Detecting Aliasing and Artifact Free Co-seismic and Tsunamigenic
Ionospheric Perturbations using GPS
Abstract
Ionospheric perturbations induced by tsunamis and earthquakes can be
used for tsunami early warning and remote sensing of earthquakes,
provided the perturbations are characterized properly to distinguish
them from the ones caused by other sources. The ionospheric
perturbations are increasingly being obtained from Global Positioning
System (GPS) based Total Electron Content (TEC) measurements sampled at
uniform time intervals. However, the sampling is not uniform in space.
The non-uniform spatial sampling along the GPS satellite tracks
introduces aliasing if it is not accounted while computing the
ionospheric perturbations. At the same time, the residual approach used
to obtain the perturbations by detrending the TEC time series using
high-order polynomial fits introduces artifacts. These aliasing and
artifacts corrupt amplitude, Signal-to-Noise Ratio (SNR), phase, and
frequency of the perturbations. We show that adopting Spatio-Periodic
Leveling Algorithm (SPLA) successfully removes such aliasing and
artifacts while detecting the perturbations using GPS. The efficiency of
SPLA in removing aliases and artifacts is validated under two
theoretically simulated scenarios, and using GPS observations carried
out during the 2004 Indian Ocean tsunami and 2015 Nepal-Gorkha
earthquake. Spatiotemporal, SNR, cross-correlation, and wavelet analysis
reveal that removal of aliasing and artifacts using SPLA i) increases
SNR up to ~149% compared to the residual method and
~39% compared to the differential method, ii)
distinctly resolves signals from sharp static variations, and iii)
detects 50% more co-seismic ionospheric perturbations and 25% more
tsunami-induced ionospheric perturbations in the two events studied.
Comparing the occurrence time of the perturbations obtained using the
residual method and SPLA reveals that aliasing and artifacts shift the
time of occurrence by -7.64 minutes to +4.21 minutes. Further, the
results show that the SPLA efficiently detects the perturbations at low
elevation angles and removes the need of applying elevation cut-off.