3. ETAS Prior application one: Rapid Earthquake Discrimination
3.8 Examples
The application of the algorithm is demonstrated for four events, including ambient noise false triggers on 24 March 2015, a M3.7 Chino Hills aftershock on 29 July 2009, a M4.7
Los Angeles mainshock on 18 May 2008, and a M7.8 Japan teleseismic earthquake on 29 May 2015. All four events were excluded from the training data set, so the performance of these examples reflects the true behavior of the prediction of new data in the future. For each event, the analyses of the two near-field stations are presented.
24 March 2015 – ambient noise false triggers in Southern California
False picks are triggering the seismic networks hundreds to thousands times daily, especially in the urban areas. As an example, two false triggers at CI.CFS and CI.NEN stations occurred near Lancaster and San Bernardino on 24 March 2015, respectively.
Although a sudden increase in acceleration has been observed at both stations, the PGA ground motions are less than 2cm/s2, as shown in Figure 3.19. The benefits of the Bayesian approach are clearly demonstrated through these examples. If waveform analysis is the only model used for predictions, the large triggering amplitudes indicating high earthquake probability (over 60% being earthquake in both cases) could lead to incorrect alerts delivered. Although the conventional EEW method might correctly predict that the trigger is due to a non-earthquake source (less than 2% being earthquake in both cases), the decision is made after a fixed time window of 3.0 s after triggering. Alternatively, the posterior probability from the proposed method of Bayesian analysis makes the decision that the signal is non-earthquake immediately at 0.5 s after trigger, which optimize both the leading time for alerts and accuracy in the prediction. This is because 1) the forecasted earthquake probability in the prior information is low since no seismic activities were observed in the recent past and 2) the relatively high amplitude of ground shaking diminishes after less than a second which dissimilar to the patterns and characteristics of an ongoing earthquake. The two false triggers could be caused by busy traffic, collisions, or trains passing by.
Figure 3.19 initial 3.0 sec vertical acceleration waveform and prediction results for stations CI.CFS and CI.NEN during ambient noise false triggers on 24 March 2015
29 July 2008 - M3.7 Chino Hills aftershock
A M3.7 Chino Hills aftershock occurred on 29 July 2008, about 11 hours following the M5.5 mainshock. Immediately after the Chino Hills mainshock, the ETAS prior probability calculated at the stations located near the mainshock increased significantly due to the high-expected seismic activity during the aftershock sequence. Figure 3.20 indicates the location of the hypocenter and triggered stations. As shown in Figure 3.21, although the
amplitudes of P-wave reduced after the first trigger resulting in lower probability in the likelihood prediction, the posterior probability remains high with the assistance of the high prior seismicity probability. The PGA is recorded in the later phase after the arrival of S- wave.
Figure 3.20 Map of M3.7 Chino Hill Aftershock: hypocenter in the yellow star and locations of CI.OLI and CI.LBW1 in red triangles
Figure 3.21 initial 3.0 sec vertical acceleration waveform and prediction results for stations CI.OLI and CI.LBW1 during M3.7 Chino Hill aftershock on 27 July 2008
18 May 2009 - M4.7 Los Angeles earthquake
The M4.7 Los Angeles earthquake occurred on 18 May 2009. No recent seismicity was observed in the region prior to the event, so the prior probability of this event shows low probability of earthquake occurrence. Figure 3.22 indicates the location of the hypocenter and triggered stations. In Figure 3.23, the waveforms collected at the stations closest to the hypocenter, CI.WNS and CI.MIS, show strong on-going earthquake characteristics.
Although the prior probability is low, the convincing indication of earthquake event from the likelihood probability dominates the posterior probability. This example demonstrates that if sufficient evidence from the arrival data shows earthquake characteristics, the effect of prior probability becomes negligible.
Figure 3.22 Map of M 4.7 Los Angeles earthquake: hypocenter in the yellow star and locations of CI.WNS and CI.MIS in red
triangles
Figure 3.23 initial 3.0 sec vertical acceleration waveform and prediction results for stations CI.WNS and CI.MIS during M4.7 Los Angeles earthquake on 18 May 2009.
30 May 2015 - M7.8 Japan teleseismic earthquake
Teleseismic events are large earthquakes that occur at far distances, but are still observed by the local network. It is expected that the sensors will be sensitive (and triggered) by these signals, but the shaking does not affect the local region, and thus they should be classified as not EEW-relevant earthquake signals. The high frequency feature inputs in the proposed method aim to successfully discriminate teleseismic events from local earthquakes. As shown in Figure 3.24, CI.SMR and CI.SMW stations were triggered by the teleseismic event, but the amplitudes in acceleration records are small enough that they do not disturb the local community. The initial likelihood probability is relatively high due to the high uncertainty in the short data collected. Since no local seismicity was observed in the recent past, the ETAS prior probability was low, resulting in low posterior probability as well. The triggers from this teleseismic event would be ignored under the prediction of the proposed model.
Figure 3.24 Initial 3.0 sec vertical acceleration waveform and prediction results for stations CI.SMR and CI.SMW during M7.8 Japan Teleseismic earthquake on 30 May 2015.