly organized seismic network is a valuable tool formonitoring seismic zones and seismic risk assessment. This paper proposes a method for evaluating the performance in hypocenter location of a seismic network by numerical simulation (SNES, SeismicNetwork Evaluation through Simulation). The SNESmethod needs as input: the location of the stations that make the network and their noisiness, the velocity model used in the location routine and a law that allows to estimate the variance in the time residuals. This method gives, as function of magnitude, hypocentral depth and confidence level, the spatial distribution of: number of active stations in the location procedure and relative azimuthal gaps and confidence intervals in hypocentral parameters regarding both the geometry of the seismic network and the use of an inadequate velocity model. In recent years, the RSNC-INGV (Rete Sismica Nazionale Centralizzata dell’Istituto Nazionale di Geofisica e Vulcanologia, Italy) has been greatly increased. In fact, since 2009 it receive signals from 305 seismic stations with 258 wideband threecomponent sensors. Following the description of the SNES method and of the algorithms used, the results of its application to RSNC-INGV will be given. This application has allowed to indentify the noisiness of the RSNC-INGV and to evaluate the goodness of the velocity model used it the location routine. Furthermore, the SNES method has allowed to quantify the location performance of the RSNCINGV and to identify some seismogenic areas on the national territory that are currently not enough covered. The upgrading of the network in these areas could be optimized using the SNES technique.
2010
Settore GEO/11 - Geofisica Applicata
D’Alessandro, A., Luzio, D., D’Anna, G., Mangano, G. (2010). Valutazione della performance di localizzazione della RSNC-INGV tramite simulazione numerica. QUADERNI DI GEOFISICA, 2010-06-24(83), 1-33.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/50462
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