These results show that it is possible to monitor the behavior of pressure sources at different depths simply by using stacked data for a combination of baselines of different lengths, without any complicated analyses being necessary.
Moreover, the panel VAR also helps us address the unobserved skill group and regional specific heterogeneity while, at the same time, overcoming the data limitation problem by stacking the data for the various skill groups in each region.
In this case the space-time data snapshot is defined by stacking the data stream in (19) for all pulses as follows: (20).
We produced times series of short stacked data by various combinations of short baseline data (Fig. 4) to find the best SNR by trial and error.
To summarize, we were able to detect slight changes prior to the eruption in 2014 in the short stacked data by a quasi-real-time analysis using IGS ultra-rapid ephemeris data.
The horizontal axis shows the magnitude of the change in each stacked data normalized by the magnitude of change in the short stacked data at 0 km depth (black dots).
Uncertainties within the stacked data were evaluated by an error propagation equation by assuming that the errors in the daily impedance were independent.
Therefore, we used the IGS ultra-rapid ephemeris data analyzed by JMA to produce the short stacked data, which is most useful for detecting change in a shallow source.
SHmax orientation was determined by stacking caliper data in each zone, with uncertainties of up to 20°.
Mean SHmax azimuths were determined by stacking breakout data in zones of fairly homogeneous orientations to take lithological characteristics into account.
