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Article

Integrated Analysis of Lithosphere-Atmosphere-Ionospheric Coupling Associated with the 2021 Mw 7.2 Haiti Earthquake

by
Faisal Shahzad
1,
Munawar Shah
1,*,
Salma Riaz
2,
Bushra Ghaffar
3,
Irfan Ullah
4 and
Sayed M. Eldin
5
1
Space Education and GNSS Lab, National Center of GIS and Space Application, Institute of Space Technology, Islamabad 44000, Pakistan
2
Department of Applied Mathematics and Statistics, Institute of Space Technology, Islamabad 44000, Pakistan
3
Department of Environmental Science, Faculty of Basic and Applied Sciences, International Islamic University, Islamabad 44000, Pakistan
4
Department of Geology, Bacha Khan University, Charsadda 24420, Pakistan
5
Center of Research, Faculty of Engineering, Future University in Egypt, New Cairo 11511, Egypt
*
Author to whom correspondence should be addressed.
Atmosphere 2023, 14(2), 347; https://doi.org/10.3390/atmos14020347
Submission received: 5 January 2023 / Revised: 4 February 2023 / Accepted: 7 February 2023 / Published: 9 February 2023
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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Abstract

:
The search for Earthquake (EQ) precursors in the ionosphere and atmosphere from satellite data has provided significant information about the upcoming main shock. This study presents the abnormal atmospheric and ionospheric perturbations associated with the Mw 7.2 Haiti EQ on 14 August 2021 at geographical coordinates (18° N, 73° W) and shallow hypocentral depth of 10 km from the data of permanent Global Navigation Satellite System (GNSS) stations near the epicenter, followed by Swarm satellites data. The total vertical electron (VTEC) anomalies occur within a 5-day window before the main shock in the analysis of nearby operation stations, followed by Swarm (A and C satellites) ionospheric anomalies in the same 5-day window before the main shock. Moreover, the geomagnetic activities are completely quiet within 10 days before and 10 days after the main shock. Similarly, the atmospheric parameters endorse the EQ anomalies within 5 days before the main shock day. The evolution of gases from the lithosphere at the epicentral region possessed significant atmospheric and ionospheric perturbations within the EQ preparation period of 5-day before the main shock under the hypothesis of Lithosphere-Atmosphere-Ionosphere Coupling (LAIC).

1. Introduction

It is a fact that EQ causes huge damage to human life and infrastructure every year. There are a lot of different reports about early warning systems for possible natural hazard from various ground and satellite observations [1,2,3,4,5,6]. Moreover, the GNSS, Detection of Electro-Magnetic Emissions Transmitted from Earthquake Regions (DEMETER), and Swarm satellites also provide deep insights into the seismic precursors during the EQ preparation period [7,8,9,10,11]. The ionospheric data are studied with different statistical and mathematical methods to find a particular pattern in the form of positive and/or negative anomaly over the EQ preparation zone within a specific time window [12,13,14]. Some reports also correlated the ionospheric anomalies with geomagnetic storm [15,16], and a report even denied the ionospheric EQ anomalies with the current satellite cluster [17]. Seismic ionospheric anomalies over the epicenter may be described by three different hypotheses: positive holes (p-holes) emissions [18], an emanation of Radon [2], and Acoustic Gravity Waves (AGWs) [19]. P-hole emission from the rocks under stress in tectonic regions triggered the air ionization followed by the delays in the path of propagation of radio signals [20], and this air ionization may be due to the coupling of the LAIC process. On the other hand, Radon emanation from EQ-prone areas causes air ionization and atoms/molecules propagation to the ionosphere, causing delays in radio signals propagation over the EQ breed region [2]. The method for the electromagnetic field around the epicenter generated different signals within the seismic preparation region. After the investigation of the energy transmission route from below in the LAIC system, AGWs are found as the most likely energy carriers for troposphere to ionosphere over the EQ region [11]. The atmosphere acts as an amplitude amplifier, with amplification factors ranging from 103 (E region of the ionosphere) to 105 (F region of the ionosphere). At high elevations, air pressure oscillations induced by meteorological and other near-surface sources unavoidably became nonlinear, causing wave decay and dissipation [21].
The initiation of chemical processes in the atmosphere triggered by the emission of p-holes from EQ-prone regions further produced ionospheric anomalies. The p-holes hypothesis proposed the Earth and ionosphere as two ends of a capacitor, where charges flow from one end to another to form ionosphere perturbations [22]. Similarly, the abnormal concentration of Radon gas from the Earth’s crust along the fault lines in the seismogenic regions perturbed the passage of radio signals propagation over the fault lines. The seismic energy along the fault lines during the seismic preparation period is most probably more than the surrounding [2]. The variations in atmospheric and ionospheric layers over the epicenter are the indication of a release of seismic energy, which may follow by an EQ. The synchronized ionospheric and atmospheric anomalies over the epicenter from GNSS–TEC and other satellites-based electron density, plasma density, and electron temperature can confirm the coupling via the LAIC hypothesis [23] and rise in plasma density and electron temperature before the main shock day for 2010 Haiti earthquake, in the Caribbean region has been elaborated [24], where its source is the may be the p-hole or radon gas or the integration of both p-holes and radon. For example, Khan et al. [25] integrated the coupling of lithosphere and ionosphere from remote sensing and other satellites in the context of both p-holes and radon gas emissions. They showed the EQ energy emission due to radon gases and its propagation to the lower ionosphere via p-hole, followed by the variations in GNSS signals in the upper ionosphere.
The main aim of this paper is to study the EQ-induced ionospheric and atmospheric anomalies as seismic perturbations over the epicenter in a synchronized and co-located pattern over the fault lines and its validation by multiple satellite data. As the epicenter of Haiti, EQ Mw 7.2 is on a fault line and at the merge of two tectonic plates around a complex system of four fault lines, so in order to confirm both LAIC, we analyzed GNSS stations at different fault regions. Furthermore, previous studies did not explain the physical phenomena of the initiation of ionosphere perturbations; this study precisely discusses all the coupling methods. More emphasis on the unique method to find a clear anomaly in the ionospheric indices associated with the main shock.
Moreover, another main emphasis of this paper is on a reliable statistical bound method for scaling the seismically induced deviation in the ionosphere and atmosphere to provide an authentic method for forecasting seismic events in the future. This paper is divided into; Section 2 is about data and method of analysis, and Section 3, consists of results and discussions of this study. Similarly, Section 4 consists of a precise conclusion of the results.

2. Data and Methods

2.1. Data Retrieval

In this paper, atmospheric and ionospheric data have been investigated for the 14 August 2021 shallow hypocenter Haiti EQ of Mw 7.2. The geographical coordinates of the epicenter of the EQ that occurred in Haiti are listed in (Table 1). Haiti is located on the western part of Hispaniola Island in the Caribbean Sea, and the Dominican Republic is on the eastern side of Haiti (Figure 1). North American tectonic plate is on the north side of Haiti, the Caribbean plate is on the southern side of Haiti, and Gonave microplate is located on the western side of Haiti. Three fault zones surround Haiti; Septentrional Fault Zone is located on the northern side of Haiti, Oriento Fault Zone is located on the northwestern side, and Walton Fault Zone is located on the western side of Haiti. Enriquillo-Plantain Garden Fault Zone passes through Haiti, on which the epicenter of Mw 7.2 EQ is located, and this strong earthquake Mw 7.2, has a depth of 10 km (6.2 mi). Northeastern movements of about 0.5 m seismic slip on the Enriquillo-Plantain Garden Fault have been recorded after 14 August 2021 EQ. Haiti is located in a seismic-prone region having strike-slip faults and subduction faults (Figure 1). Detailed information about this EQ was retrieved from the United States Geological Survey (USGS) via the website https://earthquake.usgs.gov/earthquakes/search (accessed on 12 December 2022).

2.2. Space Weather Conditions

Geomagnetic data for solar storm indices of Disturbance Storm Time Index (Dst), an indicator of global geomagnetic disturbance of Planetary K index (Kp) and (ap), has been retrieved from International Services of Geomagnetic Indices (ISGI), https://isgi.unistra.fr/index.php (accessed on 12 December 2022) as the geomagnetic storm indices are important indicators in the determination and confirmation of LAIC processes from satellite data. In this paper, we analyzed the data from 25 July 2021 to 24 August 2021. The duration of the pre-EQ period is from 25 July 2021 to 13 August 2021, and the post-EQ period is from 15 August 2021 to 24 August 2021. The analysis of the geomagnetic indices for Mw 7.2 EQ has been performed during quiet storm conditions (Kp < 3: |Dst| < −30 nT). In this paper, we analyzed the atmospheric and ionospheric variations over the seismogenic zone of Mw 7.2 Haiti EQ during quiet geomagnetic activity with Kp < 3 and |Dst| < −30 nT in the Haiti region (Figure 2). Figure 2 shows that the geomagnetic storm conditions are quiet before and after the main shock day, so there is a possibility of analyzing abnormal ionospheric and atmospheric anomalies.

2.3. Data Processing

In this paper, we study ionospheric anomalies from Swarm satellites. Swarm three satellites are the European Space Agency (ESA) mission for the observation of Earth’s magnetic field with high precision and accuracy [26]. The Swarm constellation consists of three identical satellites designated as Alpha, Bravo, and Charlie (A, B, and C). Alpha and Charlie are parallel to each other at a lower orbit of about 460 km above the Earth’s surface, with a separation of around 1.4 degrees. On the other hand, the Bravo satellite has a slightly higher orbit of 510 km than the other satellites of the Swarm constellation. These orbital configurations are chosen largely to detect the fluctuations in the Earth’s magnetic field. It has many different sensors mounted on the satellites to observe the different atmospheric and electromagnetic variations [21].
GNSS TEC data were collected from four IGS stations operating nearby the seismogenic zone to compute a relation between the EQ and the ionosphere. Three IGS stations (CN06, SROD, and TGDR) are located in the Dominican Republic, and one station (TNSJ) is located in Mexico (Table 2).
In this paper, we retrieved the atmospheric and ionospheric data over and around the epicenter within the Dobrovolsky region [27]. By using the below equation, the radius of the EQ breeding zone is estimated as 1247 km.
R = 10 0.43   M w
where, R and Mw are the radii of the EQ preparation zone in km and the magnitude of the impending main shock, respectively. In this study, the slant TEC along the ray path of the recorded signal from the satellite to the receiver is computed in a unit of TECU (one TECU = 1016 electrons/m2) [19,28,29,30]. The time series of the GNSS stations are analyzed using the confidence bound methods of median and Inter Quartile Range (IQR). The confidence bounds for a single day are computed by the median and IQR of 10 days before and after the observed day, as in the below equations.
U B = x ˜ + I Q R
L B = x ˜ − I Q R
In the above equations, x ˜ and IQR is median and IQR, respectively. The abnormal TEC value is evaluated by the above statistical method for 20 days before and 10 days after EQ. The original TEC values beyond any bound are declared as an ionospheric anomaly. Moreover, the positive or negative anomaly is the difference between the original TEC from the upper or lower bound, respectively. This is shown as differential TEC in the GNSS data like in previous reports, and a cut-off angle of 15° is taken for the study from TEC retrieval [31,32,33,34,35,36,37]. The anomalies in the data of Swarm and other remote sensing satellites are detected by implementing the bounds of mean and standard deviation (M ± 2σ). The original daily values of Swarm and other satellite data are sandwiched in the bound of mean and standard deviation of all the data for all the days in this study, where the values beyond and below the bounds are denoted as anomalies.
In order to study the path of propagation of ionospheric anomalies via the atmosphere, it is important to study the atmospheric parameters. In this paper, we study the atmosphere parameters from the online database of Geospatial Interactive Online Visualization and Analysis Infrastructure (GIOVANNI) from NASA website https://giovanni.gsfc.nasa.gov/giovanni/ (accessed on 1 February 2022). These datasets include Relative Humidity (RH), Air Temperature (AT), Surface Temperature (ST), and Outgoing Longwave Radiation (OLR).

3. Results and Discussion

The ionospheric anomalies are observed 20 days before and 10 days after the main shock in the VTEC of all ground GNSS stations except TNSJ (Figure 3). The TNSJ stations are situated in Mexico outside the EQ preparation zone, the main shock in Haiti. It is included in the study to confirm the legitimate EQ-induced ionospheric variations. The other GNSS stations, CN06, SROD, and TGDR stations, are located in the Dominican Republic within the EQ preparation region of the Haiti main shock. Though, the TNSJ station is located outside the seismogenic zone.
Interestingly, CN06, SROD, and TGDR stations show clear VTEC anomalies before the main shock, as it is located inside the seismic zone. The abnormal rise in the original VTEC on day 5 before the main shock beyond the upper bound is more than 10 TECUs in the nearby operating stations of the main shock (Figure 4 and Figure 5). On the other hand, the GNSS station outside the Dobrovolsky region has no clear variation in VTEC 5 days before the main shock. The VTEC anomalies before the main shock are clear in the UT 0800 h to UT 1200 h. Another important thing to note is all are positive ionospheric anomalies (anomalous beyond the upper bound).
It is clear that the ionospheric anomalies in the GNSS stations are clear in the 5–10-day window before the main shock of the Mw 7.2 EQ. However, the anomaly of day 5 is more than 10 TECUs before the EQ, which is a clear indication of abnormal energy emission over the seismogenic region. One more thing is clear the TNSJ station outside the seismic zone has no clear variations before and after the main shock, and it is important for a GNSS station to operate within the Dobrovolsky region for the monitoring of EQ abnormal ionospheric anomalies. To further validate the ionospheric anomalies, we performed a statistical analysis on the Swarm satellites data to clear the abnormal main shock anomalies.
Since the EQ anomalies in GNSS occurred during the daytime, we also analyzed daytime (Slant Total Electron Content) STEC [38], electron temperature, and plasma density from Swarm three satellites before and after the main shock day (Figure 6, Figure 7, Figure 8, Figure 9, Figure 10, Figure 11 and Figure 12). The daytime enhancements are clear on the day (−5) in plasma density and STEC due to the upward rise of gases over the epicenter due to the EQ preparation process. Furthermore, the abnormal rise in the ionosphere is clear in Swarm satellites like GNSS VTEC anomalies. The variations are also verified by the differential values of Swarm three satellites in the form of Slant TEC and plasma density anomalies before the main shock (Figure 8, Figure 10 and Figure 12). The variations in Swarm data are clearly related to EQ as the storm indices are showing quiet storm activities (top panels of Figure 6, Figure 8 and Figure 10). On the other hand, TEC anomalies as deviation are visible before the seismic event within 5 days from the Swarm satellites (Figure 9, Figure 11 and Figure 13). The plasma density and TEC anomalies from Swarm satellites endorse the variations from the nearby GNSS station of the Haiti event during the EQ preparation period before the main shock. It is also important to mention that the ionospheric anomalies are clearly correlated with the lithosphere activity because the GNSS and Swarm satellites both confirm the variations. Therefore, these pre-seismic anomalies can assist in the development of a stringent forecasting system for seismic monitoring from multiple satellite measurements.
The comparative analysis of plasma density and electron temperature from Swarm’s three satellites clearly shows the abnormal variations on August 10, 2021 (−5 day) before the main shock day (Figure 12). The daytime enhancement in GNSS TEC data is further endorsed by the daytime plasma density and electron temperature data along with STEC data from Swarm’s three satellites. It also shows that the ionospheric anomalies began to initiate on day −6 before the main shock and reached to abnormal peak on day −5, followed by a decline in ionospheric indices on day −4 before the main shock. This analysis confirms the previous findings [39,40,41,42]. Moreover, the method for detecting anomalies is new as compared to previous methods [43,44,45,46].
Atmospheric indices values of daily OLR, RH, AT, and ST are evaluated within 20 days before and 10 days after the main shock (Figure 13 and Figure 14) [47,48,49,50]. While analyzing atmospheric indices, the deviation in time series data over the epicentral zone beyond the upper and lower bounds is clear on days (−5, −4, −3) before the main shock day. Similarly, the ionospheric anomalies are also clear on day −5 before the main shock. Hence, a synchronized and co-located anomalous window in the atmosphere and ionosphere on day −5 confirmed the LAIC coupling for Mw 7.2 (Haiti) EQ. While evaluating the data of daily OLR values at 400 mb for the month of July and August 2021, the results have shown a sudden and sharp rise, where a rise was observed on the day (−3rd), and the maximum value recorded was 295 W/m2 (Figure 14a). Similarly, the data of ST for the month of July and August 2021 has a clear rise on day −4, with a maximum value of 306 K. These variations are clear like previous papers [51,52,53,54]. However, the deviation can be positive beyond the upper bound [55,56,57,58]. Maximum deviation from the upper bound of AT time series values has been observed on day −5 with a maximum value of 259 K, and maximum deviation from the lower bound in the case of ST has been observed on day −5 with a minimum value of 29% before the main shock of Haiti EQ.
In this paper, the ionospheric indices show abnormal variations from GNSS and Swarm satellites within a time window of 5 days before the main shock day. Furthermore, the same variations are confirmed by the different atmospheric parameters within the same time window of 5 days before the main shock. We observed an increase in 10–12 TECU in daily VTEC from GNSS and Swarm during the daytime in UT 0800–1200 h before the seismic event [59,60,61,62]. The enhancement in the ionosphere before the main shock confirms the emission of huge energy from the seismogenic region due to tectonic stress building in rocks beneath the epicenter. These results are evidence of the possible precursors of ionospheric and atmospheric anomalies associated with Mw 7.2 Haiti (Table 3). Both the ground stations from IGS and swarm satellite observations compromise on positive abnormal variations from the respective upper bounds, like previous reports [63,64,65,66]. In short, Earth observation satellites can provide more information about future EQ precursors.
Additionally, all the ground and space satellites confirm the abnormal ionospheric variations within 5–10 days with huge intensity. The epicenter of 7.2 Haiti EQ lies between the two tectonic plates and on a fault line, where three other small fault lines merge in the vicinity of the epicenter [67,68,69,70]. There is a possibility of an abnormal amount of energy from the stressed rocks that can possibly perturb the ionosphere during seismic activity. Moreover, this energy occurred in the form of abnormal Radon gas emission, and the abnormal ionospheric clouds can possibly move from the lithosphere to the ionosphere due to the p-hole hypothesis, which is generated at hypocentral depth and further ionizes the atmosphere over the seismogenic zone, followed by excitation of ionospheric electron content during the seismic preparation period [71,72,73,74,75]. After this electron content reaches a certain threshold value, it causes to deviate the path of satellite signals during its journey toward the receiver stations. The need to aid more sensors for EQ monitoring on the other Earth observation satellites for possible forecasting of the main shock.

4. Conclusions

The atmospheric and ionospheric anomalies associated with the Mw 7.2 from different ground and space instruments indicate the LAIC coupling during the seismic preparation period. The following findings in the study are summarized below.
a.
The ionospheric anomalies before the main shock occurred in VTEC from GNSS stations operating nearby within 5–10 days before the main shock during quiet storm conditions in the EQ preparation period. The ionospheric anomaly of GNSS is endorsed by the Swarm satellites on the day (−5) before the main shock in STEC and plasma density from the A and C satellites of the mission. All these anomalies occurred as positive enhancement beyond the upper bound during the daytime from GNSS and Swarm satellites.
b.
Moreover, the TEC anomalies from GNSS on 10 August 2021 (−the 5th day before the main shock) have prominently crossed the bound by an amount of 10–12 TECU during UT = 08–12 h in local time hours.
c.
The Swarm three satellites confirm the high rise in plasma density and STEC before the main shock as pre-seismic ionospheric anomalies, which endorse the LAIC coupling before the main shock. The STEC and plasma density of Swarm three satellites retrieved over the epicenter in the Dobrovolsky region shows an anomalous pattern before the seismic event, which is evidence of pre-seismic perturbations.
d.
Atmospheric parameters had shown maximum deviation in 5 days window before the main shock, and anomalies in OLR, RH, AT, and ST values occurred 5 days before of the main shock of Haiti EQ. The data has indicated variations well beyond the predefined upper and lower bounds in 5 days as LAIC coupling prior to Haiti EQ 2021.
e.
The anomalous increase in multiple satellite data noted above the confidence bound as a positive anomaly confirms the evaluation of gases from epicentral stressed rock variations during the seismic preparation zone. The synchronized pattern of multiple ionospheric and atmospheric anomalies in the same time window of 5 days before the main shock has strong evidence for the development of the LAIC hypothesis. This study also confirms the previous finding of the release of gases from epicentral cavities and its propagation from the lithosphere to the atmosphere via air ionization and condensation to further drift towards the ionosphere.

Author Contributions

Conceptualization, Data curation, M.S.; methodology, software, F.S.; writing—review and editing, S.R., B.G., I.U. and S.M.E. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors are very grateful to USGS and UNAVCO for EQ and GNSS data, respectively. We are also thankful to OMNI Web NASA, NOAA, and Kyoto University for providing space weather data. Special thanks to the Institute of Space Technology, Islamabad, Pakistan, for providing a working space to conduct this research.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Liu, J.Y.; Chen, Y.I.; Huang, C.C.; Parrot, M.; Shen, X.H.; Pulinets, S.A.; Yang, Q.S.; Ho, Y.Y. A spatial analysis on seismo-ionospheric anomalies observed by DEMETER during the 2008 M8.0 Wenchuan earthquake. J. Asian Earth Sci. 2015, 114 Pt 2, 414–419. [Google Scholar] [CrossRef]
  2. Pulinets, S.; Ouzounov, D. Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) model—An unified concept for earthquake precursors validation. J. Asian Earth Sci. 2011, 41, 371–382. [Google Scholar] [CrossRef]
  3. Ryu, K.; Parrot, M.; Kim, S.G.; Jeong, K.S.; Chae, J.; Pulinets, S.; Oyama, K.I. Suspected seismo-ionospheric coupling observed by satellite measurements and GPS TEC related to the M7.9 Wenchuan earthquake of 12 May 2008. J. Geophys. Res. Space Phys. 2014, 119, 10305–10323. [Google Scholar] [CrossRef]
  4. Sekertekin, A.; Inyurt, S.; Yaprak, S. Pre-seismic ionospheric anomalies and spatio-temporal analyses of MODIS Land surface temperature and aerosols associated with Sep, 24 2013 Pakistan Earthquake. J. Atmos. Sol.-Terr. Phys. 2020, 200, 105218. [Google Scholar] [CrossRef]
  5. Shah, M.; Jin, S. Statistical characteristics of seismo-ionospheric GPS TEC disturbances prior to global Mw ≥ 5.0 earthquakes (1998–2014). J. Geodyn. 2015, 92, 42–49. [Google Scholar] [CrossRef]
  6. Shah, M.; Jin, S. Pre-seismic ionospheric anomalies of the 2013 Mw = 7.7 Pakistan earthquake from GPS and COSMIC observations. Geodesy Geodyn. 2018, 9, 378–387. [Google Scholar] [CrossRef]
  7. Satti, M.S.; Ehsan, M.; Abbas, A.; Shah, M.; de Oliveira-Júnior, J.F.; Naqvi, N.A. Atmospheric and ionospheric precursors associated with Mw ≥ 6.5 earthquakes from multiple satellites. J. Atmos. Sol. Terr. Phys. 2022, 227, 105802. [Google Scholar] [CrossRef]
  8. Shah, M.; Tariq, M.A.; Ahmad, J.; Naqvi, N.A.; Jin, S. Seismo ionospheric anomalies before the 2007 M7.7 Chile earthquake from GPS TEC and DEMETER. J. Geodyn. 2019, 127, 42–51. [Google Scholar] [CrossRef]
  9. Shah, M.; Tariq, M.A.; Naqvi, N.A. Atmospheric anomalies associated with Mw > 6.0 earthquakes in Pakistan and Iran during 2010–2017. J. Atmos. Sol.-Terr. Phys. 2019, 191, 105056. [Google Scholar] [CrossRef]
  10. Shahzad, R.; Shah, M.; Ahmed, A. Comparison of VTEC from GPS and IRI-2007, IRI-2012 and IRI-2016 over Sukkur Paki-stan. Astrophys. Space Sci. 2021, 366, 42. [Google Scholar] [CrossRef]
  11. Guo, J.; Shi, K.; Liu, X.; Sun, Y.; Li, W.; Kong, Q. Singular spectrum analysis of ionospheric anomalies preceding great earthquakes: Case studies of Kaikoura and Fukushima earthquakes. J. Geodyn. 2019, 124, 1–13. [Google Scholar] [CrossRef]
  12. Shah, M. Chapter 28—Earthquake ionospheric and atmospheric anomalies from GNSS TEC and other satellites. In Computers in Earth and Environmental Sciences; Pourghasemi, H.R., Ed.; Elsevier: Amsterdam, The Netherlands, 2022; pp. 387–399. [Google Scholar]
  13. Shah, M.; Ahmed, A.; Ehsan, M.; Khan, M.; Tariq, M.A.; Calabia, A.; Rahman, Z.U. Total electron content anomalies associated with earthquakes occurred during 1998–2019. Acta Astronaut. 2020, 175, 268–276. [Google Scholar] [CrossRef]
  14. Shah, M.; Aibar, A.C.; Tariq, M.A.; Ahmed, J.; Ahmed, A. Possible ionosphere and atmosphere precursory analysis related to Mw > 6.0 earthquakes in Japan. Remote Sens. Environ. 2020, 239, 111620. [Google Scholar] [CrossRef]
  15. Thomas, J.N.; Huard, J.; Masci, F. A statistical study of global ionospheric map total electron content changes prior to occurrences of M ≥ 6.0 earthquakes during 2000–2014. J. Geophys. Res. Space Phys. 2017, 122, 2151–2161. [Google Scholar] [CrossRef]
  16. Li, W.; Zhao, D.; He, C.; Hancock, C.M.; Shen, Y.; Zhang, K. Spatial-Temporal Behaviors of Large-Scale Ionospheric Per-turbations During Severe Geomagnetic Storms on September 7–8 2017 Using the GNSS, SWARM and TIE-GCM Techniques. J. Geophys. Res. Space Phys. 2022, 127, e2021JA029830. [Google Scholar] [CrossRef]
  17. Afraimovich, E.L.; Altyntsev, A.T.; Kosogorov, E.A.; Larina, N.S.; Leonovich, L.A. Ionospheric effects of the solar flares of September 23, 1998 and July 29, 1999 as deduced from global GPS network data. J. Atmos. Sol.-Terr. Phys. 2001, 63, 1841–1849. [Google Scholar] [CrossRef]
  18. Freund, F. Pre-earthquake signals: Underlying physical processes. J. Asian Earth Sci. 2011, 41, 383–400. [Google Scholar] [CrossRef]
  19. Heki, K. Ionospheric electron enhancement preceding the 2011 Tohoku-Oki earthquake. Geophys. Res. Lett. 2011, 38, L17312. [Google Scholar] [CrossRef]
  20. Freund, F. Toward a unified solid state theory for pre-earthquake signals. Acta Geophys. 2010, 58, 719–766. [Google Scholar] [CrossRef]
  21. De Santis, A.; Marchetti, D.; Pavón-Carrasco, F.J.; Cianchini, G.; Perrone, L.; Abbattista, C.; Alfonsi, L.; Amoruso, L.; Campuzano, S.A.; Carbone, M.; et al. Precursory worldwide signatures of earthquake occurrences on Swarm satellite data. Sci. Rep. 2019, 9, 20287. [Google Scholar] [CrossRef] [Green Version]
  22. Freund, F. Stress-activated positive hole charge carriers in rocks and the generation of pre-earthquake signals. In Electro-magnetic Phenomena Associated with Earthquakes; Terra Scientific Publishing: Tokyo, Japan, 2009; pp. 41–96. [Google Scholar]
  23. Sarkar, S.K.; Choudhary, S.K.; Sonakia, A.; Vishwakarma, A.; Gwal, A.K. Ionospheric anomalies associated with the Haiti earthquake of 12 January 2010 observed by DEMETER satellite. Nat. Hazards Earth Syst. Sci. 2012, 12, 671–678. [Google Scholar] [CrossRef]
  24. Zhang, X.; Chen, C.H. Lithosphere–Atmosphere–Ionosphere Coupling Processes for Pre-, Co-, and Post-Earthquakes. Atmosphere 2022, 14, 4. [Google Scholar] [CrossRef]
  25. Khan, M.M.; Ghaffar, B.; Shahzad, R.; Khan, M.R.; Shah, M.; Amin, A.H.; Eldin, S.M.; Naqvi, N.A.; Ali, R. Atmospheric Anomalies Associated with the 2021 Mw 7.2 Haiti Earthquake Using Machine Learning from Multiple Satellites. Sustainability 2022, 14, 14782. [Google Scholar] [CrossRef]
  26. Marchetti, D.; De Santis, A.; D’Arcangelo, S.; Poggio, F.; Piscini, A.; Campuzano, S.A.; De Carvalho, W.V. Pre-earthquake chain processes detected from ground to satellite altitude in preparation of the 2016–2017 seismic sequence in Central Italy. Remote Sens. Environ. 2019, 229, 93–99. [Google Scholar] [CrossRef]
  27. Dobrovolsky, I.P.; Zubkov, S.I.; Miachkin, V.I. Estimation of the size of earthquake preparation zones. Pure Appl. Geophys. 1979, 117, 1025–1044. [Google Scholar] [CrossRef]
  28. Okazaki, I.; Heki, K. Atmospheric temperature changes by volcanic eruptions: GPS radio occultation observations in the 2010 Icelandic and 2011 Chilean cases. J. Volcanol. Geotherm. Res. 2012, 245–246, 123–127. [Google Scholar] [CrossRef]
  29. Abbasi, A.R.; Shah, M.; Ahmed, A.; Naqvi, N.A. Possible ionospheric anomalies associated with the 2009 Mw 6.4 Taiwan earthquake from DEMETER and GNSS TEC. Acta Geod. Geophys. 2021, 56, 77–91. [Google Scholar] [CrossRef]
  30. Ahmed, J.; Shah, M.; Awais, M.; Jin, S.; Zafar, W.A.; Ahmad, N.; Amin, A.; Shah, M.A.; Ali, I. Seismo-ionospheric anomalies before the 2019 Mirpur earthquake from ionosonde measurements. Adv. Space Res. 2021, 69, 26–34. [Google Scholar] [CrossRef]
  31. Ahmed, J.; Shah, M.; Zafar, W.A.; Amin, M.A.; Iqbal, T. Seismoionospheric anomalies associated with earthquakes from the analysis of the ionosonde data. J. Atmos. Sol. Terr. Phys. 2018, 179, 450–458. [Google Scholar] [CrossRef]
  32. Hafeez, A.; Shah, M.; Ehsan, M.; Jamjareegulgarn, P.; Ahmed, J.; Tariq, M.A.; Iqbal, S.; Naqvi, N.A. Possible Atmosphere and Ionospheric Anomalies of the 2019 Pakistan Earthquake Using Statistical and Machine Learning Procedures on MODIS LST, GPS TEC, and GIM TEC. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 11126–11133. [Google Scholar] [CrossRef]
  33. Mehdi, S.; Shah, M.; Naqvi, N.A. Lithosphere atmosphere ionosphere coupling associated with the 2019 Mw 7.1 California earthquake using GNSS and multiple satellites. Environ. Monit. Assess. 2021, 193, 501. [Google Scholar] [CrossRef]
  34. Shah, M.; Ehsan, M.; Abbas, A.; Ahmed, A.; Jamjareegulgarn, P. Possible Thermal Anomalies Associated with Global Terrestrial Earthquakes During 2000–2019 Based on MODIS-LST. IEEE Geosci. Remote Sens. Lett. 2021, 19, 1–5. [Google Scholar] [CrossRef]
  35. Shah, M.; Inyurt, S.; Ehsan, M.; Ahmed, A.; Shakir, M.; Ullah, S.; Iqbal, M.S. Seismo ionospheric anomalies in Turkey associated with Mw ≥ 6.0 earthquakes detected by GPS stations and GIM TEC. Adv. Space Res. 2020, 65, 2540–2550. [Google Scholar] [CrossRef]
  36. Shah, M.; Qureshi, R.U.; Khan, N.G.; Ehsan, M.; Yan, J. Artificial Neural Network based thermal anomalies as-sociated with earthquakes in Pakistan from MODIS LST. J. Atmos. Sol. Terr. Phys. 2021, 215, 105568. [Google Scholar] [CrossRef]
  37. Panda, S.K. Robustness of Elevation Cut-off in Estimating Ionospheric Total Electron Content from GPS Observation Data. In Proceedings of the 8th International Conference On Microwaves, Antenna, Propagation & Remote Sensing (ICMARS-2012), Jodhpur, India, 10–15 December 2012. [Google Scholar] [CrossRef]
  38. Xiang, Y.; Gao, Y. An Enhanced Mapping Function with Ionospheric Varying Height. Remote Sens. 2019, 11, 1497. [Google Scholar] [CrossRef]
  39. Quan, Q.; Gao, S.; Shang, Y.; Wang, B. Assessment of the sustainability of Gymnocypris eckloni habitat under river damming in the source region of the Yellow River. Sci. Total. Environ. 2021, 778, 146312. [Google Scholar] [CrossRef]
  40. Zhang, K.; Ali, A.; Antonarakis, A.; Moghaddam, M.; Saatchi, S.; Tabatabaeenejad, A.; Chen, R.; Jaruwatanadilok, S.; Cuenca, R.; Crow, W.T.; et al. The Sensitivity of North American Terrestrial Carbon Fluxes to Spatial and Temporal Variation in Soil Moisture: An Analysis Using Radar-Derived Estimates of Root-Zone Soil Moisture. J. Geophys. Res. Biogeosci. 2019, 124, 3208–3231. [Google Scholar] [CrossRef]
  41. Liu, Y.; Zhang, K.; Li, Z.; Liu, Z.; Wang, J.; Huang, P. A hybrid runoff generation modelling framework based on spatial combination of three runoff generation schemes for semi-humid and semi-arid watersheds. J. Hydrol. 2020, 590, 125440. [Google Scholar] [CrossRef]
  42. Alam, Z.; Sun, L.; Zhang, C.; Samali, B. Influence of seismic orientation on the statistical distribution of nonlinear seismic response of the stiffness-eccentric structure. Structures 2022, 39, 387–404. [Google Scholar] [CrossRef]
  43. Li, Z.-J.; Zhang, K. Comparison of Three GIS-Based Hydrological Models. J. Hydrol. Eng. 2008, 13, 364–370. [Google Scholar] [CrossRef]
  44. Kim, Y.; Kimball, J.; Zhang, K.; Didan, K.; Velicogna, I.; McDonald, K. Attribution of divergent northern vegetation growth responses to lengthening non-frozen seasons using satellite optical-NIR and microwave remote sensing. Int. J. Remote Sens. 2014, 35, 3700–3721. [Google Scholar] [CrossRef]
  45. Sun, R.; Wang, J.; Cheng, Q.; Mao, Y.; Ochieng, W.Y. A new IMU-aided multiple GNSS fault detection and exclusion algorithm for integrated navigation in urban environments. GPS Solut. 2021, 25, 147. [Google Scholar] [CrossRef]
  46. Mao, Y.; Sun, R.; Wang, J.; Cheng, Q.; Kiong, L.C.; Ochieng, W.Y. New time-differenced carrier phase approach to GNSS/INS integration. GPS Solut. 2022, 26, 122. [Google Scholar] [CrossRef]
  47. Zhang, Y.; Luo, J.; Li, J.; Mao, D.; Zhang, Y.; Huang, Y.; Yang, J. Fast Inverse-Scattering Reconstruction for Airborne High-Squint Radar Imagery Based on Doppler Centroid Compensation. IEEE Trans. Geosci. Remote Sens. 2021, 60, 1–17. [Google Scholar] [CrossRef]
  48. Peng, J.; Xu, C.; Dai, B.; Sun, L.; Feng, J.; Huang, Q. Numerical Investigation of Brittleness Effect on Strength and Microcracking Behavior of Crystalline Rock. Int. J. Géoméch. 2022, 22, 04022178. [Google Scholar] [CrossRef]
  49. Guo, Y.; Luo, L.; Wang, C. Research on Fault Activation and Its Influencing Factors on the Barrier Effect of Rock Mass Movement Induced by Mining. Appl. Sci. 2023, 13, 651. [Google Scholar] [CrossRef]
  50. Li, R.; Wu, X.; Tian, H.; Yu, N.; Wang, C. Hybrid Memetic Pretrained Factor Analysis-Based Deep Belief Networks for Transient Electromagnetic Inversion. IEEE Trans. Geosci. Remote Sens. 2022, 60, 1–20. [Google Scholar] [CrossRef]
  51. Li, R.; Yu, N.; Wang, X.; Liu, Y.; Cai, Z.; Wang, E. Model-Based Synthetic Geoelectric Sampling for Magnetotelluric Inversion with Deep Neural Networks. IEEE Trans. Geosci. Remote Sens. 2020, 60, 1–14. [Google Scholar] [CrossRef]
  52. Wang, G.; Zhao, B.; Wu, B.; Wang, M.; Liu, W.; Zhou, H.; Han, Y. Research on the Macro-Mesoscopic Response Mech-anism of Multisphere Approximated Heteromorphic Tailing Particles. Lithosphere 2022, 2022, 1977890. [Google Scholar] [CrossRef]
  53. Wang, G.; Zhao, B.; Wu, B.; Zhang, C.; Liu, W. Intelligent prediction of slope stability based on visual exploratory data analysis of 77 in situ cases. Int. J. Min. Sci. Technol. 2023, 33, 47–59. [Google Scholar] [CrossRef]
  54. Li, J.; Cheng, F.; Lin, G.; Wu, C. Improved Hybrid Method for the Generation of Ground Motions Compatible with the Multi-Damping Design Spectra. J. Earthq. Eng. 2022, 26, 1–27. [Google Scholar] [CrossRef]
  55. Zhou, G.; Song, B.; Liang, P.; Xu, J.; Yue, T. Voids Filling of DEM with Multiattention Generative Adversarial Network Model. Remote Sens. 2022, 14, 1206. [Google Scholar] [CrossRef]
  56. Zhou, G.; Long, S.; Xu, J.; Zhou, X.; Song, B.; Deng, R.; Wang, C. Comparison Analysis of Five Waveform Decomposition Algorithms for the Airborne LiDAR Echo Signal. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 7869–7880. [Google Scholar] [CrossRef]
  57. Zhou, G.; Zhang, R.; Huang, S. Generalized Buffering Algorithm. IEEE Access 2021, 9, 27140–27157. [Google Scholar] [CrossRef]
  58. Zhang, X.; Ma, F.; Yin, S.; Wallace, C.D.; Soltanian, M.R.; Dai, Z.; Ritzi, R.W.; Ma, Z.; Zhan, C.; Lü, X. Application of upscaling methods for fluid flow and mass transport in multi-scale heterogeneous media: A critical review. Appl. Energy 2021, 303, 117603. [Google Scholar] [CrossRef]
  59. Huang, S.; Liu, C. A computational framework for fluid–structure interaction with applications on stability evaluation of breakwater under combined tsunami–earthquake activity. Comput. Civ. Infrastruct. Eng. 2022, 38, 325–352. [Google Scholar] [CrossRef]
  60. Huang, S.; Huang, M.; Lyu, Y. Seismic performance analysis of a wind turbine with a monopile foundation affected by sea ice based on a simple numerical method. Eng. Appl. Comput. Fluid Mech. 2021, 15, 1113–1133. [Google Scholar] [CrossRef]
  61. Liu, Y.; Zhang, Z.; Liu, X.; Wang, L.; Xia, X. Efficient image segmentation based on deep learning for mineral image classification. Adv. Powder Technol. 2021, 32, 3885–3903. [Google Scholar] [CrossRef]
  62. Zhao, T.; Shi, J.; Lv, L.; Xu, H.; Chen, D.; Cui, Q.; Jackson, T.J.; Yan, G.; Jia, L.; Chen, L.; et al. Soil moisture experiment in the Luan River supporting new satellite mission opportunities. Remote Sens. Environ. 2020, 240, 111680. [Google Scholar] [CrossRef]
  63. Meng, F.; Wang, D.; Yang, P.; Xie, G. Application of Sum of Squares Method in Nonlinear H∞ Control for Satellite Attitude Maneuvers. Complexity 2019, 2019, 1–10. [Google Scholar] [CrossRef]
  64. Du, Y.; Qin, B.; Zhao, C.; Zhu, Y.; Cao, J.; Ji, Y. A Novel Spatio-Temporal Synchronization Method of Roadside Asynchronous MMW Radar-Camera for Sensor Fusion. IEEE Trans. Intell. Transp. Syst. 2021, 23, 22278–22289. [Google Scholar] [CrossRef]
  65. Zhang, X.; Ma, F.; Dai, Z.; Wang, J.; Chen, L.; Ling, H.; Soltanian, M.R. Radionuclide transport in multi-scale fractured rocks: A review. J. Hazard. Mater. 2021, 424, 127550. [Google Scholar] [CrossRef] [PubMed]
  66. Tian, H.F.; Huang, N.; Niu, Z.; Qin, Y.C.; Pei, J.; Wang, J. Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm. Remote Sens. 2019, 11, 820. [Google Scholar] [CrossRef]
  67. Tian, H.; Pei, J.; Huang, J.; Li, X.; Wang, J.; Zhou, B.; Qin, Y.; Wang, L. Garlic and Winter Wheat Identification Based on Active and Passive Satellite Imagery and the Google Earth Engine in Northern China. Remote Sens. 2020, 12, 3539. [Google Scholar] [CrossRef]
  68. Chen, J.; Du, L.; Guo, Y. Label constrained convolutional factor analysis for classification with limited training samples. Inf. Sci. 2020, 544, 372–394. [Google Scholar] [CrossRef]
  69. Wang, N.; Xing, G.; Zhu, T.; Zhou, H.; Shi, Y. Propagating Seismic Waves in VTI Attenuating Media Using Fractional Viscoelastic Wave Equation. J. Geophys. Res. Solid Earth 2022, 127, e2021JB023280. [Google Scholar] [CrossRef]
  70. Jin, J.; Zhang, X.; Liu, X.; Li, Y.; Li, S. Study on critical slowdown characteristics and early warning model of damage evolution of sandstone under freeze–thaw cycles. Front. Earth Sci. 2023, 10, 1006642. [Google Scholar] [CrossRef]
  71. Wang, W.; Yuan, X.; Wu, X.; Liu, Y. Fast Image Dehazing Method Based on Linear Transformation. IEEE Trans. Multimedia 2017, 19, 1142–1155. [Google Scholar] [CrossRef]
  72. Yang, J.; Fu, L.-Y.; Zhang, Y.; Han, T. Temperature- and Pressure-Dependent Pore Microstructures Using Static and Dynamic Moduli and Their Correlation. Rock Mech. Rock Eng. 2022, 55, 4073–4092. [Google Scholar] [CrossRef]
  73. Cheng, Y.; Fu, L.-Y. Nonlinear seismic inversion by physics-informed Caianiello convolutional neural networks for overpressure prediction of source rocks in the offshore Xihu depression, East China. J. Pet. Sci. Eng. 2022, 215, 110654. [Google Scholar] [CrossRef]
  74. Tian, H.; Qin, Y.; Niu, Z.; Wang, L.; Ge, S. Summer Maize Mapping by Compositing Time Series Sentinel-1A Imagery Based on Crop Growth Cycles. J. Indian Soc. Remote Sens. 2021, 49, 2863–2874. [Google Scholar] [CrossRef]
  75. Li, Y.; Du, L.; Wei, D. Multiscale CNN Based on Component Analysis for SAR ATR. IEEE Trans. Geosci. Remote Sens. 2021, 60, 1–12. [Google Scholar] [CrossRef]
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Figure 1. The geographical location of Mw 7.2 EQ with the yellow dots represents the location of IGS stations. The red color circle represents the preparation zone estimated by the Dobrovolsky equation. The brown lines show the location of fault zones (information obtained from public domain data of USGS).
Figure 1. The geographical location of Mw 7.2 EQ with the yellow dots represents the location of IGS stations. The red color circle represents the preparation zone estimated by the Dobrovolsky equation. The brown lines show the location of fault zones (information obtained from public domain data of USGS).
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Figure 2. The solar and geomagnetic storm indices of (a) Kp, (b) Dst, (c) ap and (d) F are 10.7. The red color dashed line is for EQ day across all panels.
Figure 2. The solar and geomagnetic storm indices of (a) Kp, (b) Dst, (c) ap and (d) F are 10.7. The red color dashed line is for EQ day across all panels.
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Figure 3. The measurements were recorded at four IGS stations ((a) CN06, (b) SROD, (c) TGDR, (d) TNSJ) before and after the main shock day. As TNSJ station is located outside the Dobrovolsky region, it shows very minute TEC variations. The black dashed line represents the EQ day.
Figure 3. The measurements were recorded at four IGS stations ((a) CN06, (b) SROD, (c) TGDR, (d) TNSJ) before and after the main shock day. As TNSJ station is located outside the Dobrovolsky region, it shows very minute TEC variations. The black dashed line represents the EQ day.
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Figure 4. Deviation in VTEC beyond the upper bound on the day (−6, −5, −4) for (a) CN06 and (b) SROD stations before the main shock of Mw 7.2 EQ. The red dashed line represents the EQ day. The anomaly on day −5 is clear over the epicenter from GNSS satellites.
Figure 4. Deviation in VTEC beyond the upper bound on the day (−6, −5, −4) for (a) CN06 and (b) SROD stations before the main shock of Mw 7.2 EQ. The red dashed line represents the EQ day. The anomaly on day −5 is clear over the epicenter from GNSS satellites.
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Figure 5. Deviation in VTEC beyond upper bound on the day (−6, −5, −4) for (c) TGDR and (d) TNSJ stations before the main shock of Mw 7.2 EQ. The red dashed line represents the EQ day.
Figure 5. Deviation in VTEC beyond upper bound on the day (−6, −5, −4) for (c) TGDR and (d) TNSJ stations before the main shock of Mw 7.2 EQ. The red dashed line represents the EQ day.
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Figure 6. (a) and (b) The storm display of Dst and Kp for Mw 7.2 (Haiti). (c) Plasma density, (d) Electron Temperature (ET), and (e) STEC of Swarm-A for Mw 7.2 (Haiti). The red dashed line represents the EQ day.
Figure 6. (a) and (b) The storm display of Dst and Kp for Mw 7.2 (Haiti). (c) Plasma density, (d) Electron Temperature (ET), and (e) STEC of Swarm-A for Mw 7.2 (Haiti). The red dashed line represents the EQ day.
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Figure 7. (a) plasma density, (b) deviation of plasma density as an anomaly, (c) STEC, and (d) deviation of STEC from upper and lower bounds of mean and standard deviation as an anomaly from Swarm-A over Mw 7.2 (Haiti). The red dashed line represents the EQ day.
Figure 7. (a) plasma density, (b) deviation of plasma density as an anomaly, (c) STEC, and (d) deviation of STEC from upper and lower bounds of mean and standard deviation as an anomaly from Swarm-A over Mw 7.2 (Haiti). The red dashed line represents the EQ day.
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Figure 8. (a) and (b) The storm display of Dst and Kp for Mw 7.2 (Haiti). (c) Plasma density (d) Electron Temperature (ET) (e) STEC of Swarm-B for Mw 7.2 (Haiti). The red dashed line represents the EQ day.
Figure 8. (a) and (b) The storm display of Dst and Kp for Mw 7.2 (Haiti). (c) Plasma density (d) Electron Temperature (ET) (e) STEC of Swarm-B for Mw 7.2 (Haiti). The red dashed line represents the EQ day.
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Figure 9. (a) plasma density, (b) deviation of plasma density as an anomaly, (c) STEC, and (d) deviation of STEC from upper and lower bounds of mean and standard deviation as an anomaly from Swarm-B for Mw 7.2 (Haiti). The red dashed line represents the EQ day.
Figure 9. (a) plasma density, (b) deviation of plasma density as an anomaly, (c) STEC, and (d) deviation of STEC from upper and lower bounds of mean and standard deviation as an anomaly from Swarm-B for Mw 7.2 (Haiti). The red dashed line represents the EQ day.
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Figure 10. (a) and (b) The storm display of Dst and Kp for Mw 7.2 (Haiti). (c) Plasma density (d) Electron Temperature (ET) (e) STEC of Swarm-C satellite for Mw 7.2 (Haiti). The red dashed line represents the EQ day.
Figure 10. (a) and (b) The storm display of Dst and Kp for Mw 7.2 (Haiti). (c) Plasma density (d) Electron Temperature (ET) (e) STEC of Swarm-C satellite for Mw 7.2 (Haiti). The red dashed line represents the EQ day.
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Figure 11. (a) plasma density, (b) deviation of plasma density from upper and lower bounds of mean and standard deviation as an anomaly, (c) STEC, and (d) deviation of STEC from upper and lower bounds of mean and standard deviation as an anomaly from Swarm-C for Mw 7.2 (Haiti). The red dashed line represents the EQ day.
Figure 11. (a) plasma density, (b) deviation of plasma density from upper and lower bounds of mean and standard deviation as an anomaly, (c) STEC, and (d) deviation of STEC from upper and lower bounds of mean and standard deviation as an anomaly from Swarm-C for Mw 7.2 (Haiti). The red dashed line represents the EQ day.
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Figure 12. Comparison of daily values from Swarm (a–c) for Mw 7.2 (Haiti) EQ. The plasma density values are shown in blue, and Electron Temperature (ET) is shown by red bar lines, and the red dashed line represents the EQ day.
Figure 12. Comparison of daily values from Swarm (a–c) for Mw 7.2 (Haiti) EQ. The plasma density values are shown in blue, and Electron Temperature (ET) is shown by red bar lines, and the red dashed line represents the EQ day.
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Figure 13. (a) AT time series, (b) RH time series over the epicentral zone with upper and lower bounds of mean and standard deviation, (c) deviation of AT time series values, and ST time series values from upper and lower bounds of mean and standard deviation. The red dashed line indicates the EQ day.
Figure 13. (a) AT time series, (b) RH time series over the epicentral zone with upper and lower bounds of mean and standard deviation, (c) deviation of AT time series values, and ST time series values from upper and lower bounds of mean and standard deviation. The red dashed line indicates the EQ day.
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Figure 14. (a) OLR time series, (b) ST time series over the epicentral zone with upper and lower bounds of mean and standard deviation, (c) deviation of OLR time series values, and ST time series values from upper and lower bounds. The red dashed line indicates the EQ day.
Figure 14. (a) OLR time series, (b) ST time series over the epicentral zone with upper and lower bounds of mean and standard deviation, (c) deviation of OLR time series values, and ST time series values from upper and lower bounds. The red dashed line indicates the EQ day.
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Table
Table 1. Detailed information about the Haiti EQ.
Table 1. Detailed information about the Haiti EQ.
EQ Date and LocationLat (°)Long (°)Depth (km)MwStrike (°) Dip (°) EQ Preparation Zone
14/07/21 (12:29) UT, Nippes Haiti18.44° N73.49° W107.2266°51°1247 km
Table
Table 2. Detailed information about the IGS stations and their distances from the epicenter.
Table 2. Detailed information about the IGS stations and their distances from the epicenter.
S. NoGNSS StationDistance from Epicenter (km)Geographic Coordinates
Latitude (°)Longitude (°)
1.CN0630018.7900° N70.6560° W
2.SROD25319.4750° N71.3410° W
3.TGDR25418.2080° N71.0919° W
4.TNSJ245416.1724° N96.4895° W
Table
Table 3. Anomaly Observation with days of selected parameters.
Table 3. Anomaly Observation with days of selected parameters.
Anomaly Observation Days with Parameters
ParameterAnomalous Day before Upper BoundAnomalous Day before Lower Bound
Ionospheric (VTEC)−6, −5, −4
SWARM A (PD, STEC)Day-5
SWARM B (PD, STEC)NilNil
SWARM C (PD, STEC)Day −5
Atmospheric Parameter Air TemperatureDay −5
Atmospheric Parameter Relative Humidity Day −5
Atmospheric Parameter OLRDay −3
Atmospheric Parameter Surface Temperature Day −4
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MDPI and ACS Style

Shahzad, F.; Shah, M.; Riaz, S.; Ghaffar, B.; Ullah, I.; Eldin, S.M. Integrated Analysis of Lithosphere-Atmosphere-Ionospheric Coupling Associated with the 2021 Mw 7.2 Haiti Earthquake. Atmosphere 2023, 14, 347. https://doi.org/10.3390/atmos14020347

AMA Style

Shahzad F, Shah M, Riaz S, Ghaffar B, Ullah I, Eldin SM. Integrated Analysis of Lithosphere-Atmosphere-Ionospheric Coupling Associated with the 2021 Mw 7.2 Haiti Earthquake. Atmosphere. 2023; 14(2):347. https://doi.org/10.3390/atmos14020347

Chicago/Turabian Style

Shahzad, Faisal, Munawar Shah, Salma Riaz, Bushra Ghaffar, Irfan Ullah, and Sayed M. Eldin. 2023. "Integrated Analysis of Lithosphere-Atmosphere-Ionospheric Coupling Associated with the 2021 Mw 7.2 Haiti Earthquake" Atmosphere 14, no. 2: 347. https://doi.org/10.3390/atmos14020347

APA Style

Shahzad, F., Shah, M., Riaz, S., Ghaffar, B., Ullah, I., & Eldin, S. M. (2023). Integrated Analysis of Lithosphere-Atmosphere-Ionospheric Coupling Associated with the 2021 Mw 7.2 Haiti Earthquake. Atmosphere, 14(2), 347. https://doi.org/10.3390/atmos14020347

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