Global navigation satellite system detection of preseismic ionospheric total electron content anomalies for strong magnitude (Mw > 6) Himalayan earthquakes Gopal Sharma Prashant Kumar Champati ray Sarada Mohanty Param Kirti Rao Gautam Suresh Kannaujiya
Gopal Sharma, Prashant Kumar Champati ray, Sarada Mohanty, Param Kirti Rao Gautam, Suresh Kannaujiya, “Global navigation satellite system detection of preseismic ionospheric total electron content anomalies for strong magnitude (Mw > 6) Himalayan earthquakes,” J. Appl. Remote Sens. 11(4), 046018 (2017), doi: 10.1117/1.JRS.11.046018.
Global navigation satellite system detection of preseismic ionospheric total electron content anomalies for strong magnitude (Mw > 6) Himalayan earthquakes Gopal Sharma,a,b Prashant Kumar Champati ray,a,* Sarada Mohanty,b Param Kirti Rao Gautam,c and Suresh Kannaujiyaa a
Indian Institute of Remote Sensing, Geosciences and Geohazards Department, Dehradun, India b Indian Institute of Technology (Indian School of Mines), Department of Applied Geology, Dhanbad, India c Wadia Institute of Himalayan Geology, Dehradun, India
Abstract. Electron content in the ionosphere is very sensitive to temporary disturbances of the Earth’s magnetosphere (geomagnetic storm), solar flares, and seismic activities. The Global Navigation Satellite System (GNSS)-based total electron content (TEC) measurement has emerged as an important technique for computations of earthquake precursor signals. We examined the pre-earthquake signatures for eight strong magnitude (Mw > 6: 6.1 to 7.8) earthquakes with the aid of GNSS-based TEC measurement in the tectonically active Himalayan region using International GNSS Service (IGS) stations as well as local GNSS-based continuously operating reference stations (CORS). The results indicate very significant ionospheric anomalies in the vertical total electron content (vTEC) a few days before the main shock for all of the events. Geomagnetic activities were also studied during the TEC observation window to ascertain their role in ionospheric perturbations. It was also inferred that TEC variation due to low magnitude events could also be monitored if the epicenter lies closer to the GNSS or IGS station. Therefore, the study has confirmed TEC anomalies before major Himalayan earthquakes, thereby making it imperative to set up a much denser network of IGS/CORS for real-time data analysis and forewarning. © 2017 Society of Photo-Optical Instrumentation Engineers (SPIE) [DOI: 10.1117/1.JRS.11 .046018]
Keywords: total electron content; global navigation satellite system; earthquake precursor; seismoionospheric disturbance. Paper 170177 received Mar. 8, 2017; accepted for publication Nov. 2, 2017; published online Dec. 8, 2017.
1 Introduction Earthquakes are one of the most destructive phenomena of nature, causing large-scale loss of life and property. In the past 100 years, there has been an average of 18 large magnitude earthquakes (7 ≤ Mw ≤ 8) per year.1 Pre-earthquake signals are of utmost importance for developing a better understanding toward the prediction of future earthquakes. Many efforts have been made using various parameters, such as seismicity pattern,2 seismoelectromagnetic field,3 weather conditions, and unusual clouds,4 among others. Many have proposed a multiparametric approach to develop a robust system for earthquake precursor studies.5,6 Despite the development and efforts, the forecast of earthquakes on a time scale of a few days prior to its occurrence still remains an elusive goal. The advent of global positioning systems (GPSs) has led to a very promising development in this direction. The GPS signals propagating through the ionosphere are affected by the total electron content (TEC), which results in a change in the traveling velocity, thus, causing time offsets, which is a major source of error in GPS measurements.7–9 TEC is the total number of electrons present along a path between two points, with units of electrons per
*Address all correspondence to: Prashant Kumar Champati ray, E-mail:
[email protected] 1931-3195/2017/$25.00 © 2017 SPIE
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square meter, where 1016 electrons∕m2 ¼ 1 TEC unit (TECU). In the zone of earthquake preparation, mechanical and geochemical processes take place before the earthquake, such as the emanation of several types of gaseous components, including radon.10 According to one theory, stress building in rocks prior to earthquakes leads to the release of positive ions, which travel to the surface of the earth through the unstressed part of the rock and ionize the near ground atmosphere.11 As a result, a lot of positive ions travel through troposphere and join electrons in the lower ionosphere.12–14 In other words, the electrons are pulled in by positively charged ions, leading to depletion; as the process continues, it leads to an increase in concentration of electrons and thus to high TEC values. Therefore, the TEC low and high values in the lower ionosphere are of utmost importance to studying any precursor dealing with TEC.15–18 Most importantly, the basic hypothesis of near surface layer ionization has been demonstrated by experimental setup and instrumental observation in different parts of the world.19–21 The second theory on ionospheric precursors is related to the ion–molecular reactions after ionization by radon in the nearground layer of the atmosphere, and water molecule attachment to the finally formed ions that eventually make the ground layer of atmosphere rich in latent ions marked by the neutral clusters. Pulinets10 gave a detailed description of the process of ion cluster formation in the near-ground layer in the earthquake preparation zone. In the next stage, the generation of the anomalous electric field takes place. The gases released during earthquake preparation play a dual role. By generating air motion, they create instabilities, which initiate acoustic gravity wave generation. Subsequently, these air movements destroy neutral clusters because of a weakness in the Coulomb interaction. As a result, the near-ground layer of the atmosphere becomes rich in ions leading to the generation of an anomalously strong vertical electric field.22 Depending on the direction of the electric field on the ground surface, negative or positive deviations may be created in the electron concentration.23 The electric field without any decay penetrates to the higher levels of the ionosphere and is manifested in periodic electron density oscillations, registered at different ionospheric heights, as well as large-scale irregularities of electron concentrations in the F2 region of the ionosphere.24 The ionosphere is categorized into different regions based on its electron density. These regions are named D, E, and F regions. The F2 layer is the top most layer in the ionosphere and contains the highest number of free electrons, which are typically found at the attitude of about 400 km above the F1 region (about 150 to 220 km). The ion density peaks between 200 and 400 km and then slowly tapers off. Unlike the F1 layer, the F2 layer does not disappear at night and is widely studied for TEC estimation. The variations in electron density in the ionosphere can be observed by satellites, ground-based ionosondes, and networks of GPS receivers.25 The TEC measurements obtained through modeling of the data received by the dual frequency GPS receivers help us to investigate coupling of the Earth’s ionosphere and lithosphere during seismic activity. In this paper, the association of temporal and spatial variation of the ionospheric signatures prior to large earthquakes in the Himalaya (Fig. 1) was studied
Fig. 1 SRTM relief of the Himalayan mountain range and its surrounding areas showing the epicenter of earthquakes, geological features, IGS stations, and CORS analyzed in the present study. Journal of Applied Remote Sensing
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with the help of statistical analysis of GPS TEC data to identify possible pre-earthquake anomalies. The results indicate that the TEC fluctuated very significantly in the form of enhancements and depletions a few days prior to eight (8) high magnitude earthquakes considered for the study.
2 Regional Tectonic Setting of the Himalaya The Himalayan mountain belt stretches from Kashmir in the west to Arunachal Pradesh in the east and covers a distance of ∼2500 km. It is broadly divided into four units from south to north: the outer Himalaya bordering the Indogangetic plains in the south that comprises Siwalik sediments, the Lesser Himalaya, the Greater Himalaya with elevation reaching up to 6000 m, and the Tethyan Himalaya bordering a suite of ophiolite and melange to the north.26 The outer Himalaya comprises folded and faulted Siwalik molasse sediments, whereas the Lesser Himalaya comprises nonfossiliferous sediments and metasediments. The Greater Himalaya comprises the highest mountain ranges of the region and consists of crystalline rocks, and the Tethyan Himalaya comprises fossiliferous sediments of Precambrian to the Cretaceous ages. The significant east– west tectonic features separating these lithounits are the Main Frontal Thrust or the Himalayan Frontal Thrust defining the southern limit of Siwalik sediments, the Main Boundary Thrust (MBT) bordering the Lesser Himalayan rocks, and the Main Central Thrust (MCT) separating the Greater and Lesser Himalayan rocks. The general understanding is that initially active subduction occurred along the Indo-Tsangpo suture zone during the early Tertiary period, where the oceanic Indian lithosphere subducted beneath the Tibetan landmass. Subsequently, active subduction ceased with the collision of India and Asia during the Eocene, paving the way for under thrusting of the Indian subcontinent along major thrusts.27 Overall, around 300 to 500 km of the total convergence between India and Asia has occurred in the Himalayan region.28 The Himalayan arc is bound by complex syntaxial bends in the west and east. A complete overturn of the MBT and MCT characterized the western Himalayan syntaxis. The eastern syntaxis is one of the most complex and seismically active regions formed by the EW trending India–Eurasia plate and NS trending India–Burma plate margins. These syntaxes region have experienced large magnitude Himalayan earthquakes measuring 8.7 in 1897 and 8.6 in 1950.29 As the entire region starting from east to west is very prone to earthquakes, eight (8) important seismic events with magnitude varying from 6.1 to 7.8 Mw were analyzed in the present study.
3 Data and Analysis Method It has been observed that the ionosphere variability increases locally within the earthquake preparation zone a few days before the seismic event.30,31 This fact has been considered in the present study to estimate the variation in TEC in the ionosphere. The radius of the earthquake preparation zone computed is shown in Table 1. It varies from 1679 km for the Mw 7.5 Afghanistan earthquake in the west to 760 km for the Mw 6.7 Imphal earthquake in the east (Table 1). It was 1854 km for the Mw 7.6 Kashmir earthquake. The distances between the epicenter of these earthquakes and the Global Navigation Satellite System (GNSS) and International GNSS Service (IGS) continuous stations considered in the present analysis are shown in Table 1. The IGS stations are located in different parts of the world, and various data analysis centers distribute high-quality GNSS data and data products in near real time to meet the objectives of a wide range of scientific and engineering applications. In the present study, IGS orbit and bias data were used as input for correction strategy in GNSS data processing for TEC evaluation with respect to pre-earthquake anomalies. The RINEX files from the considered continuous GNSS stations were then processed to obtain TEC values using the program GPS-TEC.32–34 In our study, the RINEX GPS-TEC program version 2.0 was used. The STEC (slant range TEC) from a satellite to a receiver can be obtained from the difference between the pseudoranges (P1 and P2) of the two frequencies as per the following equation:35 Journal of Applied Remote Sensing
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6.1
6.7
January 03, 2016
6.7
April 26, 2015
September 21, 2009
7.8
April 25, 2015
7.3
7.6
October 08, 2005
May 12, 2015
6.6
7.5
April 10, 2016
October 26, 2015
Date of event (mm dd, yy)
Magnitude (Mw)
55
14
15
22.9
8.2
26
212
231
Depth (km)
24.829
27.332
27.808
27.771
28.147
34.539
36.472
36.479
Latitude (deg)
93.655
91.437
86.065
86.065
84.707
73.588
71.131
70.354
Longitude (deg)
Imphal
Bhutan
Nepal
Nepal
Nepal
Kashmir
Afghanistan
Afghanistan
Region
760
420
1377
760
2259
1854
689
1679
Radius of EQ prep zone (km)
835 1166
POL2 DELH
LHAZ
LHAZ
LHAZ
LHAZ
LHAZ
665
419
532
534
646
786
474
TASH KIT3
470
KIT3
1199
546
TASH
BADR
484
Distance from epicenter (km)
KIT3
Data used (IGS/ CORS)
5, 8, 11, 13
3, 4, 11, 14, 15
1, 3
5, 9
4, 8, 14
0, 5, 10, 12
5, 9, 13, 15, 16
5, 9, 13, 15,16
0 to 8, 16 to 17
0 to 8, 16 to 17
0 to 8, 16 to 17
0 to 8, 16 to 17
0 to 8, 16 to 17
Prominent precursor observed before (day)
Table 1 Details of the earthquakes analyzed and their associated effects on ionospheric TEC.
H
4, 14 to 15(H), 3, 11 (L)
L
9 (H), 5 (L)
8 (H), 14, 4 (L)
0, 6, 10 (H), 13 (L)
13 (H), 5, 9, 15 to 16 (L)
13 (H), 5, 9, 15-16 (L)
0 to 8 (H), 16 to 17 (L)
0 to 8 (H), 16 to 17 (L)
0 to 8 (H), 16 to 17 (L)
0 to 8 (H), 16 to 17 (L)
0 to 8 (H), 16 to 17 (L)
Effect on ionospheric TEC H = high, L = low
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sTEC ¼
EQ-TARGET;temp:intralink-;e001;116;735
2ðf1f2Þ2 ðP2 − P1Þ; Kðf12 − f22 Þ
(1)
where f1 and f2 are GPS signal frequencies at 1.57542 and 1.2276 GHz, respectively, and K ¼ 80.62 ðm3 ∕s2 Þ is a constant that relates the plasma frequency to the electron density. The program uses both pseudorange and carrier phase measurements to get group and phase TEC. The absolute TEC is then obtained from the combination of group and phase TEC values. In other words, to reduce the effect of pseudorange noise on TEC data, GPS pseudoranges are smoothed by the carrier phase measurement technique known as carrier phase leveling. sTEC refers to measurements of TEC along the slant ray paths between a satellite and a ground station. However, for precursor study, it is necessary to evaluate the variation in vertical direction or vTEC. Therefore, based on the assumption of thin-shell ionosphere at a fixed height, the slant measurement was converted to equivalent vTEC between the satellite and ground station.36 In practice, most estimates of TEC are represented by vTEC, in el∕m2 , which can be computed as per the following equation:37 vTEC ¼ sTEC × cos z 0 :
(2)
EQ-TARGET;temp:intralink-;e002;116;543
The zenith angle z 0 is expressed as follows: RE cos α z 0 ¼ arcsin ; RE þ h
(3)
EQ-TARGET;temp:intralink-;e003;116;499
where α is the elevation angle of the satellite, RE is the mean radius of the Earth, and h is the height of the ionospheric layer, which is 350 km in the present case. If bs and br are the estimated satellite and receiver biases, respectively, then vTEC can be obtained as follows: vTEC ¼ ðsTEC − bs − brÞ × cos z 0 :
(4)
EQ-TARGET;temp:intralink-;e004;116;418
The satellite and receiver biases files (p1p2 and p1c1) were obtained from the CODE analysis data center, University of Bern (AIUB), and the orbit files (Sp3) were obtained from IGS. The vTEC values computed for less than 20 deg elevation angles are more susceptible to multipath and tropospheric errors and, therefore, vTEC estimation is carried out for elevations of more than 20 deg by most TEC estimation techniques. The same was followed in the present case, and the vTEC value was calculated at every minute interval by averaging such values from all satellites. Alternatively, various approaches and software packages allow estimation of both regional and global TEC values using dual frequency signals from GNSS data.38–41 The Center for Orbit Determination in Europe (CODE) performed daily TEC analysis using the Bernese processing engine of the Bernese GNSS software. The parameters are estimated by executing a series of scripts through which precise point position solutions are computed and ionospheric models are generated.42 In addition to CODE, global TEC maps are also calculated at various research institutes that include Geodetic Survey Division of Natural Resources Canada, Jet Propulsion Laboratory of California Institute of Technology, and European Space Agency, France. For the identification of seismoionospheric signals, we examined the behavior of vTEC for 30 running days by a statistical method.14 The 15 days running median was computed to construct the upper and lower bounds as follows: Upper bound ¼ X þ 2σ;
(5)
Lower bound ¼ X − 2σ;
(6)
EQ-TARGET;temp:intralink-;e005;116;170
EQ-TARGET;temp:intralink-;e006;116;127
where X is 15-day median and σ is the standard deviation at a particular time period. vTEC values above the upper or below the lower bounds are considered anomalies. To smooth the vTEC values, 20 min running mean was applied on 1-min interval data and final TEC values were analyzed with respect to upper and lower bounds to detect the anomaly. The vTEC values Journal of Applied Remote Sensing
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obtained at 1-min intervals were analyzed with respect to geomagnetic storm data (obtained from NOAA) observed on the same day to establish the cause of the TEC variation.
4 Pre-Earthquake Ionospheric Anomalies and Analysis 4.1 2015 Afghanistan Earthquake The Mw 7.5 Afghanistan earthquake occurred at 09:09:42 UTC on October 26, 2015. We have tried to show seismoionospheric perturbations observed in the GPS-derived TEC variations before the earthquake. The GPS data from five GNSS (two CORS and three IGS) stations: Kit3, Pol2, Tash, Delh (Delhi), and Badr (Badrinath) were processed, and the TEC variations for 30 days were computed. The processed GPS data revealed strong positive anomalies from October 18 to 26, 2015, in all five stations’ data (Fig. 2). In addition, a positive anomaly was also observed on October 7 and 8, 2015. Very significant low anomalies were observed on October 9
(a)
(b)
(c)
(d)
(e)
(f)
Fig. 2 TEC values observed before, during, and after the October 26, 2015, Afghanistan earthquake. (a–e) The TEC observed in the month of October 2015, computed from the Pol2, Kit3, Tash, Badrinath and Delhi, respectively. (f) The geomagnetic Ap Index. The scale in the upper edge represents days to the event, whereas the lower edge (x -axis) represents the calendar days. Journal of Applied Remote Sensing
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and 10. The details of the earthquakes, data used, and associated earthquake precursory signatures are shown in Table 1. The observed anomalies may also be attributed to geomagnetic activities other than the seismic events. In the present case, comparison with global datasets available from National Oceanic and Atmospheric Administration (NOAA) revealed that the anomalies observed on October 7 and 8, 2015, were attributed to geomagnetic activity as the Ap index was 77 and 56. The classification of a geomagnetic storm adopted in the present analysis is as per NOAA classification.43 It is considered as a storm when Ap > 29, a minor storm when 29 < Ap < 50, a major storm when 50 ≤ Ap < 100, and a severe storm when Ap ≥ 100. For the remaining days, there was no storm. Hence, consistent anomalies as observed during October 9 to 10 and continuously from October 18 to 26, 2015, prior to the earthquake are attributed to seismogenic ionospheric perturbations detected by GNSS (GPS) receivers.
4.2 2016 Afghanistan Earthquake The epicenter of the 2016 Afghanistan earthquake, which occurred at 10:28:58 UTC on April 10, 2016 (USGS), was located at 36.472°N and 71.131°E with a focal depth of 212 km. The TEC
(a)
(b)
(c)
(d)
(e)
Fig. 3 TEC variation observed before, during, and after the seismic event of April 10, 2016, Afghanistan earthquake at: (a) IGS station Kit3; (b) IGS station Tash; (c) geomagnetic Ap Index observed during the same period as of TEC observation; (d) TEC variation for the October 8, 2005, Kashmir earthquake as observed at IGS station Kit3; and (e) geomagnetic Ap Index observed during the same time period. The scale in the upper edge represents days to the event, whereas the lower edge (x -axis) represents the calendar days. Journal of Applied Remote Sensing
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analysis was carried out using data from the IGS stations Kit3 and Tash, which are located very close to each other at the distance of 470 and 474 km, respectively, from the epicenter, well within the earthquake preparation zone with a radius of 687 km (Fig. 1). A prominent positive anomaly was observed on March 28 and a negative anomaly on April 1 and 5, 2016, prior to the earthquake on April 10, 2016 [Figs. 3(a) and 3(b)]. The geomagnetic condition during the entire month was quiet except on March 17, 2016, which could have caused an increase in the electron content [Fig. 3(c)]. The effect of the small magnitude earthquake that occurred on April 24, 2016 (Mw 5.1), also had precursor anomalies, as observed on April 11, 13, and 14, 2016. The Mw 7.6 earthquake occurred on October 8, 2005, at 03:50:39 UTC in Kashmir region, India–Pakistan border. GPS-derived TEC variations were studied using the IGS data from the station Kit3 located at the distance of 786 km from the epicenter, well within the earthquake preparation zone with a radius of 1854 km. The vTEC plots reveal strong anomalies on September 25, 27, October 2, and 8, 2005 (6 days before the event and on the day of the event, respectively). These variations are observed as a pre-earthquake ionospheric signature for the earthquake that took place on October 8, 2005, as there is an abnormal decrease and increase in the GPS TEC value prior to the event.44,45 The main shock of October 8, 2005, was followed by 42 aftershocks of magnitudes varying from 5 to 6.4 Mw (USGS Earth catalogue). The precursor signature of these aftershocks was also observed as anomalies on October 13, 16, and 17, 2005, which was followed by 5 and 5.6 magnitude earthquakes on October 18 and 19, 2005, respectively. These anomalies were analyzed in the light of geomagnetic storm data from NOAA [Figs. 3(d) and 3(e)]; however, no such activities were observed.
4.3 2015 Nepal Earthquakes The April 25, 2015, Nepal earthquake of Mw 7.8 occurred at 06:11 UTC, 34 km ESE of Lamjung, Nepal. The epicenter was located at 28.1473°N and 84.7079°E. The initial shock was accompanied by two large aftershocks of magnitudes Mw 6.6 on April 25, 2015 (06:45 UTC), which shook the entire region within 1 h of the main shock, and Mw 6.7, which occurred the next day, April 26, 2015, at 09:10 UTC. The second earthquake, analyzed in the present study, occurred on May 12, 2015, at 07:05 UTC. Attempts have been made to analyze the TEC variation prior to the Mw 7.8 Nepal earthquake 2015 from GNSS (GPS) data observed at a distance of 646 km from the epicenter, within the earthquake preparatory zone. GPS data for the period April 10 to May 14, 2015, from IGS station Lhaz (Lhasa) were processed to derive TEC variations. Anomalous very high TEC values were observed on April 16 and 17 and high values on April 23 and 24, 2015, prior to the main shock on April 25, 2015. Anomalous low values were observed on April 11 and 21 and on May 9 and 11, prior to an earthquake on May 12, 2015 [Fig. 4(a)]. In addition to this, negative anomalies were also observed from April 30 to May 4, 2015, which could be the precursory signature of the Mw 4.9 earthquakes that occurred on May 2 and 8, 2015. The anomaly observed on May 14, 2015, could be the signature of the Mw 5.5 earthquake on May 16, 2015. These anomalies were also checked against the geomagnetic activities and solar flare data available from NOAA, and it was confirmed that these anomalies were not affected by such phenomena except on April 16, 2015 [Fig. 4(b)].
4.4 2009 Bhutan Earthquake The Mw 6.1 Bhutan earthquake occurred on September 21, 2009, at 08:53:05 UTC. The epicenter was located at 27.332°N and 91.437°E, and the focal depth was 14 km. GPS data from the nearest IGS station Lhaz (Lhasa) for the month of September 2009 were analyzed to study the precursor signal [Fig. 4(c)]. During this time period, TEC was not influenced by a magnetic storm as revealed from the Ap index [Fig. 4(d)]. As the station lies in one of the most seismically active regions of the world, smaller magnitude earthquakes may influence the TEC perturbations. In addition to the smaller perturbations, high anomalies were observed on November 6, 7, and 17 and low anomalies on November 10 and 18, 2009, prior to the event. Journal of Applied Remote Sensing
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(a)
(b)
(c)
(d)
(e)
(f)
Fig. 4 TEC variation observed before, during, and after the event of: (a) 2015 Nepal earthquake at IGS station Lhaz; (b) geomagnetic Ap Index observed during the same time; (c) TEC variation for the September 21, 2015, Bhutan earthquake observed at IGS station Lhaz; (f) geomagnetic Ap index observed during the same period; (e) TEC variation for the January 3, 2016, Imphal earthquake observed at IGS station Lhaz; and (g) geomagnetic Ap Index observed during the same period for the Imphal earthquake. The scale in the upper edge represents days to the event, whereas the lower edge (x -axis) represents the calendar days.
4.5 2016 Imphal Earthquake The event of Mw 6.7 occurred at 23:05:22 UTC on January 3, 2016. The epicenter was located at 24.804°N and 93.650°E with focal depth of 55 km. Attempts have been made to analyze the TEC variation prior to this event from GNSS (GPS) data observed at a distance of 665 km from the epicenter, lying within the earthquake preparatory zone. GPS data for the period December 15, 2015, to January 14, 2016, acquired from the IGS station Lhaz (Lhasa) were analyzed to derive TEC variations [Fig. 4(e)]. Prominent positive anomalies were observed on December 20 to 22, 25 and 28, 2015, prior to the event. On December 21, 2015, the geomagnetic index (Ap index) was 38 units [Fig. 4(f)]; hence, it was considered a geomagnetically disturbed day. Positive anomalies were also observed on January 12 and 13, 2016, which could be attributed to minor events in China (Mw 4.4 and Mw 5.2) that occurred on January 13 and 14, 2016, respectively (USGS earthquake catalogue). Therefore, the anomalies observed on December 20, 22, Journal of Applied Remote Sensing
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25, and 28, 2015, were attributed to the seismogenic effect of the January 3, 2016, Imphal earthquake.
5 Discussion and Conclusion To better understand seismic precursor in the Himalayan region, an attempt was made to analyze TEC variations observed by GNSS receivers in continuous operating mode, located in different parts of the Himalaya. TEC variations preceding the Afghanistan earthquake (7.5 Mw) on October 26, 2015, started almost 24 days prior to the event. However, the prominent negative anomaly was observed on October 9 to 10, i.e., 16 to 17 days prior to the event and positive anomalies were observed continuously from 0 to 8 days prior to the event as inferred from five sets of GNSS data (Fig. 2). All datasets show similar patterns in TEC variation with correlation coefficients of 0.99, 0.97, 0.95, and 0.94 between the Kit3/Tash, Kit3/Pol2, Kit3/Delhi, and Kit3/ Badrinath stations, respectively. A geomagnetic storm also affected the electron concentration on October 7 and 8. Therefore, it is inferred that TEC variations observed during 1 to 17 days prior to the event could be seismogenic in nature. In the case of the April 10, 2016, Afghanistan earthquake, results from two IGS stations (Kit3 and Tash) lying very close to each other (470 and 474 km from the epicenter) within the earthquake preparation zone show negative anomalies on March 25 and 26 (15 to 16 days before), April 1 (9 days before), and April 5, 2016 (5 days before) and a prominent positive anomaly on March 28, prior to its occurrence [Figs. 3(a) and 3(b)]. The data from two IGS stations were analyzed to assess consistency of observation, and it showed correlation of 0.97, suggesting very good consistency in the observation. In addition to the major shock on April 10, there was a minor earthquake of Mw 5.1 on April 24, 2016, which could have reduced the electron density on April 11 and increased the same on April 13 and 14, as shown in [Figs. 3(a) and 3(b)]. Therefore, the pre-earthquake perturbation of the Mw 6.6 earthquake was clearly observed 5 to 16 days prior to its occurrence by both the IGS stations. Analysis of the Mw 7.6 Kashmir earthquake showed anomalies 6 days before the event and on the day of the event. There were 42 aftershocks of magnitude 5 to 6.4 (USGS earthquake catalogue) following this event, and the precursor signature of these aftershocks was also detected. The most prominent TEC variation was observed on the day of the event in addition to other consistent anomalies during the 6 to 13 days prior to the event, as listed in Table 1 and shown in Fig. 3(d). There were no geomagnetic storms during the study period; thus, we infer that these anomalies could be considered ionospheric precursors related to the earthquake. The major Nepal earthquakes on April 25 and 26, 2015, were preceded by TEC anomalies as observed from station (Lhaz) at a distance of 646 km from the epicenter. The most prominent consistent positive anomaly was observed 8 days and negative anomalies 4 and 14 days prior to the event on April 25 (Mw 7.8) and low values on 1 and 3 days prior to an earthquake on May 12, 2015 [Fig. 4(a)]. The earliest signature observed on April 16, 2015, is attributed to geomagnetic disturbances. It is to be noted that the main shock on April 25, 2015, was followed by 88 aftershocks of magnitude greater than Mw 4.51 within the April 25 to May 14 window including the Mw 6.7 and 7.3 event of April 26 and May 12, respectively. Some of these minor aftershocks were also found to be preceded by anomalous TEC behavior, as shown in Fig. 4(a). The low TEC value observed on May 14 could be the precursory signature for the earthquake on May 16, 2015, of Mw 5.5, which occurred at 11:34:09 UTC, at a focal depth of 7 km.1 Therefore, TEC variations observed on April 11, 17 and 21, 2015, prior to April 25 earthquake are attributed to seismic causes for the ionospheric perturbations as measured by GNSS/CORS. Anomalous low values on May 9 and 11 are also attributed to the same cause for the Mw 7.3 earthquakes on May 12, 2015. The Mw 6.1 Bhutan earthquake of 21.9.2009 analyzed with GPS data from the station at the distance of 419 km from the epicenter shows prominent anomalies on 3, 4, 11, 14, and 15 days prior to its occurrence. The TEC values on September 10 (11 days prior) and September 18 (3 days prior) were found to be depleted, whereas the same was found to be increased on September 6 and September 7 (14 to 15 days prior) 2009. Hence, the pre-earthquake anomaly was observed clearly during 3 to 15 days prior to its occurrence. The electron concentration variation was Journal of Applied Remote Sensing
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observed 5 to 13 days prior to the Mw 6.7, Imphal earthquake on January 3, 2016. The anomaly on December 21 mentioned earlier was observed to be influenced by a geomagnetic storm reflected by a higher Ap Index. In addition to the anomalies observed prior to the event, high TEC values were also observed on January 12 and 13, 2016. Observation of data from USGS reveals that this could be due to the Mw 4.4 and Mw 5.2 events of China, which took place on January 13 and 14, 2016. For all of the events analyzed in the present study, consistent increases and decreases in electron concentration values were observed 0 to 17 days prior to the event, with an average of 3 to 14 days. All of the earthquakes had at least one low anomaly and a number of positive anomalies except for the 6.7 Mw Imphal earthquake, which did not show any significant negative anomaly during 18 days prior to the event. As per the hypothesis postulated by Friedemann et al.,11 at least one negative anomaly is expected prior to an earthquake followed/preceded by a couple of high anomalies. This was observed for most of the considered earthquakes. The TEC fluctuations during the geomagnetically disturbed period were studied thoroughly, and those days were excluded; the remaining TEC variations as observed were attributed to seismogenic causes. The computed TEC values were validated comparing it with the nearest available dataset from global ionosphere map (GIM), which shows a correlation of 0.9 and 0.8 for Kit3 and Badrinath, respectively (Fig. 5). The total TEC deviation (exceeding upper and lower bounds) varies from 6 to 13 TECU for the Himalayan earthquakes with a magnitude greater than Mw 6.0. In general, anomalous TEC values were found to be decreasing with the increase of hypocenter depth, as shown in Table 2 and Fig. 6. In general, there is an overall increase of magnitude of the earthquakes with TEC fluctuations; however, the relationship is not very clear as the datasets are very limited for a meaningful statistical analysis. It is to be noted that the TEC variation could also be mixed by minor earthquakes of magnitudes greater than Mw 4.5 as the study area is seismotectonically very active. However, to monitor such events and to have a better understanding of such anomalous TEC variations and their correlation with seismic events, the location of the IGS/CORS should be very close to the epicenter. The present locations of IGS/CORS stations are too sparse in the Himalaya, and, therefore, a closer network of monitoring stations is required for better correlation and improved precursor analysis. The study has revealed that significant TEC variations (6 to 13 TEC units) can be considered as precursors to damaging earthquakes, whereas low TEC variations could occur before smaller magnitude earthquakes. These subtle changes are recorded by nearby GNSS receivers and can be considered for testing, evaluation, and general characterization of seismotectonics and ionospheric coupling. Overall, the smaller events can be used for optimization and automation of methodology, which would help immensely during precursor analysis of major events. Therefore, it is important to monitor continuously TEC using GNSS receivers, and, whenever there is anomalous low value, it should be monitored very carefully for the subsequent three weeks for detecting further anomalies, which could be high or low. In cases in which the TEC
(a)
(b)
Fig. 5 Comparison of TEC derived from global ionospheric map (GIM) and computed at IGS and COR stations. (a) TEC computed at IGS station Kit3 verses TEC derived from GIM and (b) TEC computed at COR station Badrinath (BADR) verses TEC derived from GIM. (Source: Center for Orbit Determination in Europe-CODE) Journal of Applied Remote Sensing
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Table 2 Average anomalous TEC variations with magnitude and hypocenter depth of the earthquakes. Average anomalous TEC (TECU) Magnitude (Mw)
Depth of hypocenter (km)
Exceeding upper bound
Exceeding lower bound
Total TEC deviation (TECU)
April 25, 2015, Nepal
7.8
8.2
9
2
11
September 21, 2009, Bhutan
6.1
14
10
2
12
May 12, 2015, Nepal
7.3
15
2
11
13
April 26, 2015, Nepal
6.7
22.9
9
2
11
October 8, 2005, Kashmir
7.6
26
6
3
9
January 3, 2016, Imphal
6.7
55
6
0
6
April 10, 2015, Afghanistan
6.6
212
5
3
8
October 26, 2015, Afghanistan
7.5
231
5
3
8
Earthquake
(a)
(b)
Fig. 6 TEC deviation versus earthquake magnitude and hypocenter depth. (a) Total TEC variation (exceeding upper bound and lower bound) with magnitude of the earthquakes. (b) Total TEC variation (exceeding upper bound and lower bound) with hypocenter depth of the earthquakes.
anomaly is high, one must evaluate it against the magnetic storm or solar flare. Simultaneously, the TEC monitoring at different locations should be cross-checked against global GIM data for spatial detection of anomaly. Finally, the anomalies should be considered as confirmed precursors. However, the challenge lies in data accessibility from ground stations (CORS/GNSS) and their analysis in real time, at least on a daily basis. The most significant aspect of the study is that large anomalies are associated with bigger events and anomalous TEC low values are free from magnetic storms or solar flares and, therefore, can be detected as confirmed precursors, which is very promising for earthquake precursor studies in real time.
Acknowledgments This work is a part of a project titled “Geodynamics and Seismicity Investigation in Western Himalaya.” Financial support received from Indian Institute of Remote Sensing, Indian Space Research Organization, Department of Space, Government of India is gratefully acknowledged. We thank Dr. A. Senthil Kumar, director of Indian Institute of Remote Sensing (IIRS), and Prof. A. K. Gupta, director of Wadia Institute of Himalayan Geology (WIHG) for providing all support. We thank Dr. Gopi Seemala, Indian Institute of Geomagnetism, for providing the latest Journal of Applied Remote Sensing
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version of GPS-TEC software for TEC computation. We also thank International GNSS Services (IGS) and National Oceanic and Atmospheric Administration (NOAA) for freely providing high quality data for our analysis. We are also thankful to the anonymous reviewers and editors for valuable suggestions to improve the manuscript, and Dr. S. L. Chattoraj for contributions in editing.
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Gopal Sharma is a senior research scholar at Indian Institute of Remote Sensing, Dehradun, working in an ISRO funded project “Geodynamics and Seismicity Investigation in Western Himalaya.” He is pursuing his PhD from Indian Institute of Technology (Indian School of Mines), Dhanbad. His research interests include earthquakes precursors, crustal deformation, and active tectonics. Prashant Kumar Champati ray, with a postgraduate degree and PhD from IIT Bombay, India, in the field of applied geology and remote sensing, is actively involved in research, education, and training in the field of application of remote sensing and GIS in geosciences. His research interests include monitoring and modeling of landslides, active fault mapping, seismic hazard assessment, geodynamics, mineral exploration, and planetary geology. Sarada Mohanty is a professor of structural geology. He obtained his MSc (applied geology) degree and PhD from Indian Institute of Technology, Kharagpur, and carried out his postdoctoral research at ETH, Zurich, Switzerland. His teaching and research interests include structural geology and tectonics. Currently, his research is concentrated on the application of GIS and geophysical data for understanding neotectonics of the Kachchh region, India. Param Kirti Rao Gautam is a scientist at Wadia Institute of Himalayan Geology, Dehradun. He received his PhD in earth sciences and MTech in applied geophysics from Indian Institute of Technology, Roorkee. His current research interest includes GPS seismology, crustal deformation, and geodesy. Suresh Kannaujiya is a scientist at Indian Institute of Remote sensing, Dehradun. He received his MTech in geophysics from Indian Institute of Technology, Roorkee. His current research interest includes GNSS applications on crustal stress, strain, deformation analysis, and geophysical surveys.
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