Computation of synthetic seismograms for a large number earthquake scenarios


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The main test would be to wait until a significant number of earthquakes have been recorded, also of medium to large energy, and to verify the number of alarms that have correctly been sent, along with the number of false alarms and alarms missed.

  • The main test would be to wait until a significant number of earthquakes have been recorded, also of medium to large energy, and to verify the number of alarms that have correctly been sent, along with the number of false alarms and alarms missed.

  • To verify the significance of each alarm, including the useful time before the arrival of the destructive seismic wave (lead time), and the predicted amplitude at a site with respect to that which is actually recorded.

  • (For instance, the EEWS operating in Japan by JMA was tested for 29 months, starting in February 2004. During this period, the JMA sent out 855 earthquake early warnings, with only 26 recognized as false alarms due technical problems or human error)













Computation of synthetic seismograms for a large number earthquake scenarios

  • Computation of synthetic seismograms for a large number earthquake scenarios

  • We introduce two main parameters:

  • Prediction error definition

  • PE = Log10(PGVobs/PGVpred)

  • Where PGVobs are measured on synthetics and PGVpred are predicted by the early warning procedure (PE is computed as a function of time for the whole number of simulated eqk scenarios)

  • Effective Lead Time

  • Time interval between the S-arrival at the target and the time at which the prediction error distribution is stable (no significant variation of magnitude, location after this time).



Computation of synthetic seismograms for a large number of M6 and M7 earthquake scenarios

  • Computation of synthetic seismograms for a large number of M6 and M7 earthquake scenarios

  • Off-line, but sequentially application of the EW chain of methodologies to investigate the areal distribution of lead-time and prediction error on PGV



Hybrid source model based on k-square slip distribution (Gallovic and Brokesova, 2008)

  • Hybrid source model based on k-square slip distribution (Gallovic and Brokesova, 2008)

  • Complete wavefield Green function in a1-D velocity model

  • Waveforms have been noise contaminated and convolved by the site transfer function to account for site effects













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