We use observations of surface waves in the ambient noise field recorded at a
dense seismic array to image the North Anatolian Fault zone (NAFZ) in the
region of the 1999 magnitude 7.6 Izmit earthquake in western Turkey. The NAFZ
is a major strike-slip fault system extending ∼1200 km across northern
Turkey that poses a high level of seismic hazard, particularly to the city of
Istanbul. We obtain maps of phase velocity variation using surface wave
tomography applied to Rayleigh and Love waves and construct high-resolution
images of S-wave velocity in the upper 10 km of a 70 × 30 km
region around Lake Sapanca. We observe low S-wave velocities (<2.5 km s-1) associated with the Adapazari and Pamukova sedimentary
basins, as well as the northern branch of the NAFZ. In the Armutlu Block,
between the two major branches of the NAFZ, we image higher velocities (>3.2 km s-1) associated with a shallow crystalline basement. We
measure azimuthal anisotropy in our phase velocity observations, with the
fast direction seeming to align with the strike of the fault at periods
shorter than 4 s. At longer periods up to 10 s, the fast direction aligns
with the direction of maximum extension for the region (∼45∘).
The signatures of both the northern and southern branches of the NAFZ are
clearly associated with strong gradients in seismic velocity that also denote
the boundaries of major tectonic units. Our results support the conclusion
that the development of the NAFZ has exploited this pre-existing contrast in
physical properties.
Introduction
The formation of fault zones appears to be a balance between
the accommodation of the tectonic strain field and the exploitation of
pre-existing weak zones such as tectonic suture zones or lithological
boundaries (e.g. ; ;
; ). Studying how structural
changes affect strain localization in the upper crust is critical to
understanding the earthquake cycle . Imaging the seismic
velocity structure of fault zones provides information essential to
understanding the long-term behaviour of faults and the earthquakes that
occur on them.
Here we interpret images from ambient noise surface wave tomography of the
upper 10 km of the North Anatolian Fault zone (NAFZ), Turkey, in the rupture
zone of the 1999 Izmit earthquake. This allows us to study the near-surface
structure of a recently ruptured fault. The NAFZ is a ∼1200 km long
strike-slip fault that forms the boundary between the Anatolian block and the
Eurasian continent. Progressively localized since the middle Miocene
(∼ 3 Ma), the NAFZ propagated westward from the Karliova Triple
Junction in eastern Turkey across northern Anatolia and reached the
Izmit–Adapazari region ∼200 ka, although a more broad zone of shear
deformation was present since the middle Miocene . The
motion of Anatolia is driven by a gradient of lithospheric gravitational
potential energy that extends across the Anatolian Peninsula
and is sustained by the collision between the Arabian and
Eurasian plate in the east and the roll-back of the Hellenic trench to the
south-west . Since 1939 a
westward-propagating sequence of large earthquakes (Mw>7.0) has
occurred along the NAFZ . The 1999 Izmit
(Mw 7.6) and Düzce (Mw 7.2) earthquakes are the
most recent in this sequence , and the NAFZ continues to
pose a significant seismic hazard to the region.
In the Izmit–Adapazari region, the NAFZ is split into northern and southern
branches (Fig. ). The northern branch has
seen more seismic activity historically, but microseismicity in this region
does not appear to be strongly localized to the major fault strands
. The northern branch of the fault appears to exploit the
so-called Intra-Pontide suture between the Eurasian continent and sedimentary
accretionary complexes formed during the closure of the Tethys Ocean
. There are three major geological units delineated by the
fault zone (Fig. ). To the north of the
northern branch of the NAFZ is the Istanbul zone, a cratonic fragment of the
Eurasian continent. The Istanbul zone includes the Adapazari basin, a
∼ 2 km thick pull-apart sedimentary basin formed by right-lateral
motion acting on a change in strike of the northern branch of the NAFZ
.
(a) Overview of the Izmit–Adapazari region and the DANA
network. Stations of the DANA network are shown as red triangles; station
names are of the form Dx01 to Dx11, where x is A through
F from west to east and 01 is at the southern end of
each line. Thick black lines identify mapped faults in the region
. The thick red line indicates the extent of the rupture of
the 1999 Izmit and Düzce earthquakes . The epicentre and
focal mechanism for the Izmit earthquake provided by the GCMT catalogue
are shown. Topography data were acquired
by the Shuttle Radar Topography Mission .
(b) Geological map of the Izmit–Adapazari region simplified from
. The locations of the southern and northern branches of
the North Anatolian Fault zone are indicated. The black dashed line shows the
location of the Intra-Pontide suture within the Armutlu Block inferred by
. AB and PB show the location of the Adapazari and
Pamukova basin, respectively.
Located between the two fault branches are the Armutlu Block and the Almacik
Mountains. The Armutlu Block is a section of the Almacik Mountains
that has migrated further westward with motion along the NAFZ. Both are areas
of high topography, formed as an accretionary complex of upper Cretaceous
sediments overlying a metamorphic basement . The dominant
feature of the Armutlu Block is an abundance of metamorphosed sediments and
marbles of unknown age and provenance . The Pamukova
sedimentary basin is located in the southern part of the Armutlu Block
(Fig. ). Striations and down-dip motion on
faults observed along the southern branch of the NAFZ in the Pamukova basin
indicate that extension in the NE–SW direction due to
right-lateral motion is more dominant than shortening in the NW–SE. The
resulting transtensional strain is believed to have caused the opening of the
Pamukova basin . The total thickness of the sediments in the
Pamukova basin is generally unknown, but it is thought to be thinner than in
the Adapazari basin .
To the south of the NAFZ lies the Sakarya Terrane, an accretionary complex of
sedimentary rocks from the Jurassic–lower Cretaceous overlying a metamorphic
basement of mainly Paleozoic rocks . The Sakarya Terrane
also contains a number of ophiolitic melanges, including serpentinites close
to the southern branch of the NAFZ that were probably produced by imbrication
and thrust-stacking during the closure of the Neo-Tethys Ocean
.
To study the structure of the NAFZ in the Izmit–Adapazari region, the
University of Leeds, Kandilli Observatory and Earthquake Research Institute
(KOERI), and Sakarya University deployed a temporary array of seismometers
across the rupture zone of the 1999 Izmit earthquake between May 2012 and
October 2013 . The array, known as the Dense Array for Northern Anatolia,
included 62 three-component
seismometers in a 70 km × 35 km rectangular grid
(Fig. ) and an approximate station spacing
of 7 km.
Also included were three stations of the KOERI national network located
within the main grid of the DANA array: GULT, SAUV and SPNC. DANA was
deployed over both strands of the NAFZ in this region, with stations sited on
all three of the major crustal units described above
(Fig. ).
Short-period surface waves from ambient noise have been used to study the
upper crust in the vicinity of active fault zones in the past (e.g.
; ). In such studies low seismic
velocities have been attributed to earthquake damage zones and pull-apart
sedimentary basins. Here our analysis of the DANA data provides an image of
the top 10 km of the NAFZ in the Izmit–Adapazari region, with a lateral
resolution dictated by the ∼ 7 km station spacing, to better constrain
the relationship between the fault and its regional geological context.
We also interpret first-order observations of azimuthal anisotropy within our
phase velocity measurements. Observations of azimuthal anisotropy in the
upper crust can provide insights into the state of tectonic stress within a
region and potentially the orientation of pervasive mineral fabric and the
structural influence of major faults (e.g. ;
). Such information provided by azimuthal anisotropy is
particularly important in areas such as the North Anatolian Fault, where in
situ stress observations are rare, and extensive deformation occurs off of
mapped faults . Earthquake focal mechanisms
suggest that the direction of maximum compressive stress in the
Izmit–Adapazari region is oriented NW–SE between 120 and 160∘ from
north . If the regional anisotropy is primarily stress
controlled, we would expect the seismic fast direction to be aligned in the
direction of maximum compressive stress due to the preferential closure of
fractures in this direction . However,
used local seismicity to show that the fast polarization direction at
stations close to the ruptured Düzce fault
(Fig. ) are generally parallel to and vary
with the fault strike, suggesting an anisotropy mechanism determined by
deformation fabric. They suggested that the anisotropy is confined to the top
3–4 km of the crust.
Using local seismicity recordings from other stations in the Izmit–Adapazari
region more distant from the ruptured fault, found a more
complex pattern, with the fast polarization directions for at least three of
their stations consistent with the maximum compressive stress direction
(approximately NW–SE). They concluded that anisotropy is limited to depths
less than 8 km. Further east, on the central section of the North Anatolian
Fault system, used teleseismic data to find a coherent
anisotropy signature attributed to mineral fabric within the mantle
lithosphere, in which the fast polarization direction aligns with the
principal extension direction (approximately NE–SW). These results indicate
that stress orientation controls shear wave anisotropy in places, but mineral
fabric dominates in others. By providing further analysis of the regional
anisotropy through surface wave phase velocities, we expect to provide more
observations that can contribute to a better understanding of the various
mechanisms that cause seismic anisotropy in the upper crust.
Data and methodsCalculation of the cross-correlation functions
To image the upper 10 km of the NAFZ we used ambient noise data recorded at
DANA to construct cross-correlation functions and retrieve empirical
estimates of the elastic Green's function of the Earth for all inter-station
paths of the network
. The instruments
used for the DANA network were all three-component broadband sensors, the
majority of which were Guralp CMG-6TDs (30 s maximum period). Some stations
were equipped with CMG-3Ts or CMG-3ESPs (120 s maximum period). From these
cross-correlation functions we extract surface wave dispersion curves in
order to perform seismic tomography and invert for S-wave velocity structure
. The data were first reduced to a 25 Hz sampling rate
and corrected for the instrument response. An initial bandpass filter was
applied between 0.02 and 10 Hz, and the frequency spectrum of each noise
window was whitened between 0.05 and 2 Hz . We tested
several preprocessing methods for producing the cross-correlation functions
for this study. These included the trial use of 4 and 1 h long noise
windows. In order to remove any data windows containing signals from large
earthquakes, each window was split into three segments. If the amplitude of
one of these segments has a significantly higher standard deviation (> 1.8
times) than the other two, the data window is discarded . For
amplitude normalization , we tested 1-bit normalization
against clipping any data with an amplitude > 3.5 times the standard
deviation of each data window. Figures S1 and S2 in the Supplement show the
results of these tests. We found little difference between the processing
schemes in terms of the signal-to-noise ratio of the final cross-correlation
functions. However, the approach of amplitude clipping for 4 h long noise
windows was found to produce correlation functions with a slightly higher
frequency domain coherence than the other schemes. As such, we selected this
preprocessing method.
Record section of correlation functions calculated for inter-station
paths of the DANA network. Correlation functions were filtered between 0.05
and 2.0 Hz and binned and stacked in 0.5 km distance bins, and the
amplitude is normalized within each bin. Record sections for every
combination of three-component motion are labelled as follows: Z – vertical,
R – radial, T – transverse. The ZR correlation (bottom left) represents the
motion recorded on the radial component due to a vertical point source. ZZ,
ZR, RR and RZ components show Rayleigh waves, and TT shows Love waves.
Following this preprocessing, each data window is cross-correlated with the
corresponding window at every other station in the network, and these
cross-correlations are then stacked over the entire duration of the array
deployment (16 months of data). We calculated the correlations for all nine
possible combinations of the vertical, north and east components of ground
motion and then rotated the final stacked correlations into the relevant
great circle path (station to station) to retrieve the vertical, radial and
transverse correlation components
(Fig. ). The correlation functions in
Fig. are stacked in bins of 0.5 km
inter-station distance and bandpass filtered between 0.05 and 2.0 Hz. The
amplitudes are normalized within each bin.
Extraction of surface wave phase velocities
The record sections exhibit multiple features and arrivals. There are two
explanations for the large-amplitude features around t=0. Firstly, they
may represent the signature of the overlapping converging and diverging
surface waves to form focal spots in the wave field . A
second possible explanation is teleseismic body wave energy that arrives at
the stations at a near-vertical incidence angle. When these arrivals are
cross-correlated, the very small differential travel times of the energy
result in large amplitudes near the zero lag correlation time
. The large amplitudes are particularly
prominent on the ZZ component. This phenomenon has been observed in a
previous ambient noise study in Turkey: observed large
zero-time amplitudes in their correlation functions up to a distance of
80 km. Additionally, large-amplitude waveforms near t=0 are often
observed in ambient noise correlation studies (e.g. ;
; ) and are typically left
uninterpreted. While these waveforms can be used for imaging (e.g.
; ), we focus here on the
propagating surface waves that dominate the record sections. Correlations
between the vertical and radial components (ZZ, ZR, RR and RZ) predominantly
contain Rayleigh waves propagating between DANA stations, whilst the
transverse (TT) correlations contain Love waves.
Figure shows some evidence for
cross-talk between vertical and transverse components (ZT and TZ) in the form
of low-amplitude coherent waves, perhaps indicating the effects of anisotropy
or the scattering of waves off 3-D Earth structures. Linear arrivals that are
most prominent at arrival times of ±10 s may represent body wave
reflections contained within the ambient noise, but may also be an artefact
produced by the GPS time synchronization of the seismic instruments
.
To create phase velocity dispersion curves for the study region, we first
create group velocity–period diagrams for each stacked
correlation function between periods of 1.0 and 10.0 s (Fig. S3 in the
Supplement) using the programme do_mft. We
then pick the dispersion curve for each correlation function manually. Due to
a poorer signal-to-noise ratio on the ZZ component, Rayleigh wave dispersion
measurements are picked from the RR component correlations, whilst Love wave
measurements are picked from the TT component. Examples of period–group
velocity maps used for picking the dispersion curves are shown in Supplement
Fig. S3. suggest that in order for dispersion measurements
to be considered reliable, the station separation must be greater than 3
wavelengths of the target wave. If we assume an average phase velocity of c=3 km s-1 for the upper crust, our shortest-period surface waves of
1.5 s will have a wavelength of 4.5 km. Thus, in order to satisfy the
wavelength criterion, we discard all measurements with an inter-station
distance of 13.5 km or less as unreliable. For longer periods and
inter-station distances, for which some of the short-period data may be
trustworthy, unreliable long-period measurements are discarded based on
visual inspection. This also ensures that the large amplitudes of the
near-zero arrivals do not contaminate our measurements from the
later-arriving surface waves. We use 62 stations in this study, which amounts
to a total of 1891 unique station pairs. As a result of the wavelength
criterion, coupled with the visual inspection of each period–velocity map,
we retain measurements from 929 station pairs for Rayleigh waves (49 % of
the RR correlations) and 1173 station pairs for Love waves (62 % of the
TT correlations).
Phase velocity dispersion curves are also picked using do_mft. The phase velocity at each period is calculated from
the previously picked group velocity by
c=ω0r-Φ+π4+ω0rU0+N2π,
where Φ is the instantaneous phase of a narrow bandpass-filtered surface
wave, ω0 is the centre frequency of the bandpass filter, r is the
inter-station distance, U0 is the group velocity and N is some integer.
The N2π term in Eq. () introduces an ambiguity in
the calculation of the phase velocity. To overcome this ambiguity,
do_mft uses Eq. () to
generate a suite of dispersion curves corresponding to different values of
N. To pick the correct phase velocities, we calculate the theoretical
dispersion curve using an a priori seismic velocity model of the region
and manually pick the calculated dispersion curve
(Eq. ) that most closely corresponds to the
theoretical dispersion curve.
Phase velocity tomography
After picking phase velocity dispersion
curves for all inter-station pairs for both Rayleigh and Love waves, we
convert the phase velocity at each period into a travel time between the
stations. We then use these travel time observations to invert for phase
velocity as a function of position at each discrete period. We discretize
each model as a 2-D grid of phase velocity nodes. The phase velocity
tomography is carried out in a spherical coordinate system
, with the node spacing (6.6 km in latitude and 7.6 km
in longitude) comparable to the average horizontal separation of the stations
of the DANA network. We begin each inversion with a constant velocity model,
with the velocity set to the average observed phase velocity at the given
period. We then invert the travel times for periods between 1.5 and 10.0 s
using the method of . This is an iterative inversion,
with each step consisting of calculating travel times through the current
phase velocity model by wave-front tracking using the fast marching method
. The inversion then seeks to minimize the objective
function:
g(m)-dobs2+ϵm-m0Tm-m0,
where g(m) represents the travel times through the current model,
dobs represents the observed travel times from our dispersion
data, ϵ is a variable damping factor, and m and m0
represent the current model and the starting model, respectively. The
variable damping term is included in order to minimize unconstrained model
parameters (phase velocities) by preventing them from straying too far from
our initial constant velocity model. The choice of damping parameter,
ϵ, is somewhat subjective. It should be selected with the aim of
achieving a balance between the variance of the perturbations in the final
phase velocity model with respect to the initial model (a high variance
indicates unrealistic values for unconstrained model parameters) and
obtaining a satisfactory misfit to the observed travel time data. We
constructed trade-off curves (Supplement Fig. S4) of final model perturbation
variance vs. final data misfit for both the Rayleigh and Love wave
inversions. We selected a damping factor of 40 s4 km-2 for
Rayleigh waves as it provided a 68 % reduction in the perturbation
variance of the final model parameters (0.025 to 0.008 (km
s-1)2) for only a 2 % increase in data
misfit (795 to 815 ms) at a 4 s period. Likewise, for Love waves we choose
a damping parameter of 60 s4 km-2, which provides a 75 %
reduction in final model variance (0.055 to
0.014 (km s-1)2) for an 8 % increase
in misfit (670 to 730 ms). Increasing the damping parameter above these
values leads to an increase in misfit to the observed data which we find
unacceptable. These constant damping factors are applied to the inversions at
every period (Figs. and
).
Rayleigh wave phase velocity maps at 2.0, 3.0 and 5.0 s periods.
Black lines show the mapped faults. The blue line represents the Sakarya
River flowing towards the north.
Love wave phase velocity maps at 2.0, 3.0 and 5.0 s periods. Black
lines show the mapped faults. The blue line represents the Sakarya River
flowing towards the north.
We do not include a separate smoothing parameter in our inversion scheme, as
a similar effect can be obtained by simply reducing the number of model
parameters and controlling the inversion through a damping parameter as
described above . We have designed our model
discretization so that our velocity node separation is comparable to our
station separation, which should be a sufficiently coarse parameterization to
constrain all our model parameters and produce a smooth final model.
The minimization of the objective function is performed using an iterative
subspace inversion approach , which projects the objective
function onto a multidimensional subspace of the data and model parameters.
After 10 iterations the data misfit does not improve appreciably with further
iterations, and the inversion is judged to have converged. Stable solutions
are shown in Figs. and
for periods of 2.0, 3.0 and 5.0 s.
S-wave velocity inversion
After obtaining 2-D maps of phase
velocity for all periods between 1.5 and 10.0 s, the resulting dispersion
relation at each node on the same geographic grid was inverted to obtain
isotropic S-wave velocity as a function of depth at that location. Both
Rayleigh and Love wave dispersion data are inverted together, with equal
weighting, in order to obtain an S-wave velocity model that best satisfies
both data sets. The initial inversion was performed using a neighbourhood
algorithm parameterized by a model
consisting of 10 layers with variable layer thickness and S-wave velocity.
The total number of free parameters is 20. The S-wave velocity of each layer
is permitted to vary with a uniform distribution between 0.5 and
4.5 km s-1, whilst layer thickness could vary between 0.5 and 1.5 km.
An increase in S-wave velocity with layer depth is also prescribed. The
neighbourhood algorithm was allowed to run until 20 050 different S-wave
velocity models had been generated for each node in the grid. The misfit
parameter at each location is defined for the neighbourhood algorithm as
ϕm=∑i=1nfvdi-vmi2vdi2nf,
where nf is the number of frequencies in the dispersion curve,
vdi is the observed phase velocity at frequency i from our
tomographic model and vmi is the phase velocity at that
frequency inferred from the inverted S-wave model. Models that fit the
dispersion curves extracted from the phase velocity tomography with
ϕm<0.25 (Eq. ) were used in a
weighted average to construct an initial estimate for S-wave velocity vs.
depth. Examples of the distribution of models used in the weighted average at
three grid points, one each in the Sakarya Terrane, Armutlu Block and
Istanbul zone, are shown in Fig. .
The weighting of each model is the inverse of its misfit to the dispersion
data as described in Eq. ().
Results of the neighbourhood algorithm inversion for S-wave velocity
at three nodes in the different geological units
(Fig. ). The grey region represents the
range of accepted models with a misfit below 0.25
(Eq. ). The coloured region shows the range of the
1000 models with the lowest misfit. Red colours indicate a higher number of
the best 1000 models with a certain S-wave velocity at that depth. The solid
red line shows the best-fitting model, the misfit of which is shown at the
bottom of each panel. The location of each of these nodes is shown in
Fig. .
This average model was then used as the starting model for a linearized
iterative inversion scheme as implemented in surf96. The inversion was judged to have converged once the
root mean square change in the S-wave velocity model between iterations was
negligible (<0.1 km s-1), usually within
six iterations. The set of 1-D models obtained from the linearized inversion
represents our 3-D S-wave velocity model for the region.
The advantage of the neighbourhood algorithm is that it provides a much
broader overview of the acceptable parameter space for our S-wave velocity
model, rather than inverting for a single model that best fits the data. The
output of the neighbourhood algorithm
(Fig. ) also allows for an intuitive,
if qualitative, understanding of potential uncertainty in our final S-wave
velocity model. A disadvantage of the neighbourhood algorithm is that only a
relatively small number of model parameters can be included in the inversion
(∼30) before the parameter space becomes too large to search
efficiently . This means that the neighbourhood
algorithm can only constrain relatively simple models. For these reasons, we
present the results of the neighbourhood algorithm
(Fig. ), but also perform a
linearized inversion to obtain a final model that better
fits the data overall. This approach has been used previously in fault zone
imaging and attempts to strike a balance between
presenting a model that satisfies the data and gives a broader overview of
the acceptable model space that is not available when using only a linearized
inversion scheme.
Results
In this section, we describe the phase velocity maps derived separately for
Rayleigh and Love wave travel time data. Sensitivity kernels representing the
vertical resolution for Rayleigh and Love waves within our period range can
be found in the Supplement (Fig. S8), along with synthetic checkerboard
recovery tests to illustrate the horizontal resolution of the inversion
(Figs. S9 and S10). The initial and final data misfit of the tomography
models for both Rayleigh and Love wave phase velocities are shown in
Supplement Figs. S5 and S6. The significant reduction in the variance of the
travel time residuals in the final models, on average about 50 %,
indicates that the final models better account for structural heterogeneity.
Similarly, the higher variance of the final travel time residuals at shorter
periods indicates stronger heterogeneity at shallow depths or noisier phase
velocity measurements at these periods.
Rayleigh wave phase velocity
Figure shows the results of the Rayleigh
wave phase velocity tomography for periods between 2.0 and 5.0 s. The most
interesting features of the velocity model include the large low-velocity
(1.5–2.0 km s-1) anomaly located north of the northern branch of the
NAFZ. These low velocities are likely due to the deep sedimentary basin at
Adapazari in the north-eastern part of the model and heavily faulted
sediments near Izmit in the north-western sector . Between
the two fault strands, the Armutlu Block can be seen as a prominent region of
high phase velocity (∼3.0 km s-1), likely associated with the
metamorphic rocks and possible granitic intrusions that exist in this region
. At 2.0 and 3.0 s periods, this high-velocity
region is particularly prominent in the western part of the Armutlu Block
(Fig. ). At a 5.0 s period, the entire
Armutlu Block consists of high velocities. At a 2.0 s period, the sediments
of the Pamukova basin can be seen along the southern branch of the NAFZ with
velocities of approximately 2.0 km s-1. To the south, in the Sakarya
Terrane, a relatively high-velocity anomaly (faster than 2.5 km s-1)
can be seen at all periods greater than 2.0 s. These velocities are in
general higher than those observed in the part of the Istanbul zone that
bounds the fault, and they likely indicate the crystalline basement of the
Sakarya Terrane at shallower depths, with thinner sedimentary cover. It is
likely that the high phase velocities observed in the far north of the model
correspond to the older sedimentary units and crystalline rocks of the
Istanbul zone that underlie the clastic sediments at Izmit and Adapazari
. In general, at a 5.0 s period or lower, the contrast in
phase velocity between the major tectonic units is relatively low. This is
likely due to the longer wavelength of these waves, which will average
lateral variations in structure at these larger periods.
Love wave phase velocity
The Love wave phase velocity images (Fig. )
show a very similar pattern to the Rayleigh wave images. To the north of the
fault extremely low (∼1.2 km s-1) phase velocities are
associated with the faulted sediments near Izmit, as well as the Adapazari
basin. Both of these features are visible for periods <5.0 s. Low
velocities also seem to be strongly associated with the NW–SE-striking
faults just north of the rupture zone of the Izmit earthquake at
40.7∘ N and 30.45∘ E. Focal mechanisms for earthquakes in
this region show examples of normal faulting , indicating
these low velocities could be associated with a releasing bend on the
northern branch. The Armutlu Block between the two fault strands shows high
phase velocities exceeding 2.4 km s-1, which is comparable with those
of the Rayleigh wave images. The Pamukova basin can be seen for periods <5.0 s near the southern branch of the fault with velocities of
1.5–2.5 km s-1. At a 5.0 s period, higher phase velocities
(>3.0 km s-1) are observed within the southern portion of the
Sakarya Terrane and the northern part of the Istanbul zone. These high
velocities are again interpreted to represent the crystalline basement of
these tectonic units. As with the Rayleigh wave phase velocity maps
(Fig. ), the lateral resolution of the Love
wave images decreases with increasing period.
S-wave velocity model misfit
In order to construct an isotropic S-wave velocity profile at each node a
two-step inversion process was chosen, as described in
Sect. . Examples of the results of the
neighbourhood algorithm from three locations in the Sakarya Terrane, Armutlu
Block and Istanbul zone are shown in
Fig. . The best 1000 models from the
neighbourhood algorithm occupy a much smaller range for the Sakarya Terrane
and Armutlu Block examples. The broader range for the Istanbul zone example
shows that the data here provide weaker or possibly conflicting constraints
on the model velocity profile. To improve the data misfit in such cases, a
linearized inversion approach with surf96 is
used to find an optimum model. Supplement Fig. S7 shows the final fit of the
dispersion curves calculated at each of the nodes shown in
Fig. . The dispersion curves were
calculated for the final S-wave velocity model and compared to dispersion
curves extracted from the Rayleigh and Love wave phase velocity tomography.
Supplement Fig. S7 also summarizes the improvement in the misfit to the
dispersion data provided by employing the linearized inversion
after the neighbourhood algorithm. Each node has a
significant improvement in misfit following the linearized inversion
(>50 %).
Isotropic S-wave velocity maps
Figure shows depth slices through the
final S-wave velocity model at depths of 1.5, 3.5 and 5.5 km. The final
S-wave velocity model is produced by performing a minimum curvature
interpolation between our model nodes, which have the same spacing as our
phase velocity model (Sect. ). In the top
3 km of the crust we observe low S-wave velocities (1.6–2.0 km s-1)
on the north side of the northern fault strand, associated with the Adapazari
basin and faulted sediments near Izmit. These low S-wave velocities are not
observed at model depths of 3.5 km and below
(Fig. ), indicating that the Adapazari
basin is likely not deeper than about 3.5 km. At 5.5 km of depth, relatively
low S-wave velocities (2.8 km s-1) are clearly associated with the
northern branch of the NAFZ, particularly within the zone of the Izmit
rupture beneath Lake Sapanca at 40.7∘ N and 30.2∘ E. Faster
S-wave velocities, up to 3.5 km s-1, are observed within the Armutlu
Block between the two strands of the NAFZ. As with the phase velocity maps,
these high velocities are more prominent west of the Sakarya River to a depth
of about 3.5 km. The slow velocities associated with the Pamukova basin
along the southern branch of the NAFZ are much attenuated at 3.5 km of depth,
indicating that this basin is shallower than the Adapazari basin. We observe
evidence in the southern part of the model for crystalline rocks below a
depth of 1.5 km in the Sakarya Terrane, where S-wave velocities exceed
2.5 km s-1. These high velocities are also observed in the far north
of the model within the Istanbul zone. Both the northern and southern
branches of the NAFZ appear to exploit the regions where we observe high
gradients in seismic S-wave velocity. Both branches of the main fault skirt
the edges of the high-velocity zone associated with the Armutlu Block.
Isotropic S-wave velocity maps at 1.5, 3.5 and 5.5 km of depth.
Black lines show the mapped faults. The blue line represents the Sakarya
River flowing towards the north. The black squares represent the locations of
the nodes shown in Fig. .
Isotropic S-wave velocity vertical profiles
Figure shows two vertical sections
through the S-wave velocity model along a north–south profile located at
30.2∘ E (profile A–A′) and 30.4∘ E (profile B–B′). In
profile A–A′ the low-velocity zone associated with the heavily faulted
sediments near Izmit (40.82∘ N) can be observed to a depth of ∼3.5 km, as can the Adapazari basin along profile B–B′. In profile
A–A′ the Armutlu Block is clearly distinguishable as a region of high
velocity (∼2.8 km s-1) extending towards the surface between
40.5 and 40.6∘ N. It is clear that high-velocity metamorphic rocks
found in this region are located closer to the surface
than the basement rocks of the Sakarya Terrane and Istanbul zone. In both
profiles, a zone of low velocity (∼2.8 km s-1) can be seen
extending to a depth of at least 6 km beneath the location of the surface
expression of the northern branch of the NAFZ. This low-velocity zone appears
to be of the order of 10 km wide (40.65 to 40.75∘ N). Low
velocities associated with the southern branch of the fault zone are less
clear, particularly for the eastern profile B–B′, but are evident to 5 km
of depth beneath profile A–A′. However, it is difficult to distinguish the
southern branch of the fault from the surrounding sedimentary cover of the
Sakarya Terrane and Pamukova basin.
(a) Map of the Izmit–Adapazari region
showing station locations of the DANA
network as red triangles and mapped faults as black
lines. Thick red lines indicate the location of the vertical profiles taken
through the S-wave velocity model along lines A–A′ and B–B′.
(b) Vertical S-wave velocity profile between A and A′.
(c) Vertical S-wave velocity profile between B and B′. The
profiles show S-wave velocity between the surface and 9 km of depth.
The approximate locations of the surface traces of the northern and southern
branches of the NAFZ are indicated by NNAF and SNAF, respectively.
Azimuthal anisotropy
In order to
quantify the level of azimuthal anisotropy in our phase velocity data set, we
plot our raw phase velocity measurements against the azimuth of the
propagation direction (from north). To reduce the scatter in the data and
provide a meaningful measurement, we bin all of our phase velocity
measurements by azimuth with a bin size of 5∘. The phase velocities
within each bin are averaged to provide a mean measurement and a
corresponding standard error. Rayleigh and Love wave observations are treated
separately. Due to the presumed symmetry of propagation velocity in both
directions between pairs of stations, our measurements are in an azimuth
range of 0 to 180∘. We attempt to fit the binned data at each period
with the following function to describe the azimuthal variation of phase
velocity :
c(θ)=u0+Acos(2(θ-ϕ2))+Bcos(4(θ-ϕ4)),
where u0 is the average (isotropic) phase velocity. A is the amplitude
of the 2θ term, which describes an azimuthal variation with
180∘ periodicity. ϕ2 is the fast direction of the 2θ
term. B is the amplitude of the 4θ term, which has 90∘
periodicity, and ϕ4 is the corresponding fast direction.
Azimuthal variation of Rayleigh wave phase velocities with
propagation azimuth (from north). Black dots indicate the raw phase velocity
measurements, and large red dots show the average of the phase velocities
within 5∘ azimuth bins and the corresponding standard error of the
mean for the bin. The blue line is the best-fitting curve
(Eq. ) to the binned data (red
dots). u0 is the average (isotropic) phase velocity. We show the root mean
square misfit of the blue curve to the phase velocity measurements, as well
as the variance of the residuals. We indicate the 2θ and 4θ
amplitudes and fast directions that correspond to the blue curve. The
azimuthal distribution of ray paths used in this analysis is shown in
Supplement Fig. S14.
The azimuthal variation of the raw Rayleigh wave phase velocity measurements
between 2.0 and 8.0 s periods is shown in
Fig. . Figure
shows the variation of fast direction and magnitude of anisotropy for all
periods between 1.5 and 10.0 s. Although there is considerable variability
in the individual phase velocities, there is a robust dependence of phase
velocity on propagation direction that is observed when averaging velocities
in 5∘ azimuth bins. Figure shows a smooth
variation in the fast direction with an increasing period of the wave. At
short periods (2–3 s) the fast direction is aligned close to 90∘
from north, but changes smoothly to ∼50–70∘ from north above a
5 s period. Below 2 s periods, the anisotropy has a magnitude greater than
1 %, but this magnitude decreases substantially between 2 and 4 s
periods, before increasing again at periods greater than 4.0 s to a value of
∼3 %.
In general, the amplitude of the 4θ term is at least 50 % lower
than the 2θ term, which is to be expected for Rayleigh waves
. The exception to these trends is at 2.0 s periods. Here,
the fast directions do not align with those observed at longer periods, and
the 4θ component has twice the amplitude of the 2θ component.
However, both the RMS misfit and the variance of the residuals between the
observed data and Eq. () are much
greater at 2.0 s periods, as is the case with the phase velocity
tomography. In particular, the greater variance of the residuals implies a
greater uncertainty in the data fit. Greater variance in the 2.0 s phase
velocities is likely due to the fact that waves at 2.0 s periods are more
sensitive to short-wavelength heterogeneities near the surface.
Variation of 2θ Rayleigh wave anisotropy with period in the
Izmit–Adapazari region. The red dots are the measured magnitude of
anisotropy at each period, and the corresponding uncertainty is the standard
deviation of the anisotropy magnitude taken from the covariance matrix during
the curve-fitting process described in
Sect. . The black lines indicate the angle
from north of the 2θ fast direction at each period, and the top of the
plot represents north.
A further source of uncertainty in our calculation of azimuthal anisotropy is
the unknown noise source distribution of the region. It is clear from the
azimuthal distribution of our phase velocity measurements (Figs. S17 and S18
in the Supplement) that there is a possible bias due to the number of ray
paths that are oriented north–south. Fewer observations are available for
ray paths that are not aligned in the dominant direction, leading to higher
uncertainty in our measurements of anisotropy. This effect is visible in
Fig. : measurements taken from
east–west-oriented ray paths (∼90∘) generally display a higher
standard error of the mean than those for north–south-oriented ray paths (0
or 180∘).
The azimuthal anisotropy of the Love wave phase velocities is shown in
Supplement Fig. S13. The Love wave anisotropy is less clear. In general, the
2θ fast direction lies between 25 and 40∘ from north. The
4θ fast direction is more variable, mostly lying between 85 and
120∘. The average amplitude of the 2θ term is
0.036 km s-1. Whilst the amplitude of the 4θ term is more
comparable in amplitude to the 2θ term than for the Rayleigh waves, it
is still consistently smaller, with an average of 0.024 km s-1. The
RMS misfit and variance of the residuals are again higher at the shorter
periods of 2.0 and 4.0 s, again indicating
sensitivity to shorter-wavelength structural complexities near the surface.
The azimuthal distribution of ray paths used in this analysis is shown in
Supplement Figs. S14 and S15.
DiscussionS-wave velocity model
The horizontal resolution of the S-wave velocity model at depth in
Fig. is limited by the wavelength of
the surface waves used in this study. Receiver function and autocorrelation
studies of the region show that the shear zone associated with the NAFZ is
perhaps no wider than ∼7 km through the crust and into the upper
mantle . In the upper crust, the main fault
strands are estimated to be no more than a few kilometres wide in this region
. Low S-wave velocities associated with the northern branch
of the NAFZ are observable in our model down to a depth of 6 km. Below this
depth, we rely on observations derived from Rayleigh waves with a period
greater than 8.0 s (phase velocity sensitivity kernels in Supplement
Fig. S8). Assuming a phase velocity of 3 km s-1, these waves have a
wavelength of ∼24 km. Thus, we cannot expect to resolve such a narrow
structure at depth unless it offsets rocks of differing seismic velocity. In
the Supplement (Fig. S9), we include the resolution kernels of the final
S-wave velocity models at the three locations specified in
Fig. .
Our tomographic models show that both the northern and southern branches of
the NAFZ have exploited boundaries between major lithological units. In
particular, the metamorphic rocks of the Armutlu Block are clearly mapped due
to the strong velocity contrast with rocks of the Istanbul zone to the north
and the Sakarya Terrane to the south
(Figs. ,
and ).
Seismic velocity models of the crust in this region have also been
constructed from teleseismic body wave tomography by
. They image depth-averaged seismic velocity
between the surface and 90 km of depth, with a vertical and horizontal
resolution of ∼15 km . Despite the
large difference in model resolution and a non-overlapping depth range,
detect reduced crustal seismic velocities
immediately to the north of the NAFZ, in the same regions we observe low
S-wave velocities associated with the Adapazari basin, and heavily faulted
sedimentary cover in the north-western part of the array
(Figs. and
). Low P-wave velocities observed by
are also co-located with the low S-wave velocities
detected in this study beneath the Pamukova basin.
also found relatively high seismic velocity
at depth within the Armutlu Block. We detect high S-wave velocities much
closer to the surface that we attribute to the shallow metamorphic rocks
reported in this region . We note that the relatively high
seismic velocities we find in the upper crust of the Armutlu Block also
correspond to a region of relatively low electrical resistivity found by
in the upper 10 km.
The depth of sedimentary cover of the Adapazari basin has been estimated to
be at least 1.0 km in some locations . These estimates
were made by inverting Rayleigh wave phase velocity measurements from
microseisms recorded at two arrays within the basin. Due to a lack of
measurements below 0.6 Hz (>∼1.6 s period) the inversion of
assumed an S-wave velocity of 3.5 km s-1 below a
depth of 500 m in the basin. Our velocity model, which incorporates Rayleigh
wave observations up to a 10.0 s period, indicates that S-wave velocity may
be no greater than 3.0 km s-1 up to a depth of 2.5 km within the
basin. Our measurements therefore imply that the Adapazari basin could have a
depth of up to 2.5 km based on the observed increase in S-wave velocity at
this depth. Similarly, the Pamukova basin may be as deep as 2.5 km, though
it is difficult to accurately detect the depth to material interfaces using
only surface wave observations.
Studies of the near-surface structure of the San Jacinto Fault zone in
southern California (; ) observe
prominent “flower structures” associated with the fault. These structures
are zones of low seismic velocity that are wide near the surface, become
narrower with depth and are interpreted to be a damage zone created during
fault propagation through undeformed crust. The surface wave analysis does
not enable us to observe a narrowing with depth of the low-velocity zone
associated with the northern branch of the NAFZ in
Fig. . Nonetheless, the low-velocity
anomalies associated with the Adapazari and Izmit regions might be
interpreted as crust that has been damaged by movement on and around the
northern strand of the fault. It is clear that the strongest contrasts in
seismic velocities in our model (Figs. ,
and )
are associated with boundaries among the three main tectonic units. The North
Anatolian Fault zone appears to have developed along pre-existing tectonic
boundaries.
Such seismic velocity contrasts across an active strike-slip fault are also
present in California on the creeping section of the San Andreas Fault to the
north of Parkfield where the fault trace is located along a strong seismic
velocity contrast between the Great Valley sedimentary sequence and the
granites of the Salinian terrane .
This phenomenon is also observed across the Hayward fault near San Francisco
where there is a clear seismic velocity contrast between the Great Valley
sequence and the Franciscan Complex .
suggest that the San Andreas Fault is likely to
creep in sections in which this clear velocity contrast exists, whilst being
locked and rupturing seismogenically where the velocity contrast across the
fault is less defined. However, this association between a creeping fault
segment and a clearly defined velocity contrast evidently does not hold for
this section of the NAFZ where the 1999 Izmit and Düzce earthquakes
occurred. Furthermore, a recent geodetic study found evidence of only low
creep rates on this segment, probably related to earthquake after-slip at
shallow depths .
The relatively high S-wave velocities we observe within the Armutlu Block
likely indicate metamorphic rocks and pre-Jurassic basement
, the surface outcrops of which are of unknown provenance
and age . This metamorphic unit within the Armutlu Block is
evidently resistant to strain, which is deflected onto the northern and
southern branches of the NAFZ that bound this high S-wave velocity region.
This behaviour is also observed in the near-surface structure of the
south-eastern section of the Alpine Fault on South Island, New Zealand, where
the fault trace is located at the edge of the metamorphic Haast Schist and
cuts through thick coastal sediments .
image the S-wave velocity structure of the upper mantle
beneath the NAFZ using full waveform inversion. At this much larger length
and depth scale, they also note that the NAFZ appears to be bounded by
tectonic blocks of high seismic velocity. They interpret this as evidence
that the fault zone developed along the edges of high-rigidity blocks,
analogous to our observations for the near-surface structure of the Armutlu
Block.
Azimuthal anisotropy
The 2θ and 4θ fast directions for Rayleigh waves vary between
50 and 90∘ from north (Fig. ), whilst Love
wave 2θ fast directions vary from 20 to 40∘ from north
(Fig. S13). The Love wave 4θ fast direction is highly variable, with
no distinct pattern that can be readily observed.
Our observations of azimuthal anisotropy are complementary to the
observations of previous studies along the North Anatolian Fault. Two studies
of shear wave splitting measurements of the Karadere–Düzce segment (∼50 km east of the current study region) by also
display a seismic fast direction in the upper crust that clusters between 45
and 90∘ from north, often aligning parallel to the strike of the
North Anatolian Fault. Further shear wave splitting measurements made by
at the station CAY, located within our study region to the
east of Lake Sapanca (Fig. ), also showed
fast directions between 30 and 90∘, with the majority falling between 40
and 50∘. Further east, the fast polarization directions measured by
are more commonly aligned NW–SE.
There are two possible mechanisms of crustal anisotropy: stress controlled or
structure controlled. If the anisotropy is stress controlled, it is expected
that the fast direction will align with the direction of maximum horizontal
compression in the stress field due to the closure of cracks on the
perpendicular direction . For an east–west-striking
fault, this would result in an expected fast direction aligned NW–SE, or
120–160∘ from north . Our observations, and
those of previous studies , show that this is not
the case, at least for stations located close to the fault. A dominant fast
direction between 50 and 90∘ (NE–SW) from north
(Fig. ) indicates that the anisotropy in the region
is likely structure controlled. This observation was also noted in
anisotropic receiver functions by , who found that the
fast shear wave polarization directions along the central portion of the
North Anatolian Fault align with the strike of mapped faults at stations
located close to those faults, implying structure-controlled anisotropy.
Figure shows a nearly 90∘ fast direction at
a 2–3 s period (depths of ∼0–3 km) that aligns approximately with
the strike of the North Anatolian Fault through the region. This observation
clearly implies structure-controlled anisotropy that is dominated by faulting
in the very upper crust, similar to the observations of
for the top 15 km of the central section of the North Anatolian Fault. At
periods greater than 4.5 s (Fig. ), our observed
fast direction does not systematically align with any of the mapped faults in
the region (Fig. ). Instead, the fast
direction at these periods is better compared to the 45∘ direction of
maximum extension for the Izmit–Adapazari region calculated from
inter-seismic GPS data by , and it is consistent with
shear wave splitting measurements from the central portion of the North
Anatolian Fault made by , who found a fast polarization
direction that varied between 35 and 60∘. Further analysis of shear
wave splitting results by show an average fast direction
of ∼60∘ down to a depth of about 30 km.
This close correspondence between the seismic fast direction and the
direction of maximum extension implies that structure-controlled anisotropy
is the result of mineral foliation within the crust. Some minerals in upper
crustal rocks, such as micas and amphibole, typically have cleavage planes or
crystallographic axes aligned with the dominant strain direction and are the
dominant source of anisotropy within the bulk rock (e.g. ;
; ). These minerals are
particularly common in high-grade metamorphic rocks, such as slates and
schists, and are likely abundant within the Armutlu Block. Analyses of
samples of calcite and amphiboles taken from the Uludag Massif (∼100 km south-west of Izmit–Adapazari) by show that the
fast propagation for both P and S waves aligns parallel to the foliation
direction in these minerals. We therefore think it likely that the seismic
fast directions we observe at longer periods are determined by deformation
fabrics aligned with the dominant shear regime.
Conclusions
We utilized the ambient noise field recorded at a temporary
network in the Izmit–Adapazari region of north-western Turkey to retrieve
Rayleigh and Love waves propagating between the stations of the array. We
performed surface wave phase velocity tomography, followed by an inversion
for S-wave velocity structure, with waves of periods from 1.5 to 10.0 s to
image the shear wave velocity in the top 10 km of the North Anatolian Fault
zone.
Our model shows low S-wave velocity to the north of the NAFZ, associated with
faulted marine clastic sediments near Izmit and with the
Adapazari sedimentary basin, which we estimate to have a thickness of at
least 2.5 km. Between the two branches of the NAFZ, we observe a
high-velocity region linked to metamorphic and igneous rocks in the Armutlu
Block. It is likely that this high S-wave velocity in the upper crust is
indicative of a rheologically strong region that preferentially localizes
strain at the boundaries of the Armutlu Block, particularly along its
northern boundary, which has been identified as the Intra-Pontide suture
zone. We also image the Pamukova basin as a region of low S-wave velocity to
a depth of about 2.5 km associated with the southern branch of the NAFZ.
Both basins are likely related to pull-apart motion along the northern and
southern branches of the NAFZ, where they are oblique to the principal shear
direction.
To the south of the NAFZ, we image the Sakarya Terrane as a region of
moderate to high S-wave velocity, consistent with the Sakarya Terrane being
an accretionary complex of sedimentary rocks overlying a metamorphic
crystalline basement . Our analysis of the azimuthal
variation in phase velocities finds that regional seismic anisotropy is
likely structure controlled. At short periods, both Rayleigh and Love waves
have a fast direction which roughly aligns with the strike of the North
Anatolian Fault (east–west), as opposed to the direction of maximum
compression (NW–SE). At longer periods (>4.0 s), the fast direction
smoothly transitions from the maximum shear direction towards the principal
extension direction of the lithosphere (NE–SW), indicating that mineral
fabric may be the source of azimuthal anisotropy. Studying the relationship
among the three distinct tectonic units of the region, including the patterns
of seismic anisotropy, provides insight into the potential for strain
localization along both the northern and southern branches of the NAFZ. This
knowledge is critical to understanding the long-term behaviour of the fault
zone and the seismic hazard that it poses.
Data availability
The final S-wave velocity model of the Izmit–Adapazari
region is included as an ASCII text file within the Supplement.
Data for this study can be found at the IRIS Data Management Centre under
network code YH (2012–2013) .
The supplement related to this article is available online at: https://doi.org/10.5194/se-10-363-2019-supplement.
Author contributions
GT performed the formal analysis, with the
exception of the calculation of the raw cross-correlation functions, under
the supervision of SR, GAH and GH. GH calculated
the raw cross-correlation functions. GT prepared the paper with
contributions from all co-authors. GH produced Supplement Figs. S1 and S2.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
George Taylor is supported by the Leeds–York Doctoral Training Partnership of the
Natural Environment Research Council (NERC), UK. Gregor Hillers acknowledges
support through a Heisenberg fellowship from the German Research Foundation
(HI 1714/1-2). The DANA array was part of the Faultlab project, a
collaborative effort by the University of Leeds, Kandilli Observatory and
Earthquake Research Institute, and Sakarya University. Major funding was
provided by the UK NERC under grant NE/I028017/1. Equipment was provided and
supported by the NERC Geophysical Equipment Facility (SEIS-UK) loan 947. We
would like to thank Sven Schippkus and an anonymous reviewer for their detailed
reviews that helped improve the paper.
Edited by: Irene Bianchi
Reviewed by: Sven Schippkus and one anonymous referee
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