1:45 PM - 3:15 PM
[MIS04-P07] On the possible relation between aftershocks and characteristic EM variations observed after the 2016 Central Tottori earthquake
Keywords:electromagnetic field, earthquake, aftershock, pattern matching, statistical test
Among them, Type 3 phenomena are easily identified by a comparison between time series of the EM data and seismograms and their generation mechanisms are largely understood, although not complete. Type 2 phenomena are also plausible, although their generation mechanisms are less understood. In contrast, the existence of the Type 3 phenomena has been still a topic on dispute. The less reliability of Type 3 phenomena is possibly due to the small number of the cases. Many of the earlier reports on Type 3 phenomena are mainly related to large earthquakes, meaning that the number of samples are small so that the statistical significance is difficult to be confirmed. If Types 1-3 EM variations do exist, it is natural to assume that they can be observed not only for large earthquakes, but also for small earthquakes. Therefore, a promising approach to confirm the existence of Type 3 phenomena is to investigate EM variations in association with small earthquake. Because the number of small earthquakes are far larger than that of large earthquakes, we will be able to conduct statistical test. However, it is also natural to assume that the EM variations in association with small earthquakes may be very small. We should refer the time series of EM data acquired at an ideal situations, including the small distance from epicenters.
In the present study, we investigated EM dataset acquired just after the 2016 Central Tottori earthquake, which were originally recorded for the subsurface electrical conductivity structure around the focal area. The characteristic variations in the EM time series were tried to be identified and the timing of their occurrence is compared with the aftershocks to confirm the hypothesis that variations in the electromagnetic do occur in association with earthquakes.
We first focused on a small portion of the time series and visually marked each EM signal with the timing of its occurrence. The number of EM signals in several time windows defined relative to the occurrence time of earthquakes were counted to confirm whether the signals were observed at the close timing to earthquakes. Disappointingly, the number of signals close to the earthquakes was not larger than that independent from earthquakes.
However, our first analysis based on the visual inspection was only performed for a small portion of the entire time series because of it was laborious; moreover, it was less objective. To perform an exhaustive and objective counting, we next propose an automatic procedure to detect EM signals in the time series. In the proposed procedure, a template waveform was defined by stacking representative waveforms that were picked up by visual inspection. The signals in the time series were defined as the waveform with which the correlation to the template waveform exceeds a certain threshold.
We applied the proposed automatic procedure to the EM time series and confirmed that the number of signals occurred during a several time-range before and after earthquakes are larger than that expected when it occurred randomly. We also performed a statistical test for the increase in the signal occurrence rate and confirmed that the recognized increase in the signal occurrence rate was significance with the 95% reliability. These results suggest that EM variations recorded in the EM time series include earthquake-related signals, at least for the case of aftershocks of the 2016 Central Tottori earthquake.
In the present study, we investigated EM dataset acquired just after the 2016 Central Tottori earthquake, which were originally recorded for the subsurface electrical conductivity structure around the focal area. The characteristic variations in the EM time series were tried to be identified and the timing of their occurrence is compared with the aftershocks to confirm the hypothesis that variations in the electromagnetic do occur in association with earthquakes.
We first focused on a small portion of the time series and visually marked each EM signal with the timing of its occurrence. The number of EM signals in several time windows defined relative to the occurrence time of earthquakes were counted to confirm whether the signals were observed at the close timing to earthquakes. Disappointingly, the number of signals close to the earthquakes was not larger than that independent from earthquakes.
However, our first analysis based on the visual inspection was only performed for a small portion of the entire time series because of it was laborious; moreover, it was less objective. To perform an exhaustive and objective counting, we next propose an automatic procedure to detect EM signals in the time series. In the proposed procedure, a template waveform was defined by stacking representative waveforms that were picked up by visual inspection. The signals in the time series were defined as the waveform with which the correlation to the template waveform exceeds a certain threshold.
We applied the proposed automatic procedure to the EM time series and confirmed that the number of signals occurred during a several time-range before and after earthquakes are larger than that expected when it occurred randomly. We also performed a statistical test for the increase in the signal occurrence rate and confirmed that the recognized increase in the signal occurrence rate was significance with the 95% reliability. These results suggest that EM variations recorded in the EM time series include earthquake-related signals, at least for the case of aftershocks of the 2016 Central Tottori earthquake.