Ionospheric disturbances associated with the 2015 M7.8 Nepal earthquake

Zhou, Yuemin; Yang, Jiaji; Zhu, Fengxiao; Su, Fanfan; Hu, Leyin; Zhai, Wenbo

doi:10.1016/j.geog.2017.04.004

Cited by 25 publications

(12 citation statements)

References 43 publications

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“…Liu et al () reported RW CIDs propagating with velocities 2.0–2.3 km/s. It is interesting to note, that analyzing the same set of data as in two previous reports, Zhou et al () failed to detect any RW CIDs. Difficulties with obtaining CMONOC GNSS stations data prohibit us from more detailed analysis of RW CIDs in that region.…”

Section: Study Case Descriptionmentioning

confidence: 55%

“…Chen et al (), Zhou et al (), and Liu et al () also analyzed CIDs based on International GNSS Service and Crustal Movement Observation Network of China (CMONOC) data. Chen et al () found CIDs of maximum amplitude of 1.34 TECu propagating with 0.61 and 1.62 km/s and driven by near‐field AGWs and RW AWs, respectively, at near‐epicentral region, then RW CIDs with phase velocities of 2.35 km/s at 500–1,500 km distances and RW CIDs of 2.74 km/s at distances

>

1,500 km from epicenter.…”

Section: Study Case Descriptionmentioning

confidence: 99%

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Modeling of Ionospheric Responses to Atmospheric Acoustic and Gravity Waves Driven by the 2015 Nepal 7.8 Gorkha Earthquake

et al. 2020

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Near-and far-field ionospheric responses to atmospheric acoustic and gravity waves (AGWs) generated by surface displacements during the 2015 Nepal M w 7.8 Gorkha earthquake are simulated. Realistic surface displacements driven by the earthquake are calculated in three-dimensional forward seismic waves propagation simulation, based on kinematic slip model. They are used to excite AGWs at ground level in the direct numerical simulation of three-dimensional nonlinear compressible Navier-Stokes equations with neutral atmosphere model, which is coupled with a two-dimensional nonlinear multifluid electrodynamic ionospheric model. The importance of incorporating earthquake rupture kinematics for the simulation of realistic coseismic ionospheric disturbances (CIDs) is demonstrated and the possibility of describing faulting mechanisms and surface deformations based on ionospheric observations is discussed in details. Simulation results at the near-epicentral region are comparable with total electron content (TEC) observations in periods (∼3.3 and ∼6-10 min for acoustic and gravity waves, respectively), propagation velocities (∼0.92 km/s for acoustic waves) and amplitudes (up to ∼2 TECu). Simulated far-field CIDs correspond to long-period (∼4 mHz) Rayleigh waves (RWs), propagating with the same phase velocity of ∼4 km/s. The characteristics of modeled RW-related ionospheric disturbances differ from previously-reported observations based on TEC data; possible reasons for these differences are discussed.

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Section: Study Case Descriptionmentioning

confidence: 55%

>

1,500 km from epicenter.…”

Section: Study Case Descriptionmentioning

confidence: 99%

Modeling of Ionospheric Responses to Atmospheric Acoustic and Gravity Waves Driven by the 2015 Nepal 7.8 Gorkha Earthquake

et al. 2020

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“…Standard procedures for detecting TEC variations include filtration 2 of 16 and detrending. In the bulk of studies, the following techniques are used to detrend data: the moving average [6,7], and high-order polynomial [8][9][10]. To filter data, one most often uses either the moving average [3], or the Butterworth filter [11,12] used a short-time Fourier transform (STFT) to avoid the filtering problem.…”

Section: Introductionmentioning

confidence: 99%

Wave Signatures in Total Electron Content Variations: Filtering Problems

2020

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Over recent years, global navigation satellite systems (GNSSs) have been increasingly used to study near-Earth space. The basis for such studies is the total electron content (TEC) data. Standard procedures for detecting TEC wave signatures include variation selection and detrending. Our experience showed that the inaccurate procedure causes artifacts in datasets which might affect data interpretation, particularly in automated processing. We analyzed the features of various detrending and variation selection methods. We split the problem of the GNSS data filtering into two subproblems: detrending and variation selection. We examined centered moving average, centered moving median, 6th-order polynomial, Hodrick–Prescott filter, L1 filter, cubic smoothing spline, double-applied moving average for the GNSS-TEC detrending problem, and centered moving average, centered moving median, Butterworth filter, type I Chebyshev filter for the GNSS-TEC variation selection problem in this paper. We carried out the analysis based on both model and experimental data. Modeling was based on simple analytical models as well as the International Reference Ionosphere. Analysis of TEC variations of 2–10 min, 10–20 min, and 20–60 min under insufficient detrending conditions showed that the higher errors appear for the longer periods (20–60 min). For the detrending problem, the smoothing cubic spline provided the best results among the algorithms discussed in this paper. The spline detrending featured the minimal value of the mean bias error (MBE) and the root-mean-square error (RMSE), as well as high correlation coefficient. The 6th-order polynomial also produced good results. Spline detrending does not introduce a RMSE more than 0.25 TECU and MBE > 0.35 TECU for IRI trends, while, for the 6th-order polynomial, these errors can exceed 1 TECU in some cases. However, in 95% of observations the RMSE and MBE do not exceed 0.05 TECU. For the variation selection, the centered moving average filter showed the best performance among the algorithms discussed in this paper.

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“…So far, two mechanisms are in place to explain the seismic-ionospheric coupling. In the first mechanism, the atmospheric acoustic gravity waves are excited by the vertical ground displacement or underground gas release during earthquake preparation time, which propagate to the ionosphere affecting the ionization (Garmash et al, 1989;Harrison et al, 2010;Kim & Nikiforova, 1997;Pulinets & Davidenko, 2014;Rolland et al, 2011;Shalimov & Gokhberg, 1998;Zhou et al, 2017). In the second mechanism, an additional vertical electric field penetrates into the ionosphere due to earthquake fault rupture, which causes redistribution of charged particles (Freund, 2000;Hadjicontis & Mavromatou, 1996;Hao, 1988;Pulinets, 2009).…”

Section: Introductionmentioning

confidence: 99%

Earthquake/Tsunami‐Linked Imprints in the Equatorial F Region Zonal Plasma Drifts and Spatial Structures of Plasma Bubbles

et al. 2019

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We report preseismic and postseismic distinct features observed in equatorial ionospheric F region in Indian longitude during two major earthquakes that occurred on 28 March 2005 (Mw=8.6) and 26 December 2004 (Mw=9.1) with epicenters near Sumatra, Indonesia. We used spaced receiver scintillation observations on a 251‐MHz signal transmitted from a geostationary satellite and recorded at dip equatorial station Tirunelveli, which is ≈2,200 km away from the epicenter of these earthquakes. On 28 March 2005, we noticed significant enhancement (≈88.5 m/s) in F region zonal plasma drift as compared to their seasonal quiet time drifts, 20 min prior to the start of earthquake. This implies the presence of additional vertically downward electric field of approximately 3 mV/m, which is linked with the earthquake mechanism. It is unusual because so far we have not encountered such feature on either magnetically quiet or disturbed days. Spatial scales associated with equatorial spread F irregularities generated in the postsunset hours on this earthquake day are considerably smaller. It suggests that the earthquake‐linked electric field influenced the structuring and evolution of equatorial spread F irregularities on this earthquake day. Another earthquake that occurred on 26 December 2004 triggered a tsunami. We noticed that the earthquake‐ and tsunami‐linked effects in the F region plasma drifts are distinctly observed even 18.5 hr after the earthquake.

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Ionospheric disturbances associated with the 2015 M7.8 Nepal earthquake

Cited by 25 publications

References 43 publications

Modeling of Ionospheric Responses to Atmospheric Acoustic and Gravity Waves Driven by the 2015 Nepal 7.8 Gorkha Earthquake

Modeling of Ionospheric Responses to Atmospheric Acoustic and Gravity Waves Driven by the 2015 Nepal 7.8 Gorkha Earthquake

Wave Signatures in Total Electron Content Variations: Filtering Problems

Earthquake/Tsunami‐Linked Imprints in the Equatorial F Region Zonal Plasma Drifts and Spatial Structures of Plasma Bubbles

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