Introduction
The Tibetan Plateau has a long history of deformation within the
last 50 million years e.g.. The
reliability of seismic anisotropy measurements is a challenging issue as it
is essential to identify the tectonics, coupling–decoupling of the
crust-lithospheric mantle, multi-layered anisotropic modelling, and active
seismicity in relation to the type of deformation and possible flow patterns,
which are still a matter of debate in understanding the formation process and
future challenges of this active region.
Lattice-preferred orientation (LPO) of olivine mineral in the mantle as a
result of plate interactions is controlled by various geodynamic processes
and is considered to be the main cause of the shear wave splitting
observations on the teleseismic S and SKS waves. Deformation in the upper
mantle generally takes place through two processes: diffusion and dislocation
creep under favourable conditions. The dislocation creep process, which is
the creeping motion of crystal dislocation, is considered to be the leading
cause of mantle anisotropy
.
It can be caused by either high-stress conditions or large grain size or
both, but the nonlinear increase in the strain rate is independent of the
grain size . This type of deformation is expected to
occur at a depth range of less than 400 km
e.g. where olivine is the most common
mineral, and hence LPO development and observed anisotropy mainly represents
the upper 400 km of the mantle .
Several observations on seismic anisotropy have greatly contributed to
elucidating these deformation patterns in relation to the past and present
geodynamic activity of the region. Generally speaking, SKS splitting analyses
are the most diagnostic, quick, and well-established way of detection and
quantification of seismic anisotropy. The SKS phase does not propagate as an
S wave in the liquid outer core and refracts from a P wave into an SV
(radially polarized) wave when entering the receiver-side mantle. Hence a
recorded SKS phase at the surface is not influenced by the source-side
anisotropy. The main disadvantage of using the SKS phase in splitting
measurements is that finding good-quality observations is restricted by
several parameters, i.e. epicentral distance and propagation direction of the
event, and therefore the measurements need to be supplemented with other
phases (e.g. ScS and direct S) that can provide better azimuthal coverage.
However, employing such additional phases may introduce contamination due to
the source-side anisotropy. Splitting of shear waves is similar to the
birefringence phenomena in optics. Shear waves split into fast and slow
components when they pass through an anisotropic medium. In such a situation,
we obtain particle motion (e.g. elliptical, cruciform) with different shapes
that depend on the anisotropy along the ray path. If the anisotropy is the
only cause of splitting, then the observed shear wave (fast or slow) can be
rotated in such a way that two very similar phases are seen, apart from
scaling and a time delay between them
. Resultant splitting parameters,
ϕ and δt, indicate the rotation angle in relation to the flow
direction and shearing or extension along the ray path under the assumption
of LPO in the upper mantle (a.k.a. fast polarization direction or FPD), and
to the strength and thickness of the anisotropic layer (a.k.a. delay time),
respectively. Splitting measurements from the Himalaya–Tibet collision zone
have long been explained by the presence of a single homogeneous layer with a
horizontal axis of symmetry
e.g..
Tectonic and topographic map of the Himalayas and Tibet. Red
triangles represent the broadband seismic stations of the XE network within
the study region (MFT: Main Frontal Thrust; MBT: Main Boundary Thrust; MCT:
Main Central Thrust; ITSZ: Indus–Tsangpo suture zone; BNSZ: Bangong–Nujiang
suture zone).
Earlier SKS and SKKS measurements in the study area
. The length of solid bars shown for each
seismic station is proportional to the splitting time delay (δts), and their orientation represents the fast polarization
direction (ϕs). For clarity, the seismic stations used in the
present study are shown by yellow-filled rectangles along with the SKS
splitting measurements of . Seismic stations where null or
negligible anisotropy is reported in earlier studies
see are shown by grey-filled
rectangles. All other stations are shown by red-filled circles.
The use of direct S waves of earthquakes at teleseismic distances
(30–90∘) can provide complementary splitting measurements to SKS
measurements as this helps in establishing a more complete database of
anisotropy that will be inferred from good-quality S wave signals from an
enhanced azimuthal distribution relative to SKS splitting only. This is
crucial for the Indian subcontinent where SKS measurements are skewed towards
eastern azimuths, and very few SKS measurements have been obtained due to
temporary deployments see. However, the major problem
in including direct S waves in splitting measurements is the contamination
of the S wave signals due to the influence of anisotropic structures
existing within the source-side region. have recently
shown that this problem can be overcome using an array-based approach, known
as the reference station technique (RST). The method assumes an identical
source-side anisotropy effect at two closely located stations (reference and
target stations) with small differences in epicentral distances. In this
case, optimum splitting parameters can be estimated by searching for
receiver-side correction parameters for the target station that result in
maximum similarity to the S wave signal corrected for previously known
receiver-side anisotropy beneath the reference station in a grid search
scheme. Signals used for that comparison are those of the reference station
previously corrected for known reference receiver-side anisotropy and of the
target station whose receiver-side splitting parameters are desired to be
estimated. In this technique, we utilize seismic anisotropy parameters, which
were previously inferred from the SKS measurements by as
the reference knowledge of the receiver-side anisotropy beneath the reference
station. The RST has been successfully tested through both synthetic and
observed data collected along the northeastern and southwestern parts of the
Tibetan Plateau and the Hellenic Trench in the eastern Mediterranean
e.g.. The present study
focuses on the southeastern part of Tibet near Namcha Barwa (Fig. 1). The
study region is located between and around the Indus–Tsangpo suture zone
(ITSZ) and Bangong–Nuijiang suture zone (BNSZ). Our major motivation is to
calculate S-wave-derived seismic anisotropic parameters that may have a
potential link to tectonic setting and deformation history with the help of a
correlative analysis of resultant anisotropy observations with absolute plate
motion (APM) directions, GPS measurements, and the structural and topographic
features. Our results contradict previous interpretations of an isotropic
Indian lithospheric mantle and
add new constraints in understanding the types of deformation and their
causes in the region.
Examples of the direct S wave splitting measurements based on the
reference station technique at stations ES01 and ES35. Figures on the left
side represent the splitting measurement recorded at station pair ES03
(reference station)–ES01 (target station), and those on the right side
represent the splitting measurement recorded at station pair ES16 (reference
station)–ES35 (target station). (a) Misfit surface with splitting
parameter 82∘ ± 13∘ and 1.25 ± 0.35 s.
(c) Signal at reference station (ES03) with receiver-side
correction. (e) Signal at target station (ES01). (g) Fast
and slow component after rotating signal at target station (ES01) using
ϕ (82∘). (i) Fast and slow component corrected for
δt (1.25 s). (k) Corrected radial and transverse components
at target station (ES01) using optimum ϕ and δt and isotropic
delay (-0.2 s). (m) Residual trace. Panels on the right side
follow the same order and explanation.
Data and method
In this study, we examine a total of 5285 waveforms with the direct S waves
extracted from 161 teleseismic events with magnitudes ≥ 5.5 within an
epicentral distance range from 30 to 90∘. The teleseismic events used
in this study are recorded at 47 seismic stations of the XE network, which
operated between 2003 and 2004 . Of those 47 stations, we
selected for use as reference stations only those 36 seismic stations where
we have knowledge of seismic anisotropy inferred from SKS splitting
measurements performed by . Prior to the data analysis, we
remove the instrument response from the original seismograms to overcome
biases that can depend on the potential use of different stations (at
reference and target sites). At the stage of the preprocessing, a band-pass
filter between 0.03 and 0.2 Hz is performed to enhance S signals and
resample the seismograms at 20 samples per second to avoid aliased signals
and to reconstruct the waveforms in the appropriate frequency range. Signals
with possible contamination with other phases, such as ScS, SKKS, and SKS,
are omitted from the analysis. We select only those waveforms, which have
≥ 2.5 signal-to-noise ratio (SNR) on the transverse and radial
components for further analysis. The selection of the waveforms is achieved
by performing a manual visual inspection that allowed only 40 % of the
direct S waveforms. We begin data analysis by determining station pairs
over the entire area. We form the station pairs by selecting the same
earthquake event recorded at both (reference and target) stations.
and successfully applied the RST to
regional arrays with an interstation distance less than 300 km. By taking
300 km interstation spacing as the limit in a similar fashion, we have
formed 22 816 station pairs with four horizontal components available at
reference and target stations out of 35 649 possible station pairs; these
are based on 161 teleseismic events prior to the application of the
technique. To minimize the effects of the coda waves and converted phases, we use a 45 s
time window starting 15 s before the theoretical onset of the direct S waves
on the basis of the IASP91 1-D radial earth velocity model of
. This excludes the undesired effect of crustal S
multiples in the thick Tibetan crust.
Examples of the direct S wave splitting measurements based on the
reference station technique at stations ES33 and ES19, where previously null
anisotropy was obtained using an SKS splitting measurement .
Panels on the left side represent the splitting measurement observed at the
station pair with ES12 (reference station) and ES33 (target station), and
those on the right side represent the splitting measurement observed at the
station pair with ES12 (reference station) and ES19 (target station). The
explanation for each panel is the same as in Fig. 3.
The approach used in the present study avoids the source-side anisotropy by
minimizing the misfit function between the corrected seismic waveforms at the
reference and target stations. At the first stage, an inverse splitting
operator depending on a backward angular rotation with two horizontal
components, a time shift, and the reversal of the back angular rotation are
employed to correct the reference station for known receiver-side anisotropy
(generally inferred from SKS splitting analyses) when estimating the
direct-S-derived individual splitting parameter .
Following the correction of the reference station, S signals are corrected
for splitting parameters in a grid search manner at the target stations.
Corrected S wave signals at reference and target stations are compared to
each other for each pair of splitting parameters. Such comparison also allows
for time shifts and amplitude corrections to account for the lateral
heterogeneities and differences in site response between these stations by
optimizing the time shift (Δt) and amplitude factor (a). First, we
assign splitting parameters that minimized the misfit function simply
representing the difference between the corrected reference and target
station traces as optimum splitting parameters for the receiver-side beneath
the target station at a given station pair. Later, taking the average of all
optimum splitting parameters estimated at station pairs related to a given
target station are considered representative of a given event. In the end,
station-pair averaged splitting parameters are averaged over all events to
estimate the final splitting parameters at each given target station.
Here we should note that our approach initially depends on the knowledge of
receiver-side seismic anisotropy that can be most likely inferred from
directionally averaged SKS splitting parameters at a given reference station.
Conventional SKS splitting measurements are performed under the assumption of
a single-layer anisotropic structure with a horizontal axis of symmetry.
However, beneath the regions with complex anisotropic structures, for
instance, in the case of a well-developed continental lithosphere with a
dipping axis of symmetry (i.e. stable cratonic regions, see
) or an existing double-layer anisotropy
, significant directional variation of apparent
splitting parameters will likely be expected. In such regions, the average
value of splitting parameters as reference knowledge of seismic anisotropy
cannot be representative of the events from different directions, thus making
resultant average S-derived splitting parameters misleading in our method
since complicated anisotropic structures likely introduce a similar influence
on both the SKS phase and direct S waves. However due to the fact that our
approach certainly provides more splitting observations from an increased
amount of back-azimuths, these new directionally enhanced apparent
S-derived splitting parameters help in resolving the actual orientation of
the anisotropic structure by using more sophisticated modelling strategies,
which is not within the scope of the present work.
An example of null-anisotropy measurement based on the RST at
stations ES29 and ES37. Panels on the left side represent the splitting
measurement recorded at station pair ES27 (reference station)–ES29 (target
station), and those on the right side represent the splitting measurement
recorded at station pair ES05 (reference station)–ES37 (target station).
The explanation for each panel is the same as in Fig. 3.
Epicentral distribution of teleseismic earthquakes used in the study
(30–90∘). Rectangles indicate seismic stations in this study.
The RST relies on two important underlying assumptions: (i) the ray path at
two stations can be considered equivalent in the deeper mantle part and near
the source-side region due to the fact that the distance between receiver and
target stations is small (< 300 km) compared to the epicentral distance;
(ii) waveform differences between the receiver and target stations are only
due to differences in anisotropic structure after correcting any waveform
differences in time and amplitude presumably due to the lateral
heterogeneities and differences in site response between these stations. Any
potential difference between the thickness of the crust and sedimentary
layers will also cause the timing and amplitude of converted phase, but
showed, numerically, its influence on expected splitting
parameters would be negligible. During the application of the technique, we
let ϕ and δt vary from 0 to 180∘ with an increment of
1∘ and from 0 to 3 s with an increment of 0.05 s, respectively. We
perform an inverse F test error analysis for uncertainty estimates of
obtained splitting parameters. In this process, we check the reliability of
the individual splitting parameters by comparing variation in the residual
energy distribution away from the minimum with the variation according to the
preset confidence level of 95 %. At this stage, the number of degrees of
freedom in the data and unknown model parameters becomes crucial. According
to 1 degree of freedom is set to 1 s and considering
two horizontal components, the number of degrees of freedom becomes 2 times
the data length, which could be considered a typical value for teleseismic
data. In our case, however, the number of unknown model parameters is four at
the minimum point (ϕ and δt, isotropic delay, amplitude
correction factor) or two at any given splitting parameters tried in the grid
search, reducing the number of degrees of freedom by four or
two, respectively. Estimating the number of degrees of freedom is a
challenging task. For an appropriate uncertainty analysis, the assumption of
band-limited Gaussian noise is required to be justified as reported by
. Thus, taking a fixed value for the degrees of freedom
as performed in this study will allow us to compare the reliability of
different individual splitting estimates rather than the absolute value of
the error bounds.
List of the earthquakes used in this study.
Event date
Event time
Latitude
Longitude
Depth
Magnitude
Location site
(∘)
(∘)
(km)
2003/10/04
14:49:02.7
-07.05
+125.41
532.7
5.5
Banda Sea
2003/10/17
17:19:53.6
-05.08
+102.46
35.1
5.6
Southern Sumatra, Indonesia
2003/11/09
19:23:28.6
+01.56
+127.36
133.9
5.8
Halmahera, Indonesia
2004/02/08
08:58:51.8
-03.66
+135.34
25.7
5.7
Irian Jaya Region, Indonesia
2004/02/20
05:58:45.2
-11.61
+166.45
84.0
5.6
Santa Cruz Islands
2004/03/17
05:21:00.8
+34.59
+023.33
24.5
5.9
Crete, Greece
2004/03/26
15:20:06.6
+41.86
+144.21
22.4
5.7
Hokkaidō, Japan region
2004/04/09
15:23:35.0
-13.17
+167.20
228.4
5.8
Vanuatu
2004/05/28
12:38:44.3
+36.25
+051.62
17.0
6.2
Northern and central Iran
2004/06/22
09:04:43.9
-10.90
+166.26
152.8
5.8
Santa Cruz Islands
2004/06/30
23:37:25.5
+00.80
+124.73
90.8
6.0
Minahassa Peninsula, Sulawesi
2004/07/08
10:30:49.2
+47.20
+151.30
128.5
5.9
Kuril Islands
2004/07/25
14:35:19.1
-02.43
+103.98
582.1
6.8
Southern Sumatra, Indonesia
2003/07/27
06:25:32.0
+47.15
+139.25
470.3
6.3
Primor'ye, Russia
2004/08/02
02:36:54.9
-05.47
+102.62
40.5
5.5
Southern Sumatra, Indonesia
2004/08/07
14:18:35.2
-06.24
+095.67
20.7
5.8
Southwest of Sumatra, Indonesia
2004/08/28
17:00:58.2
-08.69
+157.25
10.0
5.5
Solomon Islands
2003/08/31
23:08:00.3
+43.39
+132.27
481.1
5.5
Primor'ye, Russia
2003/09/11
21:58:25.5
-08.20
+156.16
10.0
5.5
Solomon Islands
2004/09/15
19:10:50.6
+14.22
+120.41
115.4
6.0
Luzon, Philippines
2003/10/11
00:08:49.1
+41.92
+144.36
33.0
5.9
Hokkaidō, Japan region
2003/10/11
01:11:31.2
+43.97
+148.21
51.2
6.2
East of Kuril Islands
2003/10/17
10:19:06.8
-05.47
+154.15
133.0
6.2
Solomon Islands
2003/10/22
11:45:30.8
-06.06
+147.73
53.5
6.2
Eastern New Guinea region
2003/11/12
08:26:43.7
+33.17
+137.07
384.9
6.1
Near S. coast of Honshū
The tectonic and topographic map of the southeastern Tibetan region,
which represents the station average splitting parameters: (a) the
previous SKS-derived splitting measurements (solid blue bar) performed by
and (b) the direct S-wave-derived splitting
measurements (solid red bar, this study). The length of the solid bars in
each panel indicates the strength of anisotropy and is scaled by
station-averaged splitting time delays. Azimuth of the solid bars indicates
the fast polarization direction (FPD). Black circles show location of the
seismic stations used in this study. (MFT: Main Frontal Thrust; MBT: Main
Boundary Thrust; MCT: Main Central Thrust; ITSZ: Indus–Tsangpo suture zone;
BNSZ: Bangong–Nujiang suture zone).
An example of the basic steps of the RST can be found in Fig. 3 for target
stations ES01 and ES35, respectively. Figure 4 present the examples of the
obtained splitting parameters at target stations ES19 and ES33, where null or
no measurements are reported by . Null splitting may be
observed for three reasons: (i) if the incoming polarization direction below
an anisotropic layer is parallel to the fast or slow axis; (ii) if the region
is isotropic in nature due to complex anisotropy
e.g.; (iii) if the region itself is
isotropic in nature. Following and ,
we use the F test with the null-split rejection criteria to be able to
avoid the contamination of the null measurement with the good splitting
measurements. In this process, we calculate the theoretical residual energy
under the assumption of null measurements and compare this with the observed
residual energy at the minimum. Figure 5 shows two examples of null-splitting
measurements at target stations ES29 and ES37, respectively. To ensure the
stability of the results, we perform a stepwise quality assessment criterion
before calculating the average splitting parameters at each station. To
achieve this aim, we considered only those waveform pairs that have (i) a
normalized residual energy (ΔE) smaller than 0.5; (ii) an amplitude
correction factor parameter (a) in between 0.4 and 0.6; and (iii) a
95 % confidence level for null-splitting rejection. We rejected the
waveform pairs that have a ϕ error greater than 25∘ and a delay
time error greater than half of the delay time itself. After these quality
assessments, we are left with only 3231 waveform pairs. At this stage of the
processing, we perform another visual inspection to enhance the quality of
our estimates, yielding only 501 very high-quality waveform pairs. These
final waveforms show clear splitting and are free of any distortions due to
signal processing. The remaining 501 waveform pairs are extracted from only
25 teleseismic events (Fig. 6) and are used to calculate the average
splitting parameter at each station. The list of these 25 teleseismic events
is provided in Table 1. We apply the Von Mises approach
to calculate the circular mean at each target stations
for ϕ and an arithmetic mean is used for δt.
Obtained average splitting parameters (ϕs and δts) estimated from direct S wave splitting
measurement.
Station
Latitude
Longitude
ϕs
δts
Number of events
Contributing reference stations
(∘)
(∘)
(∘)
(s)
at a station
ES01
31.26
92.09
73.9
1.5
31
2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 36, 38, 39, 40, 41
ES02
31.00
92.54
75.7
1.5
26
1, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 38, 40, 41
ES03
30.75
92.86
81.7
1.2
16
2, 4, 5, 7, 8, 10, 11, 12, 38, 39, 43
ES04
30.65
93.25
91.5
1.2
11
1, 3, 5, 7, 8, 10, 11, 12
ES05
31.68
92.40
71.2
1.1
14
1, 2, 3, 4, 7, 8, 10, 11, 12, 13
ES07
31.48
93.70
93.8
1.0
15
1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 14, 38
ES08
31.28
93.84
106.3
0.9
12
1, 2, 4, 5, 7, 10, 11, 12, 13
ES09
31.91
93.06
81.5
1.1
15
1, 3, 5, 8, 10, 11, 12, 13, 14
ES10
31.84
93.79
103.1
1.0
19
1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 38
ES11
31.91
94.14
96.2
1.2
25
1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 14, 36, 38
ES12
31.59
94.71
101.3
1.2
28
1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 23, 25, 38
ES13
31.54
95.28
88.5
1.4
15
2, 4, 9, 11, 12, 13, 14, 15, 26, 30, 36, 38
ES14
31.25
95.90
102.9
1.2
24
3, 4, 7, 9, 10, 11, 12, 13, 15, 17, 23, 25, 31
ES15
31.19
96.50
104.2
1.0
09
11, 12, 14, 16, 25, 36, 38
ES16
31.18
97.02
117.0
1.4
01
13
ES17
31.27
97.55
135.4
1.0
11
18, 23, 25, 31
ES18
31.30
97.96
122.0
0.8
02
17
ES19
30.81
95.71
106.6
1.5
09
3, 4, 7, 8, 11, 12, 38
ES20
30.73
96.10
108.0
0.9
01
11
ES22
30.81
96.70
110.0
0.9
01
14
ES23
30.69
97.26
96.2
1.0
16
12, 15, 25, 31
ES24
30.50
97.14
106.0
1.1
03
13, 15
ES25
30.12
97.30
121.1
0.9
10
18, 23, 31
ES26
29.96
97.51
142.2
1.0
03
14, 30, 31
ES27
29.64
97.90
130.0
1.1
01
26
ES29
30.01
96.69
84.0
1.1
16
14, 15, 23, 25, 31
ES30
29.32
97.19
110.8
1.1
07
25, 26, 27, 31
ES31
29.51
96.76
82.4
1.2
16
14, 17, 25, 26, 30, 31
ES32
29.76
96.10
103.0
1.4
02
13, 30
ES33
29.77
95.70
67.1
0.6
10
3, 7, 11, 12, 14, 15, 30, 38
ES34
29.91
95.47
84.8
1.2
20
3, 4, 7, 8, 10, 11, 12, 14, 15, 16, 36, 38
ES35
29.96
94.78
111.9
1.1
09
1, 4, 10, 14, 15, 16, 31, 36, 38
ES36
29.81
93.91
86.2
1.1
13
1, 4, 5, 7, 8, 11, 13, 14, 15, 38, 40, 41
ES37
29.90
93.51
80.5
1.4
07
1, 10, 13, 36, 39, 40, 41
ES38
30.02
92.97
72.5
1.2
18
3, 4, 5, 7, 8, 10, 11, 12, 36, 39, 40, 41
ES39
29.87
92.62
75.8
1.6
17
1, 2, 3, 4, 8, 9, 10, 11, 36, 38, 40, 41
ES40
29.71
92.15
78.0
1.4
14
1, 2, 4, 5, 7, 8, 10, 36, 38, 39, 41, 43
ES41
29.19
91.76
70.4
1.7
14
2, 3, 4, 5, 36, 38, 40
ES42
28.90
91.94
108.6
1.6
06
1, 37, 39
ES43
29.04
92.23
75.4
1.2
05
8, 36, 38, 39
ES45
29.12
93.78
65.8
1.3
05
7, 8, 38
ES46
29.25
94.26
89.2
1.1
04
10, 36
Comparison of SKS and S-wave-derived station-averaged splitting
parameters in the study region. Panels (a) and (b): scatter
plots that compare SKS- and S-derived FPDs and split time delays (TDs), respectively.
(c) Scatter plot of the number of individual SKS splitting
measurement and the number of events used in direct S splitting measurement
(this study). Note that each station here is colour-coded by its absolute
deviation value that is obtained by subtracting S- and SKS-derived FPDs.
(d) The same plot for the misfit between SKS- and S-derived
station-averaged split TDs. Average SKS splitting parameters used in this
figure are taken from .
Lateral variations of anisotropic, geodetic, and absolute plate
motion data shown over topographic and tectonic features of the study area.
(a) Map view comparison between the splitting measurement inferred
from direct S waves (this study shown by red bars) and SKS splitting
measurements in blue bars. (b) The Global
Positioning System (GPS) velocity (mm yr-1) vectors (black arrows)
around SE Tibetan region calculated with respect to the South China reference
frame and stable Eurasia. GPS data is compiled from several studies including
, , , and
. (c) Absolute plate motion (APM) velocities
calculated through
https://www.unavco.org/software/geodetic-utilities/plate-motion-calculator/platemotion-calculator.html
by using the GSRM v1.2 (2004) model of . Note that
arrows in brown, green, and purple represent the APM velocities of the
Eurasian Plate in no net rotation frame, of the Indian Plate with respect to
the Eurasian Plate, and the motion of the Indian Plate in no net rotation
frame, respectively. (d) Map view comparisons of the station (black
circle) average direct S-wave-derived splitting parameters with GPS
velocity (black arrow) and APM (brown arrow) vectors. Abbreviations on the
maps: MFT: Main Frontal Thrust; MBT: Main Boundary Thrust; MCT: Main Central
Thrust; ITSZ: Indus–Tsangpo suture zone; BNSZ: Bangong–Nujiang suture zone;
JRSZ: Jinsa River suture zone.
Results
We present here 501 splitting measurements observed for 42 seismic stations
of the XE network between the years 2003 and 2004. The angular average of
individual direct-S-derived splitting parameters (ϕs and
δts) at each station is given in Table 2. Station-averaged
splitting parameters usually reflect significant anisotropy with large delay
times (>1 s, Figs. 7 and 8) compared to those that could be considered
negligible based on previously determined SKS-derived anisotropy parameters
. For example, at station ES31 direct S waves provide a
relatively large time delay (1.23 s) although SKS splitting analysis
performed by earlier resulted in a much smaller time delay
time of about 0.3 s. Across the network, we observe considerable variation
in direct S-wave-derived delay times ranging from 0.64 to 1.68 s. In
general, we observe the SW–NE to W–E trend in ϕs before the
edge margin of the southeastern Tibetan region. A consistent change in
variation of ϕs is observed further east where orientations
take a sharp change from a SSW–ENE or W–E to a NW–SE direction (Fig. 7).
We find significant splitting (≥ 0.64 s) at seismic stations ES19,
ES20, ES22, ES32, ES33, ES34, ES42, and ES45, where previously null or
negligible splitting was observed in SKS waves . This could
stem from multi-layered anisotropic orientations or insufficient amount of
SKS-derived splitting measurements. This observation is inconsistent with an
isotropic nature of the Indian lithosphere and indicates a complex 3-D nature
or more complex deformation pattern of the EHS. The scatter plot in Fig. 8
exhibits a comparison between estimated splitting parameters
(ϕs and δts) from the analysis of direct
S waves and previous SKS splitting measurements . The
overall trend of the obtained splitting parameters (ϕs) is
consistent with the previous SKS measurement, whereas we observe larger time
delays for the S waves as compared to SKS phase.
We combine our splitting measurements with existing geodetic measurements
(GPS velocity vectors and APM velocity vectors). We plot the GPS velocities
by using published data of , ,
, and . The APM velocities are calculated
via a web-based plate motion calculator
(https://www.unavco.org/software/geodetic-utilities/plate-motion-calculator/plate-motion-calculator.html)
that is based on an integrated global plate motion model (GSRMv1.2)
originally developed by . Figure 9 shows the
correlative analysis of splitting parameters by using direct S phases, APM
directions, and GPS measurements in our target region. It suggests that the
observed anisotropy is not only due to lithospheric deformation or due to
asthenospheric dynamics at the base of the lithosphere but that it is a
combined effect of both.
Discussion
Origin of anisotropy in the southeastern Tibetan region
Our resultant splitting measurements vary over a range of δts suggesting the presence of a significant deformation in the
region. The fast polarization directions are rather consistent and match well
with the surface geology, similar to those observed from the SKS phases in
. The fast directions closely follow the strike of the major
sutures like BNSZ and ITSZ and surface strain fields as observed through GPS
and are under the influence of bending at the EHS (Fig. 7). FPDs that are
parallel to the surface geologic features such as faults
e.g. are indicative of vertically
coherent deformation of the crust and upper mantle. This has previously been
invoked to explain the anisotropic character in eastern and northeastern
Tibet . In the absence
of any compelling evidence for crust–mantle coupling, we argue in favour of
a large-scale deformation of the crust and upper mantle under similar
boundary conditions as a plausible option to explain the observed anisotropy
.
The observed large time delays (> 1 s), in this study, reflect a highly
anisotropic region with similar deformation patterns at depths. The presence
of a more complex anisotropic structure (e.g. double layer) with different
orientations in the fast axis at various depths may result in smaller delay
times . In the western Himalayan region,
observed different fast-velocity directions for seismic
azimuthal anisotropy that vary from N 60∘ E at depths between 80 and
160 km to N 150∘ E at depths between 160 and 220 km depth by using
the joint inversion of SKS particle motions and P receiver functions. This
provides an argument to explain the null or negligible anisotropy as reported
from the same region using SKS phases . Smaller time
delays in the Nepal Himalayas and the Sikkim Himalayas are attributed to the
combined effect of shear at the base of the lithosphere due to APM-related
strain of the Indian Plate and ductile flow along the collision front due to
compression, with possibly completely different orientations
. reported null measurements at a few
stations, possibly due to the lack of clear splitting measurements of SKS
phases. The transition between deformation types at the boundaries of the
Indian and Eurasian lithospheric plates was considered to be the main reason
for observed null or negligible anisotropy further west beneath the southern
Tibetan Plateau .
The lack of anisotropy beneath southern Tibet was mainly explained by an
isotropic nature of the Indian tectonic plate or a lack in the ability of SKS
phases to sample the anisotropy due to a sub-vertical mantle shear strain
field created by downwelling Indian lithosphere
. However, the hypothesis of an isotropic
nature of the Indian lithosphere was contradicted in various studies
, and significant anisotropy is
reported beneath Tibet in the region of null measurement
.
Sub-vertical shear strain or complex flow arises due to the subducting Indian
slab and may result in null or negligible anisotropy
. Recent tomographic studies
suggest that in the western Tibetan side, where the N–S extension is less,
the Indian lithosphere is supposed to extend as far as the Jinsa River suture
zone (JRSZ; ), while in the eastern Tibet side, the
Indian lithosphere extends up to the ITSZ . A
combined study using seismic anisotropy and Bouguer gravity anomalies place
the Indian mantle front up at 33∘ N in central Tibet
. In this segment of the Himalaya–Tibet collision zone,
the northern limit of the Indian lithospheric mantle does not seem to extend
beyond the ITSZ . The lack of anisotropy reported using
SKS/SKKS phases at a few seismic stations might be due to
insufficient measurements rather than the effects of the downwelling Indian
lithosphere as suggested in southern Tibet . By adding
a considerably large amount of measurements from direct S waves, we observe
significant anisotropy for the same stations and fast axis deformation, which
can be explained by eastward flow in a lithospheric crush zone formed due to
the collision of the Indian and Asian tectonic plates as suggested for
southern Tibet .
The crust beneath Tibet is thick (∼ 80 km; e.g.
) and crustal anisotropic effects should be accounted
for in the splitting measurements obtained using direct S and SKS/SKKS
phases. In the Himalayan region, highly anisotropic crust (∼ 20 %)
has been reported using an inversion of receiver functions
, while a similar approach at a few seismic
stations covering Tibet suggests approximately 4–14 % seismic anisotropy
within the Tibetan crust .
have accounted for splitting of < 0.5 s over SKS
split times due to the observed anisotropy of > 10 % in the crust.
Splitting times of 0.2–0.3 s are observed within the eastern Tibet crust
using splitting of the Moho-converted Ps phases (receiver functions,
). Tomographic
,
magnetotelluric , and gravity studies
of the SE Tibetan region suggest ductile flow in the deeper region of the
crust. Relatively low seismic velocities are resolved for shear waves in
tomographic studies at these crustal depths, indicating localized flow of the
crustal material along a network of strike–slip faults in the region
. These types of flow may produce splitting orientations
similar to lower lithospheric scales with coherent deformation. A coupled
crust and mantle increases the SKS delay times by 0.2–0.5 s due to the
effects of crust. The anisotropic orientations observed beneath most parts of
Tibet within the crust are
completely different from the SKS or direct S waves, implying that the
types of deformation in the crust and upper mantle could be different; this
does not indicate a coherent deformation pattern, at least in some parts of
Tibet. A possible explanation for such decoupling could be that the crustal
anisotropic parameters are influenced by either current deformation or
fossilized fabric with different boundary conditions at mid-crustal and lower
crustal levels.
Comparison between direct S- and SKS-derived splitting parameters
The scatter plot in Fig. 8a, b provides a comparison between estimated
splitting parameters (ϕs and δts) from the
analysis of direct S waves and previous SKS splitting measurements
. The obtained splitting parameters (ϕs) from
the direct S wave measurements show that they are consistent with previous
SKS-based measurements. Overall, the consistency between splitting parameters
inferred from SKS and direct S waves is most likely because both are
derived from the same type of large-scale anisotropic structures. Large
differences between SKS- and S-derived δts that appear as
a move-out in the scatter plot occur for three reasons: (i) longer S wave
ray paths as compared to SKS ray paths sampling the same type of large-scale
anisotropy, (ii) an increase in the number of the events (Fig. 8c, d)
sampling different azimuths that contribute to the direct S wave
measurements as compared to the SKS, and (iii) the S-derived type of
anisotropy might show strong variation in splitting with incident angle, i.e.
an event coming from different azimuths. The precision of our results is
evident from small deviations in Fig. 8c, d, and this is most likely due to
the involvement of a relatively increased number of observations from both
S and SKS phases in splitting measurements. Figure 8c shows that the
absolute deviation for ϕs is no larger than 25∘ except
at 7 out of 42 stations (namely ES23, ES27, ES31, ES39, ES40, ES41, and
ES46), where we observe large deviations of up to 48∘. An extreme
example of maximum deviation for δts is at station ES31.
The deviation for ϕs is also relatively large
(>30∘). Because station ES31 does not suffer from a lack of data,
(8 observations for SKS and 14 for S), we infer that the mismatch may be a
result of the use of an incorrect reference anisotropy when correcting for
receiver-side anisotropy. Overall, deviations for δts are
smaller than 0.56 s. In general, we observe relatively more events for the
direct S waves compared to individual SKS phases except at stations ES16,
ES18, ES23, ES26, and ES27. For these five stations, we detect deviations for
ϕs and δts of up to 36∘ and 0.5 s,
respectively. In summary, our comparative analysis of splitting parameters
shows a good accordance between SKS- and direct-S-derived splitting
parameters as previously observed in the Himalaya–Tibet collision zone
e.g. and in
the Indian shield .
Deformation pattern revealed from the comparison of the GPS, APM, and splitting measurements
Previous studies on seismic anisotropy
that compared splitting parameters with APM, GPS, and structural and
topographical features provide crucial information concerning the dynamic
deformation pattern and possible linkage of the strength of coupling between
the crust and lithospheric mantle of the southeastern or eastern Tibetan
region. We observe a sharp transition in the spatial distribution of
ϕs from nearly W–E in the western part of the study region to
nearly NW–SE or NNW–SSE near the southeastern Tibetan margin (Fig. 9). The
structural and topographical features, such as major suture zones and
mountain belts, tend to rotate around the EHS from nearly E–W or ENE–WSW to
N–S or NE–SW . The observed
ϕs and GPS velocity vectors follow a similar trend (Fig. 9a,
b). The APM directions are consistent with the present ongoing asthenospheric
flow . By using different
plots of the plate motion (APM of Eurasian and Indian plates referenced to
the no-net-rotation (NNR) frame or the relative plate motion of the Indian Plate referenced to
the Eurasian Plate), we want to examine the contribution of APM to explaining
the observed anisotropic variation and to check which plate motion best explains the observed ϕs
of the splitting measurements. But the observed ϕs are not
consistent with plate motion. The discrepancy between the ϕs
and APM may indicate that the obtained splitting, and hence the anisotropic
behaviour of the study area, is not only due to asthenospheric dynamics but
is a combined effect of the lithospheric deformation and asthenospheric
dynamics. The lateral variation of obtained splitting measurements, when
taken together with GPS velocity vectors, geological features, and the APM
directions, depicts the movement of lithospheric or crustal material of the
western and central plateau relative to the Eurasian Plate towards the
eastern Tibetan side and clockwise rotation around the EHS. This supports the
presence of a deep crustal flow and movement of material from the central and
western portion towards the eastern Tibetan side as has been suggested
previously by .
The present-day GPS measurements do not necessarily reveal the deformation of
the whole crust but could be associated with deformation of the shallow crust
. Seismic imaging of crustal anisotropy based on receiver
function studies e.g. supports this
argument. The orientation of the GPS velocity vectors and the
ϕs of the direct S waves only match when the orientation of
the different layers of anisotropy within the crust and mantle tend to be
similar. , , , and
discuss the coupling and decoupling of the crust and mantle
by making comparisons among ϕs, GPS, APM, and surficial
features. report a good coherency between anisotropic and
geodetic measurements for the entire southeastern Tibetan region, and on that
basis, they discuss the coupling of the crustal and mantle material as
similarly observed in the northeast Tibetan Plateau
e.g.. The seismic anisotropy directions
that were previously obtained from the inversion of receiver functions,
however, do not suggest vertically coherent deformation of the crust
e.g..
report 4–14 % seismic anisotropy with variable orientations at different
depths. They attribute varying patterns of anisotropic directions to both
fossilized fabric and more recent deformation. The different orientations at
mid and lower crustal levels do not necessarily support a coherent
deformation of the crust and upper mantle.
On the basis of driving forces, two kinematic models have been proposed to
explain the coupling–decoupling of the crust and lithospheric mantle. The
first one is a simple asthenospheric model , proposed
to explain the decoupling of the crust and mantle by the intrusion of a
mechanically weak layer, such as the asthenosphere, into the crust. Whenever
a mechanically weak layer is present in between the crust and mantle, the
force acting on the crustal region cannot be transmitted into the mantle. As
a result, the crust is decoupled from the mantle due to different driving
forces on them. In such models, the velocity difference between the top and
bottom of the mechanically weak layer gives rise to mantle deformation, and
that difference is parallel to the fast polarization direction. The second
model, proposed by , is the vertically coherent model,
and it explains the coupling of the materials within the crust and
lithospheric mantle on the basis of the transmission of the buoyancy forces
from the crust to the mantle. This model requires a rigid lower part of the
crust. In contrast, low shear wave velocity anomalies resolved in various
tomographic studies recently
have
indicated a weak layer in the deeper region of the crust beneath the SE
Tibetan region. It is noteworthy to mention that we avoid making any comment
on the possible linkage between the deformation and coupling of the crust and
underlying lithospheric mantle by only using splitting parameters inferred
from direct S waves and geodetic measurements, and further study is
required.