8. Summary and Conclusion
Ionospheric perturbations induced by tsunamis and earthquakes are
obtained from GPS-TEC sampled at uniform time intervals along the
satellite tracks. However, such samplings will be non-uniform in space.
Not accounting such non-uniform spatial sampling while computing the
ionospheric perturbations introduces signal aliasing, predominantly in
the amplitude. All the methods hitherto used to detect the co-seismic
and tsunamigenic ionospheric perturbations did not account the
non-uniform spatial sampling while computing the perturbations. Further,
the residual method introduces artifacts as selection of the order of
polynomials in this method is subjective. Recently, Shimna and Vijayan
(2020) proposed an algorithm called Spatio-Periodic Leveling Algorithm
(SPLA) to remove such aliasing from ionospheric irregularities induced
by geomagnetic storms.
In this study, we showed that adopting SPLA to compute tsunami and
earthquake induced ionospheric perturbations are efficient in removing
aliasing and artifacts. Further, we showed by carrying out efficiency
tests under two simulated scenarios and using GPS observations carried
out during the 26th December 2004 Indian Ocean tsunami, and 25th April
2015 Nepal-Gorkha earthquake that SPLA can resolve the perturbations
from sharp static variations. Observational validation (Fig. 13) show
that the perturbations obtained using SPLA are within the expected
values, whereas, dTEC (differential method) and rTEC (residual method)
show clear deviation. The maximum deviation (δr ) of rTEC and dTEC
in the observational data set are 1.08 and 0.69 (Fig. 13). The
uncorrected inter-IPP distances cause the magnitude of aliasing up to 2
times (Fig. 13). This emphasizes the importance of correcting the
influence of inter-IPP distance while computing the ionospheric
perturbations.
Uncorrected aliasing and artifacts severely impact the characteristics
of the ionospheric perturbations. An assessment of the impact of
aliasing and artifacts showed that SNR of the aliasing and artifact free
ionospheric perturbations computed using SPLA is ~39%
and ~149% higher compared to the perturbations obtained
using differential and residual methods (Fig. 18). Wavelet and
cross-correlation analyses carried out on TIPs and CIPs reveal that the
time of occurrence and frequency of the perturbations differ
significantly between SPLA and residual method (Fig. 14 and Fig. 16).
Besides, the residual method fails to detect 25% of TIPs and 50% of
CIPs which were detected by both differential method and SPLA (Tables 1
and Table 2). Further explorations showed that misfits of uniform high
order polynomial representing the trend of GPS-TEC caused the failure in
the detection of the perturbations in the case of residual method (Fig.
12 and 17). Above all, the results shown in section 6.4 reveals that
SPLA is a very good candidate to obtain ionospheric perturbations at low
elevation angles and employing SPLA will increase the area of
ionospheric exploration by a GPS receiver.
Overall, this study shows that residual method performs poorly compare
to other methods in resolving sharp static variations from signals and
misses to detect ionospheric perturbations. Hence, caution needs to be
exercised while adopting residual methods in real-time detection for
earthquake or tsunami early warning, particularly, the one like VARION
– Variometric Approach for Real-Time Ionosphere Observation (Savastano
et al., 2017) which uses both differential and residual approach to
obtain TIPs in real-time.
Despite the advantages, the perturbations obtained using SPLA bound to
vary with the selection of ionospheric shell height
(hmax). Hence, a careful selection of appropriate
ionospheric shell height specific to the region and time of the
ionospheric monitoring is essential while adopting SPLA to obtain
ionospheric perturbations using GPS or any other Global Navigational
Satellite Systems.