An L-band interferometric synthetic aperture radar study on the Ganos section of the north Anatolian fault zone between 2007 and 2011: Evidence for along strike segmentation and creep in a shallow fault patch
An L-band interferometric synthetic aperture radar study on the Ganos section of the north Anatolian fault zone between 2007 and 2011: Evidence for along strike segmentation and creep in a shallow fault patch
Marcello de Michele 0 1
Semih Ergintav 1
Hideo Aochi 0 1
Daniel Raucoules 0 1
0 Natural Risks department, French Geological Survey (BRGM), Orl eÂans, France, 2 Department of Geodesy, BoğazicËi University, Kandilli Observatory and Earthquake Research Institute , Istanbul , Turkey
1 Editor: Arda Yildirim , Gaziosmanpasa Universitesi , TURKEY
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files (profiles, tables, interferogram stack).
Interested researchers will be able to obtain the
ALOS PALSAR dataset in the same manner that
the authors did, that is, either by ordering the
ALOS PALSAR data from JAXA, via the ALOS
User Interface Gateway (https://auig2.jaxa.jp/ips/
home). Alternatively, data can be requested
from the European Space Agency at
We utilize L-band interferometric synthetic aperture radar (InSAR) data in this study to
retrieve a ground velocity map for the near field of the Ganos section of the north Anatolian
fault (NAF) zone. The segmentation and creep distribution of this section, which last
ruptured in 1912 to generate a moment magnitude (Mw)7.3 earthquake, remains incompletely
understood. Because InSAR processing removes the mean orbital plane, we do not
investigate large scale displacements due to regional tectonics in this study as these can be
determined using global positioning system (GPS) data, instead concentrating on the
close-tothe-fault displacement field. Our aim is to determine whether, or not, it is possible to retrieve
robust near field velocity maps from stacking L-band interferograms, combining both single
and dual polarization SAR data. In addition, we discuss whether a crustal velocity map can
be used to complement GPS observations in an attempt to discriminate the present-day
surface displacement of the Ganos fault (GF) across multiple segments. Finally, we
characterize the spatial distribution of creep on shallow patches along multiple along-strike segments
at shallow depths. Our results suggest the presence of fault segmentation along strike as
well as creep on the shallow part of the fault (i.e. the existence of a shallow creeping patch)
or the presence of a smoother section on the fault plane. Data imply a heterogeneous fault
plane with more complex mechanics than previously thought. Because this study improves
our knowledge of the mechanisms underlying the GF, our results have implications for local
seismic hazard assessment.
from the MARSITE PROJECT consortium via
Funding: This study was conducted within the
context of the EU 7th framework program project
`MARSite: New Directions in Seismic Hazard
assessment through Focused Earth Observation in
the Marmara Supersite' (Grant agreement no:
Tectonic context of the study area
The north Anatolian fault (NAF) is a major right-l ateral strike-slip fault with a length of about
1,500 km and a roughly east-west strike. This fault is thought to be the tectonic boundary
between the Anatolian and Eurasian plates in northern Turkey (e.g. [1±3]). Geologic, geodetic,
and seismologic evidence have been used to demonstrate that the NAF accommodates
between ca. 14 mm/yr and ca. 30 mm/yr of relative plate motion (e.g. [4±6]); the westernmost
section of the NAF, the Gazikoy-Saros segment (also called the Ganos fault, GF), is the onshore
section of the northern strand of the NAF (e.g. [
]). The strike of the GF runs between the Sea
of Marmara and the Gulf of Saros (Fig 1), while the Ganos section of the NAF remains
seismologically active. This section last ruptured in 1912 generating a Mw7.3 earthquake that
fractured the entire inland fault segment to a length of about 50 km. Field observations show that
this earthquake produced a right-lateral strike-slip offset of at least 3 m (e.g. [
]). A review of
Fig 1. The study area. Map of the study area discussed in this paper. The background image is the mean SAR amplitude image, calculated
from ALOS PALSAR data. Simplified fault traces from Yaltirak and Alpar (2002) are shown in red; white circles represent earthquakes (Koeri
catalogue); white boxes denotes areas used for profile stacking A, B, C. Red arrows indicate the sense of fault motion. Triangles represent
GPS locations: black triangle is DOKU, white triangle is KAVAK.
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the literature reveals additional historical reports of other large earthquakes that can be
attributed to the GF that occurred in A.D. 824, A.D. 1343, A.D. 1509, and A.D. 1766 (e.g.[10±12]).
The GF is believed to link the northern strand of the NAF zone in the Sea of Marmara with the north Aegean trough where slip partitioning results in branching of the fault zone (e.g. [13, 14]).
According to Okay et al. , the GF consists of several sub-parallel faults that share a dom
inant right-lateral strike-slip motion that was initiated towards the end of the Miocene leading
to transpressional uplift that formed the Ganos Mountains. Geodetic studies suggest that
present-day normal fault convergence is accommodated by the GF at a rate of 1.1 ± 0.4 mm/yr
]), while Tuysuz et al. [
] interpreted the apparent lack of seismicity of Mw larger than
3 along this fault as indicative of a locked segment that only slips during large earthquakes.
Studies based on the inversion of global positioning system (GPS) time series and C-band
interferometric synthetic aperture radar (InSAR) data have suggested that this section of the
NAF creeps at a depth between 8 km and 17 km and as a single segment at a rate of 2 cm/yr
]). At the same time, geological field investigations have led to interpretation of the
Ganos section as the result of multiple faults segments . Nevertheless, it remains unclear
just how present-day strain is accumulated along the GF and whether, or not, the Ganos
section is segmented at shallow depths. Both past and recent studies based on InSAR have proved
successful in measuring surface fault motion on the NAF, particularly at the Ismetpasa
creeping section (e. g. [4, 16±18]).
In this study, we process L-band InSAR data from the ALOS Phased Array type L-band
SAR (PALSAR) sensor that belongs to the Japanese Aerospace Exploration Agency (JAXA)
that encompasses the Ganos section of the NAF. Although the ALOS PALSAR archive for this
study area is not densely populated if just a single polarization acquisition mode is considered,
a much larger number of SAR scenes are available if both single and dual polarization
acquisition modes are taken into account. We therefore interferometrically combined both single and
dual polarization acquisition modes in this study to ensure a sufficiently populated archive to
allow stacking. This study has three main aims. First, we investigate whether, or not, it is
possible to retrieve ground velocity maps for this study area from the stacking of L-band
interferograms, combining both single and dual polarization SAR acquisition modes. Second, we
determine whether a ground velocity map can be used to complement GPS observations to
differentiate present-day surface displacement in the near field of multiple segments of the GF
fault. Third, we discuss whether, or not, it is possible to characterize the spatial distribution of
shallow creep on multiple segments at shallow depths. To accomplish these aims, we first
calculated all possible interferograms for the period between 2007 and 2011 coupling single and
dual polarization ALOS PALSAR data, unwrapped and stacked the most coherent, and
interpreted our results using a simple elastic dislocation model.
Data acquisition and processing
InSAR data can be used to map ground deformation at a spatial resolution of tens of meters
with sub-centimeter precision along the satellite line-of-sight direction (LOS) [19±21].
Previous work has shown that when dealing with interseismic strain measurements, L-band InSAR
(wavelength of 23,5 cm) performs better than shorter wavelength SAR for vegetated areas,
such as the region in this study (e.g. [
]). Ascending orbit satellite-path orientation with
respect to fault orientation is optimal in order to obtain suitable InSAR LOS sensitivity relative
to strike-slip movement, parallel to the NAF in the Ganos section. Besides, for InSAR process
sing to succeed, SAR scenes must be acquired at the same radar band, and with the same LOS
angle, and polarization. One major disadvantage of the ALOS PALSAR archive for the region
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of the Ganos section of the NAF is that data are not particularly abundant in terms of the
number of SAR scenes that encompass the same viewing angle and polarization mode. Thus, we
enhanced the number of potential scenes covering the GF by joint processing of multiple
polarization data, acquiring both Fine Beam Single mode (FBS) and Fine Beam Dual mode
polarization (FBD) PALSAR data with LOS angles of 38.7Ê. Of these, the FBD data matrix
consists of data columns that alternately record single polarizations, HH + HV respectively, while
the FBS data matrix just comprises HH polarization signals, where H stands for Horizontal
polarization and V stands for Vertical polarization. The drawback of this approach is that we
are only able to process these data at the same time at the cost of a decrease in spatial
resolution, while improving temporal sampling and dataset size. In practice, this can be achieved by
extracting one data column out of every two from both the HH FBS and HH-HV FBD data
matrices. This generates a series of HH FBS and HH FBD data that has the same polarization
but a 50% reduced range of spatial resolution (half of the original resolution). This reduction
will not, however, interfere with the results of this study as we are not interested in metric scale
phenomena. We implemented this processing step using GAMMA routines , resulting in a
final 14 single look complex (SLC) L-band dataset that spans four years, from July 7th, 2007, to
January 15th, 2011, with a LOS resolution of 20m (Table 1).
Atmospheric delays can mask weak shallow creep signals in the case of single interfero
]. Therefore, to reduce the extent of the atmospheric influence on interferometric
phase, we used a stacking methodology. Moreover, our aim was not to detect temporal
variations in fault motion as we assumed that tectonic-related deformation rates remained constant
over the observation period. We therefore employed the stacking method implemented in the
software GAMMA , on a set of selected interferograms, starting from 14 RAW ALOS
PALSAR images that we focused to obtain 14 SLCs. Because L band SAR data are heavily affected
by radio frequency interference (RFI) across the area, which leads to co-registration problems
and or interferogram streaks, we carefully filtered this effect during the focusing procedure,
following the procedure described in . Then, we co-registered the 14 SLCs on the basis of a
common master image (the first acquisition) and we calculated all possible interferograms
using a multi looking of 1 (LOS) and 5 (azimuth). This procedure yielded 91 multilooked
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Topographic contributions to interferometric phase were calculated for each interferogram using the Shuttle Radar Topography Mission 90 m digital elevation model (DEM) and were subtracted from the interferograms . Then, on the basis of 91 initial differential interferograms, we selected a subset of 39 high-signal-coherence examples via visual analysis (Table 2).
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We chose interferograms with a mean signal coherence of greater than 0.6 for at least 75% of
each interferogram. Then, we applied the Minimum Cost Flow (MCF) algorithm [
implemented in the software GAMMA, to unwrap selected interferograms. In each case, the
unwrapping step, performed at the original resolution of the grid, was improved using a phase
reference model obtained by unwrapping corresponding multiple- look interferograms as
these are simpler to process. We then resized the phase reference model to the original pixel
resolution of the resolution grid, while an unwrapped phase value for each pixel was computed
using the interferogram of complex values and the assumption that phase values in the resized
model will correspond to the correct unwrapped phase within the interval ± π. Thus, the
resulting unwrapped phase fulfils the condition that re-wrapping of the unwrapped phase will
result in exactly the phase of the complex interferogram, to the exclusion of a constant offset
which can be defined via the phase indicated for the reference location .
We also estimated altitude-related atmospheric phase delay. Depending on atmospheric
conditions, path delay can be dependent on altitude as the result of variations in atmospheric
water vapor and pressure profiles between the acquisition times of interferometric image pairs
]). Thus, to mitigate this, we used the software GAMMA to determine the linear
regression coefficients of the residual phase with respect to height in unwrapped
interferograms, as well as a DEM projected in the same geometry as the sensor to generate a phase
model of height-dependent atmospheric phase delay for each unwrapped interferogram. Each
model was then subtracted from the corresponding unwrapped single interferogram, and a
stacking algorithm was used to estimate the linear rate of differential phase, via the set of
unwrapped differential interferograms, to derive a time-averaged linear velocity map for the
study area. This stacking algorithm uses individual interferogram phases divided by time
interval weighted by the square of each to estimate phase rate, as proposed by Le Mouelic et al. [
The underlying assumption of this approach is that atmospheric conditions are not stationary
across the set of interferograms. Finally, because this process calculates a phase ramp on the
full PALSAR frame and removes it form the final stack, long wavelength tectonic motion in
the far-field generated by the movement of faults at depth is invisible in the velocity field
retrieved by InSAR. Nonetheless, we were able to measure near-fault velocity fields that may
reveal shallow fault processes.
The first outcome of this study is a SAR phase stack measured in the LOS direction of the sen
sor encompassing the period between 2007 and 2011 (Fig 2) and resulting in a velocity map.
These results show that the SAR signal is consistent over the study area, ca. 2/5 of the full PAL
SAR frame (S1 Fig). A clear bimodal distribution of surface displacement is present, consistent
with dextral shear. We identified three sections of the GF area (Fig 2), each of which shows a
characteristic behavior, and traced three cross-fault profiles across them to better visualize
surface displacement (Figs 1 and 2). Quite a few areas in the scene (including the area close to
section A in Fig 1) are affected by low signal coherence due to temporal surface changes related
to agricultural activities. Thus, we illustrated displacement values by stacking a number of
cross-fault profiles (Fig 3). The results of this approach show that in section A, the
westernmost part of the inland GF, there is clear evidence for near field displacement, possibly due to
the shallow motion of the fault on a smoother patch on the fault plane. At the same time, in
section B, the middle segment of the GF, evidence for transitional displacement is present,
including a smoother gradient from north-to-south. This movement is due either to deepening
of the motion of this fault, or previously undetected shallow motion of a fault branch related
to the Ganos mountains (the question mark in Fig 2). Finally, results show that there is no
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Fig 2. InSAR velocity map. The L-band InSAR velocity map that resulted from stacking 39 interferograms and showing LOS and flight
directions. The white boxes on this map mark the source areas used for profile stacking A, B, C (Fig 3). The continuous black line represents
the surface trace of the GF, black arrows indicate the sense of motion of the GF. Dotted black segments with the question mark represent
possible active faults in the study area. The white circle represents the reference area for the stacking procedure.
measurable surface motion on the easternmost inland section of the GF in section C, which
corroborates the hypothesis of a deep fault locking, as proposed in previous work (e.g. [
We emphasize that these fault motion interpretations do correspond with features inferred on
the basis of regional tectonics but which cannot be identified with our InSAR dataset.
Thus, using two-dimensional (2D) modeling, we attempted to address the question, if our
results suggest shallow fault patch motion on multiple section of the GF zone, then how
shallow is this smooth patch responsible for motion and how does it develop spatially? To do this,
we applied the equations suggested by Smith-Konter et al. [
] to compare our results over
three stacked profiles. However, as our data do not include regional tectonics, our fault model
cannot be propagated conceptually to an infinite depth but just to the upper tip of a shallow
smooth patch. We know that the locking depth of the GF in this area has already been
established by GPS at ca. 8 km and that it has been inferred to have a ca. 2 cm/yr velocity (e.g. [
On this basis, we attempted to characterize the depth of the shallow smoother patch that could
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Fig 3. Cross GF velocity profiles. Surface displacement measured on the basis of the stacked profiles
within boxes in Figs 1 and 2 (see also S1 and S2 Tables). The black and gray dots on this figure denote InSAR
mean values and standard deviations, respectively, while the dark-red line denotes our elastic dislocation 2D
model, and the white circle represents GPS velocity measured at the KAVAK station over the same period
(Ergintav et al., 2014). The standard deviation of the measurements profiles is calculated as ±0.24 cm/yr
(profile A), ±0.21 cm/yr (profile B), ±0.25 cm/yr (profile C).
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potentially be responsible for the displacement signal measured at the surface in the near field
of the GF. An elastic dislocation model can be used to describe the accumulation of elastic
strain along a simplified vertical strike-slip fault, while a velocity profile modeled across the
fault zone is given by the following equation:
x V tan 1 x :
In this expression, V denotes the far-field velocity, x is the horizontal fault-perpendicular
distance, and D is the depth of the upper tip of the smooth shallow patch. However, because
our InSAR results do not encompass regional displacement, we did not use Eq (1) to
determine locking depth but rather the depth at which the shallow smooth patch is creeping, and its
velocity on three selected GF sections (Fig 2). The best-fit interpretation of these data is shown
in Fig 3, suggesting shallow segmentation of the GF. Our observations fall within a certain
level of incertitude; the standard deviation of the measurements profiles is calculated as ±0.24
cm/yr (profile A, Fig 3), ±0.21 cm/yr (profile B, Fig 3), ±0.25 cm/yr (profile C, Fig 3). The
profile model presented in Fig 3a is compatible with motion on a localized smooth patch of the
GF at a depth of at least 0.5 km, creeping locally at ca. 1±0.25 cm/yr, while the model profile in
Fig 3b suggests that the shallow smooth patch on the GF is no longer present. In this model,
shallow motion on a localized smooth patch may be present on a different branch of the GF
zone, while the profile in Fig 3c suggests no shallow motion on this section of the GF, as has
been previously proposed (e.g. [
Discussion and conclusions
We utilized L-band InSAR stacking in this study to map surface displacement in the near
field of the GF between 2007 and 2011. There were two fundamental motivations for this
research project, the first of which is the need to create a spatially detailed map of crustal
displacement in the GF section of the NAF. Because this section is seismogenic, it is important
to understand how tectonic strain is partitioned across the region, while the second
motivation for this project is the fact that the L-band SAR signal is less affected by vegetation cover
than its C band counterpart. Therefore, the signal coherence of L-band InSAR is higher
across the study area which enables us to retrieve a spatially detailed surface displacement
map. At the same time, however processing this particular dataset is challenging, for three
reasons. In the first place, the dataset archive is not densely populated, and secondly
comprises FBS and FBD data, which need to be coherently combined to increase dataset
population. Thirdly, SAR data for this region are heavily affected by RFI which needs to be carefully
removed. Thus, to increase the size of our dataset, we combined HH FBS and HH-HV FBD
ALOS PALSAR data together, while during our focusing procedure, we carefully filtered RFI.
Because the SAR phase stack we calculated for the period between 2007 and 2011 (Fig 2) is
coherent over a wide area in the near field of the GF, our results complement previous
interpretations for the mechanics of this area suggested by GPS data and suggest that creep on the
GF was ca. 2 cm/yr to a locking depth of ca. 8 km. Although our processing removes a phase
ramp from the final stack and long wavelength tectonic surface motion driven by fault
movement at depth cannot be measured, we are able to measure near-fault velocity fields based on
this approach that might reveal shallow fault processes occurring above those at depth.
Indeed, our results suggest that the GF is characterized by along strike variations in near field surface motion, possibly due to the motion of shallower faults that together sum to equal the deeper structure hypothesized by Ergintav et al. (2014). Although our results for profile 3a suggests that this section of the GF is characterized by the motion of a shallow smoother
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patch on the fault plane, the question of whether, or not, this is driven by lithological contrast
in the area as soft quaternary sediments are juxtaposed with middle-upper Eocene turbidites
remains unanswered. We interpret this localized deformation as a linear-with-time trend just
because of the temporally sparse data sampling. Alternatively, we could be observing a
transient episodic slip reaching the surface, as recently observed elsewhere on a different section
of the NAFZ [
]. Our modeling approach suggests that the fault motion detected in profile
3b may occur on a segment parallel to the GF, as the dotted line (the question mark) in Fig 3
falls within the Eocene turbidite pro-delta lithology, and appears to be orientated parallel
with anticlinal fold axes in the region [
]. While this preliminary interpretation will require
further verification, profile 3c nevertheless suggests that there is no evidence for surface
creep along this section of the GF. This might provide further evidence that tectonic strain is
accumulating in this region, which has important implications for seismic cycle and
potential, as this section of the GF last ruptured in 1912. Further analysis will be required to fully
determine how strain partitioning in this region influences the interactions between
segments as well as the seismic potential of the area. Such an analysis might be carried out via
improvements of the GPS network in the area, by exploiting next generation L-band SAR
data from the ALOS-2 PALSAR sensor, and by combining these data with the high temporal
revisit capability of C-band SAR onboard the Sentinel-1 mission.
S1 Fig. Example interferograms on the study region. From the 39 coherent ALOS PALSAR
interferograms used in this study, we show examples of four representative interferograms
spanning 690 days, 644 days, 322 days and 92 days. The full PALSAR frame is shown. The red
rectangle represents the common area covering the GF (Figs 1 and 2) where the SAR signal is
coherent over the observation time (2007±2011). The interferometric phase is unwrapped; the
color scale represents the interferometric phase modulation between 0±2π (corresponding to
0±11.75 cm LOS).
S1 Table. Raw data for cross GF velocity profiles. Surface displacement measured on the
basis of the stacked profiles within boxe ªAº in Fig 2.
S2 Table. Raw data for cross GF velocity profiles. Surface displacement measured on the
basis of the stacked profiles within boxe ªBº in Fig 2.
This study was conducted within the context of the EU 7th framework program project `MAR
Site: New Directions in Seismic Hazard assessment through Focused Earth Observation in the
Marmara Supersite' (Grant agreement no: 308417). ALOS PALSAR radar data are available
from the Japanese Aerospace Exploration Agency (JAXA). We are thankful to JAXA for
providing ALOS PALSAR data. Original ALOS radar data are copyright JAXA and were provided
under Marmara Supersite Grant agreement no: 308417. We made raw interferograms available
to the public on the MARsite.eu/publications website.
Conceptualization: Marcello de Michele, Semih Ergintav, Hideo Aochi.
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Data curation: Marcello de Michele.
Formal analysis: Marcello de Michele, Hideo Aochi, Daniel Raucoules.
Investigation: Marcello de Michele, Semih Ergintav, Hideo Aochi, Daniel Raucoules.
Methodology: Marcello de Michele, Daniel Raucoules.
Resources: Semih Ergintav.
Supervision: Semih Ergintav, Hideo Aochi, Daniel Raucoules.
Validation: Semih Ergintav, Daniel Raucoules.
Visualization: Marcello de Michele.
Writing ± original draft: Marcello de Michele, Semih Ergintav, Hideo Aochi, Daniel
Writing ± review & editing: Marcello de Michele, Semih Ergintav, Hideo Aochi, Daniel
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