Rapid Characterization of Tsunami Sources with GNSS-TEC Ionospheric Monitoring

Large earthquakes strongly shake the upper atmosphere, leaving distinctive signatures in total electron content (TEC) measured using GNSS trans-ionospheric monitoring. The ionosphere is particularly sensitive to brutal uplift motions of the ground or sea surface, triggering upward propagating mechanical waves. In specific conditions that we will detail in this presentation, GNSS-TEC measurements contain critical information on the immediate consequences of an earthquake. If accurate and provided rapidly, independent knowledge of the sea surface deformation extent and distribution could feed tsunami early warning systems.Radio waves emitted by GNSS satellites integrate the ionospheric electron density wavefield along their propagation path. At ground level, GNSS receivers can only sense the TEC, which contains the contribution of the ionospheric wavefronts. These wavefronts are destructively or constructively integrated, depending on the involved geometry of observation. In some conditions, even a close station will not sense the TEC perturbation, while a station located 200 km away will sense large TEC fluctuations. This complex behavior mainly depends on the line-of-sight 3D geometry crossing the electron density perturbation. To study how this geometry can affect the estimation of the generating motion, we first build TEC sensitivity maps and highlight more blind or sensitive zones at the Earth’s surface. We apply the procedure to past tsunamigenic earthquakes at mid and low latitudes. Those are the 2010 Mw 7.6 Mentawaii earthquake (Indonesia), the 2016 Mw 7.8 Kaikoura earthquake (New Zealand), and the 2010 Mw 8.8 Maule earthquake (Chile). The TEC sensitivity maps allow us to investigate how the reciprocal locations of the available GNSS stations and satellites can affect the localization of the origin of the ionospheric disturbances. In a second step, we build localization maps with a full waveform method (IonoSeis software) and, where possible, with a time delay fitting method. We compare the resulting maps with the Earth’s surface deformation distribution estimated by more conventional seismo-geodetic methods. We finally show how the extension and densification of GNSS networks with multi-GNSS low-cost receivers and enhanced ionosphere monitoring could help mitigate tsunamis better.