Mapping the Impact of Non-Tectonic Forcing mechanisms on GNSS measured Coseismic Ionospheric Perturbations

Abstract

Global Navigation Satellite System (GNSS) measured Total Electron Content (TEC) is now widely used to study the near and far-field coseismic ionospheric perturbations (CIP). The generation of near field (~500–600 km surrounding an epicenter) CIP is mainly attributed to the coseismic crustal deformation. The azimuthal distribution of near field CIP may contain information on the seismic/tectonic source characteristics of rupture propagation direction and thrust orientations. However, numerous studies cautioned that before deriving the listed source characteristics based on coseismic TEC signatures, the contribution of non-tectonic forcing mechanisms needs to be examined. These mechanisms which are operative at ionospheric altitudes are classified as the i) orientation between the geomagnetic field and tectonically induced atmospheric wave perturbations ii) orientation between the GNSS satellite line of sight (LOS) geometry and coseismic atmospheric wave perturbations and iii) ambient electron density gradients. So far, the combined effects of these mechanisms have not been quantified. We propose a 3D geometrical model, based on acoustic ray tracing in space and time to estimate the combined effects of non-tectonic forcing mechanisms on the manifestations of GNSS measured near field CIP. Further, this model is tested on earthquakes occurring at different latitudes with a view to quickly quantify the collective effects of these mechanisms. We presume that this simple and direct 3D model would induce and enhance a proper perception among the researchers about the tectonic source characteristics derived based on the corresponding ionospheric manifestations.

Two‐Azimuth Co‐Seismic Ionospheric Disturbances Following the 2020 Jamaica Earthquake From GPS Observations

“…This result is consistent with previous works, i.e., the 2003 Tokachi‐Oki earthquake (Rolland et al., 2011), the 2008 Wenchuan earthquake (Zhao & Hao, 2015) and the 2012 Haida Gwaii earthquake (Jin, Jin, & Li, 2017). Because, previous works have shown that the plasma waves cannot propagate perpendicularly to MF lines and CID propagating to equatorward is at smaller angles with MF line and favorable to be detected for long disturbance (Astafyeva et al., 2014; Bagiya et al., 2019; Heki & Ping, 2005). The 2020 Jamacia earthquake occurred in the northern hemisphere, and the detailed information about the geomagnetic field near the epicenter area was obtained using the IGRF model from National Oceanic and Atmospheric Administration (NOAA, https://www.ngdc.noaa.gov).…”

Correlation Between GNSS‐TEC and Eruption Magnitude Supports the Use of Ionospheric Sensing to Complement Volcanic Hazard Assessment

“…Bagiya et al. (2019) defined a parameter named Non‐Tectonic Forcing Mechanisms which allows to evaluate the joint effect on the quality of the TEC detection of the local geomagnetic field orientation (Geomagnetic field—Neutral wave orientation Factor; GNF), the GNSS network geometry (Satellite Geometry Factor; SGF), and the effects of ambient electron density (Electron Density Factor; EDF) as: where λ , φ , and h depict the geographic longitude, latitude, and terrestrial altitude respectively.…”

“…M. Rolland et al, 2013;Sunil et al, 2015) have investigated the effects of the magnetic field on the propagation of ionospheric disturbances showing an important attenuation of the signal in regions where the perturbation is traveling in the same direction of the magnetic field. Bagiya et al (2019) defined a parameter named Non-Tectonic Forcing Mechanisms which allows to evaluate the joint effect on the quality of the TEC detection of the local geomagnetic field orientation (Geomagnetic field-Neutral wave orientation Factor; GNF), the GNSS network geometry (Satellite Geometry Factor; SGF), and the effects of ambient electron density (Electron Density Factor; EDF) as: 1 and Table 1 for legend reference and terminology. IVPI, ionospheric volcanic power index; VEI, volcanic explosivity index.…”

Near-real-time detection of co-seismic ionospheric disturbances using machine learning

“…In addition to fault mapping, our method can be used to estimate earthquake magnitude. The latter can be done by removing non-tectonic impacts introduced by the magnetic field configuration at the epicentral area, LOS geometry factor and the background ionization (Bagiya et al 2019).…”