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We find significant positive trends in the marginal distribution parameters as well as in the dependence measure from analyzing 50-year moving time windows.
In line with previous works (see for example [2]), we here pursue the latter approach and employ standard non-parametric tests to moving time windows.
The Z-mapping approach measures the difference in seismicity rate, within moving time windows, to the background rate by a standard deviation, Z (Wiemer and Wyss 1994; Wyss et al. 2004; Katsumata 2015).
Therefore, the proposed method allows us to perform analysis at least once each 30 days for approximately 80% of the observing period and once each 60 days for 95%. Figure 6 Frequency distribution of the number of analyzed teleseismic events within moving time windows.
But moving time windows provide only partial insights into the time course of processing.
If the latter, the moving time windows approach provides only a first guide to the time course of the response, and certainly fails to take full advantage of the potential of MEG for millisecond timing of events.
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The analysis was performed in time domain using moving time window technique.
A polarization analysis of waveform within a moving time window of 0.5 s during a lapse time was performed.
We then calculate the mean amplitude every 0.1 s within a moving time window of 0.2 s.
We apply a moving time window over the observed spectral radius (TE or ATE±) time series to derive the unknown coefficients and constants for natural logarithm curves.
We calculated both the amplitude ratio and the phase lag, with a moving time window of 15 days and a running step of 3 days (Fig. 6).
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