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Chapter 5

Crustal deformation study using GPS

measurement and to find correlation with

capacity fractal dimension of structural

elements.

5.1 Introduction

The satellite-based geodetic technique of GPS (Global Positioning System),

developed by the US Department of Defence for military and civilian navigation and

positioining, has now become the geodetic method for various study of geophysical

phenomena. The development of GPS in its accuracy to millimeter level has opened a

new way of studying earthquakes mechanisms.The geodetic method of GPS has

gained world-wide acceptance for monitoring crustal dynamics for earthquakes. As

the earthquake is the sudden release of energy which may have accumulated for long

time in past due to motion of tectonic plates. The study of strain energy development

in a region of seismically active is of prime importance for understanding the

earthquakes dymanics. The measurement of both the long-term rate of deformation

and the short-term deformation along the individual fault will give more exact

information regarding strain development process and hence the preparation of future

earthquakes. Though the strainmeter can provide far greater strain sensitivity than

does GPS, but GPS offer the spatial coverage and long-term stability for study. So

the importance of GPS measurement is much more than the strainmeter. The process

of development of stress and strain may be well studied with the contiunous GPS

measurements. The crustal deformation study using GPS is very much in need to

know the changes in millimeter level, which further used for estimation of strain in a

region. In earthquake prone area, GPS will be playing an important role in helping

seismologist to anticipate earthquakes. The capability of GPS measurement to provide

the precise position information has helped in determination of strain builds up slowly

over time. The study of the seismicity of a region and the GPS measurement can lead

to better understanding of the mechanics behind earthquakes and hence better

assessemnt of earthquake hazards zone. The crustal deformation study derived by

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GPS (Global Positioning System) measurements is the need of the present study and a

source of vast information in the field of earthquakes assessments.

GPS observations have been widely used for monitoring crustal deformation,

landslide, and ground subsidence (Sagiya et al. 2000; Leick 2004; Kaniuth and Vetter

2005; Wu et al. 2006; Copley, 2008; Akilan et al., 2012).The application of GPS

measurement for vertical and horizontal motion of tectonic plates have been

attempted by several researchers to show the crustal deformation globally (Bilham et

al, 1997; Chen et al., 2000; Banerjee and, Burgmann (2002) Bettinelli et al., 2006

;Reddy and Prajapati.,2008;Mukul et al, 2010 ). Moreover, importance of campaign

and continuous mode GPS study for crustal deformation has been investigated by

several researchers (Jouanne, et. al., 1999; Banerjee & Burgmann, 2002; Gahalaut, et

al. 2006; Banerjee et al., 2008 ).The estimation made by various scientists from

world over (Paul et al. 2001; Jade 2004; Altamimi et al. 2007; Banerjee et al. 2008)

showed that India moves northeast at the rate of 50–55 mm/year. Further it is

observed that about half of this motion being accommodated by convergence across

the thrust and fold belts of the Himalaya and the remaining convergence is taken up

further north in Tibet and central Asia (Peltzer and Saucier, 1996). The study in the

Kumaun Himalaya gives in Holocene shortening rates of value greater than 12 mm/yr

(Wesnousky et al., 1999). Recently GPS measurement made by Ponraj, et al., (2010)

demarcated the convergence rate in the Kumaun Himalaya is about 15 mm/yr. The

occurrence of past earthquakes has provided us with the vital information on the

ongoing crustal deformation in the Himalayas (Valdiya et al., 1984, 1992; Powers et

al., 1998 Kumar et al., 2001).

The Kumaun region and its adjacent of the active Himalayan orogeny which is

having serious slip deficit for many decades are in need of integrated approach to

reveal the seismogenesis. The Himalayan fold-thrust belt has developed since ~55 Ma

in response to the collision of the Indian and Eurasian plates that result in the

northward subduction of India (Powell and Conaghan, 1973; Searle, 1991; Hodges,

2000). The ongoing northward convergence of India produces active deformation in

the Himalaya, Tibet and adjoining areas, keeping the entire region seismically active.

The Himalayan orogenic belt comprises of principal tectonic zones of the Outer

Himalaya, Lesser Himalaya, Higher Himalaya and Tethys Himalaya. These tectonic

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units are divided by major fault systems including Indus-Tsangpo Suture Zone (ITSZ)

in the north, Main Central Thrust (MCT), Main Boundary Thrust (MBT) and

Himalayan Frontal Fault (HFF) in the south. The convergence of India was

accommodated as a result of shortening largely because of these major thrusts in

Himalaya. These principal thrusts continue longitudinally east to west along the entire

length of Himalaya. The Indus Suture Thrust (IST) to the north represents the junction

of the two colliding continents, and it is suggested that the HFF is now the primary

surface expression of shortening between the Himalaya and the Indian plate (Powers

et al. 1998). The seismicity is basically observed along the MCT, MBT zones and a

number of thrust faults/ transverse faults/ lineaments present in the region (Ni and

Barazangi, 1984). The central gap region of the Himalayas lies in the northern part of

the Indian subcontinent and falls under the highest zone V of seismic zoning map of

India which is the most seismically hazardous zone. The study of the region is of great

importance because of the presence of seismic gap (Khattri, 1987) using GPS based

measurement to have understanding the future large events estimation.

In this chapter of crustal deformation study for the Kumaun Himalayan region,

both campaign mode sites and continuous GPS sites have been utilized for the

measurement of strain rate. The process of earthquakes occurring may be well

understood with the knowledge of the strain energy distribution. The best method of

getting the information about the strain rate is by the installation of GPS station in

various fault system in the study region. Dense GPS networks of nine campaign

station were installed. Data acquisitions for three years were performed, which is

needed for the processing leading to rate of motion of GPS station sites. The

horizontal velocity vectors of sites have been estimated and hence the strain rate in the

study region. The results of the GPS measurements has been attempted to study the

correlation with the seismicity and fractal correlation dimension of fault system,

studied in chapter 3 and chapter 4. The study of the parameter taken finds good

results for identification of a block of 1°×1° as a hazardous zone.

5.2 GPS data analysis

In order to get the crustal movement, a dense GPS network of nine campaign

sites were selected in the Kumaun parts of Himalaya. The installation of GPS stations

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in various parts of study region in Uttarakhand and adjacent region are Haldwani

(jagh), Tanakpur (prbh), Almora (saih), Bageshwar (vivh), Pithoragarh (utnh and

jals), Champawat (cmoh), Dharchula (selh) and Jhulaghat (prmh). All the campaign

stations were occupied and collected data with Leica dual-frequency GNSS receiver

model GRX-1200 GG for two to three continuous days. GPS observations were

recorded at a 30s sampling rate using a cut-off elevation angle of 15º to reduce

multipath effects and tropospheric errors. Each campaign site was occupied for data

collection twice in a year, at an interval of six month from 2008 to 2011. In addition

to 9 campaign sites, data of five continuous stations viz. Dehradun (dehr), Badrinath

(badr), Almora (gbnl and gbpk) and Munsiari (muns) were also taken from Survey of

India for processing and analysis. The location map of the campaign mode sites are

shown in Fig.5.1. The arrangement of tripod is shown for the site Champawat in

Fig.5.2. Similarly the data acquisitions for all other sites were performed. The

necessary and important steps followed in the processing of GPS data with

GAMIT/GLOBK shell scripts are presented in appendix-I.

The static data collected at campaign and continuous sites were processed with

the GAMIT, version 10.4 (Herring et al., 2010a, b), to estimate the time series of site

coordinates and their velocities, developed at Massachusetts Institute of Technology

(MIT), USA. GPS data collected at all the above sites has been converted into rinex

(Receiver Independent Exchange format) observation files for GAMIT run. The

GAMIT part were used to process the GPS observation data for session solutions

while the GLOBK part were used with Kalman filter to get the final result. In order

to estimate the site coordinates and velocities with reference to ITRF05 reference

frame (Altamimi et al., 2007) daily loosely constrained solutions were combined with

the permanent tracking solutions of IGS stations. The GPS data were processed

precisely by considering following factors/parameters: IGS precise orbits, IGS Earth

orientation parameters, satellite clock error files and data from eight IGS stations

(BJFS, GUAO, HYDE, IISC, IRKT, POL2, LHAS, and KIT3) have been used. We

analyzed all the geodetic data judiciously for the velocity vectors and strains

measurement of the network.

The time series of each station were used for computing the velocity vectors

by least square estimation of a linear trend. Further for the continuous GPS stations

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(dehr, gbpk and muns) a more deeply analysis has been performed taking into

account of periodic signals present in the GPS data and the time correlation between

the daily coordinates (Cannizzaro, 2008).

Fig. 5.1 The location of campaign mode GPS stations is shown. The sites are marked

here by the symbol “+” in red colour. Here double “+” symbol depict two

stations at Pithoragarh district and Champawat is one of important sites in

Kumaun Himalayan region.

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Fig.5.2. The photograph shows the arrangement of GPS tripod with antenna at site

Champawat.

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5.3 Results and discussion

The processing of the field data set (campaign) and few sites (continuous)

from Survey of India helped in the plot of time series and hence the vector velocity of

each site. The time series plot for continuous and a campaign sites have been shown

to indicate the trend of position coordinates with our data set. The time series of some

of the important sites are shown (Fig.5.3, Fig.5.4, Fig.5.5, Fig.5.6, Fig.5.7, Fig.5.8,

Fig.5.9, Fig.5.10, Fig.5.11 and Fig.5.12,). Similarly the results for velocity vectors of

all the sites with error, so obtained are listed in Table. 5.1. In Table 5.1, V_N, V_E

and V_U are north, east and up components of the relative velocity, whereas σ_N,

σ_E and σ_U are corresponding standard deviations. Fig.5.3 shows the time series of

the continuous station dehr. Similarly Fig.5.4, Fig.5.5 and Fig.5.6 are the plots of

time series of the continuous station gbpk, muns and badr respectively. Fig.5.7,

Fig.5.8, Fig.5.9, Fig.5.10, Fig.5.11 and Fig.5.12 are the time series plots of campaign

mode stations viz jals, jagh, utnh, saih, vivh and cmoh respectively with their

uncertainty. The plots of time series clearly depict the uncertainty in three

components. The uncertainty in north and east components of both campaign and

continuous sites are observed less in comparison to vertical components. Fig.5.13 and

Table.5.1 depict the horizontal velocity vector obtained in ITRF05 indicate that the

magnitude of the horizontal velocity are in the range of 35-50 mm/yr with error

ellipse at 95% confidence level. The plot of velocity vectors for horizontal rate of

motion of campaign and continuous sites are towards the northeastern direction in the

study region. Similar vector motion of different sites in India and Himalayan arc has

been also observed by several researchers (Jade, 2004; Jade, et.al. 2007; Banerjee et.al

2008; Jade et. al., 2011.). The velocity distribution shows that all the sites are moving

parallel to one another but slight deviation in direction are present in dehr, gbnl and

prbh sites. The overlapping of two vector arrows is due to the closeness of two sites

utnh and jals plotted in Fig.5.13. Moreover this also suggests us about the accuracy in

the measurement and processing of our data set. The velocity vectors of each station

are accompanied with the error ellipses. The site selh has a strange behavior in the

vertical velocity not in horizontal. In the Kumaun part of Himalayan region the

principal strains are large and dominated by NW-SE extension and NE-SE

compression. This shows that the deformation pattern of this region is characterized

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by a complex distribution of compressional and tensional strain (Fig. 5.14). The rate

of Indian plate motion relative to Eurasian plate is calculated by using the values

reported in (DeMets et.al. 2010) and is shown in (Fig. 5.15) and Table.5.1.The strain-

rate value has been computed by least-square estimation using the horizontal site

velocities of the stations in the area bounded by 78°E ≤ long ≤ 81°E and 28.5°N ≤ lat

≤ 30.5°N. In agreement to (Ponraj et al., 2010), the principal axes show a prominent

horizontal compression oriented NE-SW of magnitude of (-1.28 ± 0.08)×10-7 strain/yr

and a lower distension of (0.45 ± 0.20)×10-7 strain/yr in the perpendicular direction.

The vertical motion of sites has also been estimated using the field data set and is

shown in Fig.5.16.The up and down vertical components of different sites show

different level of motion depending upon the site location and the error values

associated with them as depicted in Table 5.1. The motion of vertical component of

some sites (dehr, muns,saih,vivh) is very less. Some sites which look nearby are about

more than 35km apart and having different geological setting. Hence up and down

vertical component are also visible. Two such pair of sites showing the features are

prbh,cmoh and jagh,gbnl. The prbh (Tanakpur) site is located in front of Main

Boundary Thrust sheet where Sarda Depression lies south of Tanakpur. On the other

hand,the site cmoh (Champawat) is located between MBT and MCT (Main Central

Thrust). Similarly the site jagh (Haldwani) is located in front of MBT and gbnl

(Almora) is lying between MBT and MCT. Thus upward and downward motion of

vertical components is associated with different geological factors.

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Fig.5.3 Time series of a continuous GPS station (dehr) are plotted from top to bottom

with north, east and up coordinate components respectively, with the

estimated velocities and uncertainty. The continuous lines represent the

interpolation model.

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Fig.5.4 Time series of a continuous GPS station (gbpk) are plotted from top to bottom

with north, east and up coordinate components respectively, with the

estimated velocities and uncertainty. The continuous lines represent the

interpolation model.

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Fig.5.5 Time series of a continuous GPS station (muns) are plotted from top to bottom

with north, east and up coordinate components respectively, with the

estimated velocities and uncertainty. The continuous lines represent the

interpolation model.

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Fig.5.6 Time series of a continuous GPS station (badr) are plotted from top to bottom

with north, east and up coordinate components respectively, with the

estimated velocities and uncertainty. The continuous lines represent the

interpolation model.

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Fig.5.7 Time series of a campaign GPS station (jals) are plotted from top to bottom

with north, east and up coordinate components respectively, with the

estimated velocities and uncertainty. The continuous lines represent the

interpolation model.

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Fig.5.8 Time series of a campaign GPS station (jagh) are plotted from top to bottom

with north, east and up coordinate components respectively, with the

estimated velocities and uncertainty. The continuous lines represent the

interpolation model.

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Fig.5.9 Time series of a campaign GPS station (utnh) are plotted from top to bottom

with north, east and up coordinate components respectively, with the

estimated velocities and uncertainty. The continuous lines represent the

interpolation model.

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Fig.5.10 Time series of a campaign GPS station (saih) are plotted from top to bottom

with north, east and up coordinate components respectively, with the

estimated velocities and uncertainty. The continuous lines represent the

interpolation model.

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Fig.5.11 Time series of a campaign GPS station (vivh) are plotted from top to bottom

with north, east and up coordinate components respectively, with the

estimated velocities and uncertainty. The continuous lines represent the

interpolation model.

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Fig.5.12 Time series of a campaign GPS station (cmoh) are plotted from top to

bottom with north, east and up coordinate components respectively, with

the estimated velocities and uncertainty. The continuous lines represent the

interpolation model.

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Fig.5.13 The observed estimated site velocities of GPS stations are plotted as black

arrows in ITRF05 reference frame for the period from 2008 to 2011 with

error ellipse at 95% confidence level.

utnh jals prmh

gbpk saih

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Fig.5.14 The estimated strain-rate principal axes obtained by the velocities of the GPS

stations are plotted for the period from 2008 to 2011.

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Fig.5.15 The calculated estimation of sites’ relative velocities with respect to Eurasian

plate is plotted as black arrows for the period from 2008 to 2011, with error

ellipse at 95% confidence level.

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Fig.5.16 The vertical velocities of the sites with the data set acquired (three years) is

depicted here.

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Table.5.1 The estimated velocities in the ITRF05 Reference Frame of campaign and

continuous sites are given. Here V_N, V_E and V_U are north, east and up

components of the absolute velocity, whereas σ_N, σ_E and σ_U are

corresponding standard deviations. The site velocities are given in mm/yr

for north, east and up components with 1σ uncertainty. The rate of Indian

plate motion relative to Eurasian plate is calculated by using the values

reported in (De Mets et.al. 2010).

site

code

Latitude

Longitude

V_N

V_E

V_U

σN

σ_E

σ _U

V_N V_E

σ_N

σ_E

badr 30.74292 79.49346 22.0 40.3 21.90 1.92 1.33 6.5 -12.5 32.0 1.92 1.33

cmoh 29.33188 80.08257 24.7 35.5 -27.70 4.13 3.78 10.8 -9.9 26.6 4.13 3.78

dehr 30.32455 78.05465 35.5 32.1 -0.65 0.26 0.20 0.82 1.2 24.1 0.26 0.20

gbnl 29.39237 79.44701 54.1 41.4 143.60 8.22 6.76 39.5 19.6 32.7 8.22 6.76

gbpk 29.63796 79.62031 41.5 34.8 -11.20 0.24 0.18 0.67 6.9 26.1 0.24 0.18

jagh 29.21523 79.53223 37.1 35.9 -14.80 3.79 2.12 22.6 2.5 27.1 3.79 2.12

jals 29.57899 80.21324 36.1 35.4 10.70 3.75 1.31 23.1 1.4 26.5 3.75 1.31

muns 30.06049 80.24037 28.9 32.6 1.30 0.13 0.17 0.82 -5.8 23.9 0.13 0.17

prbh 29.07246 80.11280 37.60 32.20 24.90 3.39 2.76 4.04 2.9 23.2 3.39 2.76

prmh 29.57248 80.38568 32.00 33.10 11.03 0.47 1.21 5.47 -2.7 24.2 0.47 1.21

saih 29.59963 79.66038 40.40 36.60 2.70 2.67 1.22 4.34 5.8 27.9 2.67 1.22

selh 29.84760 80.53707 28.50 29.60 -59.80 1.16 0.79 106.0-6.2 20.7 1.16 0.79

utnh 29.58360 80.21181 35.60 42.90 8.71 1.71 2.36 5.69 0.9 34.0 1.71 2.36

vivh 29.83393 79.77153 31.10 38.50 2.33 1.29 3.03 2.89 -3.5 29.8 1.29 3.03

Velocities in ITRF05 Velocities relative to

Eurasian Plate

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5.4 Important correlation observed between the fractal capacity

dimension and GPS strain measurement.

The observation of results from the study of capacity dimension of faults

system in chapter 3 and the GPS measurements of field data in Kumaun Himalaya

region of the present chapter 5 leads to identify a block of high risk zone for future

earthquakes. The most interesting feature about Fig.5.14 is the basic strain rate in the

study region with block-“S” (Fig.5.17), where extensive convergence is noticed. The

striking part is that structural elements in this block -“S” are Main Boundary Thrust

(MBT), North Almora Thrust (NAT), South Almora Thrust (SAT), Ramgarh Thrust

and Moradabad Fault (Fig.5.20).To understand the region of earthquakes more

clearly, the observation of past 39 years seismicity indicate that the large number of

earthquakes are observed in blocks- “G”, “M”,“T” and “U” but block – “L” and “S”

have experienced least events (Fig.5.17 and Fig.5.18).This may be analyzed as the

release of energy occurs through earthquakes in those blocks with maximum number

of tremors. On the other hand, block – “S” (Fig.5.17 and Fig.5.18) show highest D0

value with least seismic activity. Moreover, the recent ten years’ earthquake

occurrence was also shown in (Fig. 5.19) to understand the current seismicity in the

region and its relation with capacity dimension. The result was found similar to the

seismicity of past 39 years’ seismicity (Fig. 5.17), (Fig.5.18) & (Fig. 5.19).In this case

of ten years seismicity only one event is located in the block –“S” with highest D0

value of 1.818.The reason of such a low seismicity in the block is really a matter of

interest. Moreover, the two swarms Gopeshwar and Tapovan along with trend of b

value reported by Paul and Sharma (2011), marked as the precursors for future

earthquakes in this region which lie just above the block –“S” and adjacent region

indicating the significance of least seismicity and strain accumulation from GPS

measurement (Fig.5.14) & (Fig.5.18 and 5.19). The quantification of fault systems

with the capacity dimension leads us to in-depth understanding of the present faults

activity accompanied with the intensity of seismicity. The extensive convergence is

observed in the block –“S” with highest D0 value of 1.818 and the block is

experiencing the least seismic activity suggesting us to indicate the block for seismic

hazard zone. A denser network is expected to provide better strain accumulation

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distribution in the study region. Hence more continuous and campaign GPS stations

are required to be installed in each block to understand the nature of crust deformation

leading to earthquakes. In the next chapter, the overall summary and conclusions of

results obtained by various approaches undertaken in this thesis will be made.

Fig.5.17 Fractal capacity dimension (D0) value of the structural elements in each

block is given. The block – “S” shows the significant tectonic elements present in the

block with highest capacity dimension value of 1.818 marked by big open red circle.

S

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Fig.5.18 Seismicity of the study region with the structural elements and Fractal

capacity dimension (D0) blocks marked by capital letters –“A-Y” is depicted. The

block – “S” shows the significant tectonic elements present in the block with highest

capacity dimension value of 1.818 and having very less seismicity marked by red

squared area and small black open circles denotes the earthquakes.

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Fig.5.19 Above figure shows spatial distribution of earthquakes occurred during the

recent ten years. The capital letters depict the block number and fractional value

shows the capacity dimension value of the corresponding block. Here the six highest

capacity dimension value block in the entire study area are shown with seismicity

marked by squared boxes. The small open circles denote the earthquakes. The single

bigger circle indicates the identified zone of high seismic risk which is the block –

“S” with least seismicity and highest capacity dimension value.

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Fig.5.20 The important faults and thrusts of the block where the highest capacity

dimension (D0) with least seismic activity during the recent ten years is observed. The

open circle indicates earthquake. The block – “S” is experiencing with the extensive

convergence of strain accumulation. This block lying between Latitude 29ºN - 30ºN

and Longitude 79ºE - 80ºE is estimated as the highly seismic hazard zone.

S

D0 = 1.818