Secondary Fault Activity of the North Anatolian Fault Near Avcilar, Southwest of Istanbul: Evidence from SAR Interferometry Observations

remote sensing

Article Secondary Fault Activity of the North Anatolian Fault near Avcilar, Southwest of Istanbul: Evidence from SAR Interferometry Observations

Faqi Diao 1,*, Thomas R. Walter 2, Federico Minati 3, Rongjiang Wang 2, Mario Costantini 3, Semih Ergintav 4, Xiong Xiong 1 and Pau Prats-Iraola 5

1 State Key Laboratory of Geodesy and Earth’s Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China; [email protected] 2 GFZ-German Research Centre for Geosciences, Telegrafenberg, Potsdam 14473, Germany; [email protected] (T.R.W.); [email protected] (R.W.) 3 e-GEOS—An ASI/Telespazio Company, Via Tiburtina 965, Rome 00156, Italy; [email protected] (F.M.); [email protected] or [email protected] (M.C.) 4 Department of Geodesy, Kandilli Observatory and Earthquake Research Institute, Bo˘gaziciUniversity, Istanbul 34680, Turkey; [email protected] 5 DLR German Aerospace Center, Microwaves and Radar Institute, Weßling 82234, Germany; [email protected] * Correspondence: [email protected]; Tel.: +86-27-6888-1314

Academic Editors: Magaly Koch and Prasad S. Thenkabail Received: 12 July 2016; Accepted: 11 October 2016; Published: 18 October 2016

Abstract: Strike-slip faults may be traced along thousands of kilometers, e.g., the San Andreas Fault (USA) or the North Anatolian Fault (Turkey). A closer look at such continental-scale strike faults reveals localized complexities in fault geometry, associated with fault segmentation, secondary faults and a change of related hazards. The North Anatolian Fault displays such complexities nearby the mega city Istanbul, which is a place where earthquake risks are high, but secondary processes are not well understood. In this paper, long-term persistent scatterer interferometry (PSI) analysis of synthetic aperture radar (SAR) data time series was used to precisely identify the surface deformation pattern associated with the faulting complexity at the prominent bend of the North Anatolian Fault near Istanbul city. We elaborate the relevance of local faulting activity and estimate the fault status (slip rate and locking depth) for the ﬁrst time using satellite SAR interferometry (InSAR) technology. The studied NW-SE-oriented fault on land is subject to strike-slip movement at a mean slip rate of ~5.0 mm/year and a shallow locking depth of <1.0 km and thought to be directly interacting with the main fault branch, with important implications for tectonic coupling. Our results provide the ﬁrst geodetic evidence on the segmentation of a major crustal fault with a structural complexity and associated multi-hazards near the inhabited regions of Istanbul, with similarities also to other major strike-slip faults that display changes in fault traces and mechanisms.

Keywords: fault activity; interferometric synthetic aperture radar (InSAR); persistent scatterer interferometry (PSI); Istanbul; seismic hazard assessment; North Anatolian fault

1. Introduction Crustal strike-slip faults are found to be highly segmented, with patches of high or low slip, and parasitic or associated faults that may sporadically become seismically active [1–3] and often localize at geometric bends of the main fault trace [4]. The North Anatolian Fault (NAF) is one of the most active strike-slip faults in the world and can be traced from eastern Turkey to the northern Aegean Sea with a scale of ~1200 km [5]. Near the Almacık block, the NAF separates into two branches, and the northern

Remote Sens. 2016, 8, 846; doi:10.3390/rs8100846 www.mdpi.com/journal/remotesensing Remote Sens. 2016, 8, 846 2 of 17 Remote Sens. 2016, 8, 846 2 of 17 thebranch northern of the branch NAF(NNAF) of the NAF continues (NNAF) submarine continues into submarine the Marmara into the Sea, Marmara aligning Sea, with aligning the north with and thesouth north slopes and ofsouth the slopes Çınarcık of the Basin Çınarc [6] (Figureık Basin1). [6] The (Figure strike 1). of The the northernstrike of the branch northern then branch bends fromthen bendsthe northwest from the to northwest the west [to6]. the As wewest shall [6]. discussAs we inshall this discuss work, thein this localization work, the of thislocalization geometric of bendthis geometricis also the bend region is ofalso fault the complexity region of nearfault Küçükçekmececomplexity near Lake, Küçükçekmece associated with Lake, secondary associated faulting with secondaryprocesses thatfaulting can beprocesses observed that using can synthetic be observed aperture using radar synthetic (SAR) persistentaperture radar scatterer (SAR) interferometry persistent scatterer(PSI), a modern interferometry space geodetic (PSI), a technique.modern space geodetic technique.

FigureFigure 1. 1. GeologicalGeological map map of of the the study study area. area. The The black black lines lines indicate indicate the the distribution distribution of active of active faults faults in thisin thisarea area [7], [7whereas], whereas the the dashed dashed gray gray lines lines show show the the trace trace of of the the secondary secondary faults faults southwest southwest of of IstanbulIstanbul [8]. [8]. The The red red line line and and red red star star show show the the coseismic coseismic rupture rupture and and epicenter epicenter of of the İIzmit/Düzce˙zmit/Düzce earthquakes,earthquakes, respectively. respectively. The The black black box box draws draws the the st studyudy area area of of this this paper. paper. The The dashed dashed orange orange boxes boxes showshow the the spatial spatial coverage coverage of of the the ascending ascending and and desce descendingnding track track in in this this area. area. NNAF: NNAF: Northern Northern branch branch ofof the the North North Anatolian Anatolian Fault; Fault; KL: KL: Küçükçekmece Küçükçekmece La Lake;ke; BL: BL: Büyükçekmece Büyükçekmece Lake. Lake. The The upper upper left left panel panel showsshows thethe shadedshaded relief relief map map of the of study the area, study in which area, the in isolate which red the areas isolate show activered landslidesareas show [9]. active landslides [9]. The 1999 Izmit/Düzce˙ earthquake disaster highlighted the high seismic hazard and revealed a The 1999 İzmit/Düzce earthquake disaster highlighted the high seismic hazard and revealed a westward propagation of earthquakes towards the Marmara Sea [10]. The tectonic evolution of the westward propagation of earthquakes towards the Marmara Sea [10]. The tectonic evolution of the NNAF beneath the Marmara Sea has been widely discussed in past decades; some authors suggest NNAF beneath the Marmara Sea has been widely discussed in past decades; some authors suggest that the NNAF deforms as a single shear zone [11], whereas others suggest that the crust beneath the that the NNAF deforms as a single shear zone [11], whereas others suggest that the crust beneath the Marmara Sea is separated by an E-W trending graben [12]. Besides, the evolution of the NNAF zone has Marmara Sea is separated by an E-W trending graben [12]. Besides, the evolution of the NNAF zone also been interpreted as a combination of en-echelon strike-slip faults and pull-apart basins [6,13,14] has also been interpreted as a combination of en-echelon strike-slip faults and pull-apart associated with numerous subparallel fault scarps of unknown activity [7,15]. These investigations basins [6,13,14] associated with numerous subparallel fault scarps of unknown activity [7,15]. These mostly focused on the fault activity beneath the Marmara Sea, but the possible activity of secondary investigations mostly focused on the fault activity beneath the Marmara Sea, but the possible activity faults north of the Marmara Sea remained to be studied. As the faults are shaping the land and directly of secondary faults north of the Marmara Sea remained to be studied. As the faults are shaping the affect the populated regions of western Istanbul, monitoring the fault activity in this region is of land and directly affect the populated regions of western Istanbul, monitoring the fault activity in great importance. this region is of great importance. Previous studies on seismic and bathymetric observations show evidence of the existence of Previous studies on seismic and bathymetric observations show evidence of the existence of secondary faults near the two lakes (Büyükçekmece Lake (BL) and Küçükçekmece Lake (KL); Figure1), secondary faults near the two lakes (Büyükçekmece Lake (BL) and Küçükçekmece Lake (KL); north of the Marmara Sea [8,16–19]. These secondary faults were found to be NW-SE oriented following Figure 1), north of the Marmara Sea [8,16–19]. These secondary faults were found to be NW-SE the geographic alignment of the lake, which also show general consistency with the inland drainage oriented following the geographic alignment of the lake, which also show general consistency with pattern and the scarps of the surrounding terrain [19]. Furthermore, the agreement between the strike the inland drainage pattern and the scarps of the surrounding terrain [19]. Furthermore, the and location of the secondary faults and off-shore active faults indicates that the secondary faults near agreement between the strike and location of the secondary faults and off-shore active faults indicates the lakes are not local fault systems, but related to the NW extension of the NNAF [16]. that the secondary faults near the lakes are not local fault systems, but related to the NW extension Near-ﬁeld geodetic observations can provide an independent constraint on the present activity of of the NNAF [16]. these secondary faults. Recent GPS observations show local crust deformation (~5 mm/year) near Near-field geodetic observations can provide an independent constraint on the present activity KL [17,20], however, the inferred results may suffer from the spatial limitation of the survey-mode GPS of these secondary faults. Recent GPS observations show local crust deformation (~5 mm/year) near KL [17,20], however, the inferred results may suffer from the spatial limitation of the survey-mode GPS observation. Previous studies [21,22] already described the pronounced subsidence of the

Remote Sens. 2016, 8, 846 3 of 17 observation. Previous studies [21,22] already described the pronounced subsidence of the Avcilar area (Figure1), but the horizontal movement related to the secondary fault activity remained to be studied. In this paper, well-covered long-term PSI measurements were carried out by exploiting the ESA ENVISAT ascending (Track 429) and descending (Track 336) data archive, in order to study the stable near-field crustal deformation (Figure1). After geometric decomposition and model-based correction of the post-seismic viscoelastic relaxation associated with the 1999 Izmit/Düzce˙ earthquakes, the slip rate and locking depth of the secondary fault beneath KL were determined for the first time based on the assumption of a single dextral fault that was inferred from geological studies [16,17]. We compare our findings to independent information and discuss the implications of these secondary faults, as well as the relationship of the fault slip and fault trend to the geomorphology and landslide activity.

2. Data Handling and Method Spaceborne SAR interferometry (InSAR) allows investigating the study area at high resolution and identifying subtle deformation occurrences. The basic principles of SAR interferometry have been extensively described in the literature [23,24], and the techniques were then signiﬁcantly improved by the application of time series procedures. The small baseline subset technique [25] allows such time series analysis and is based on interferograms with small baselines to limit geometric decorrelation and was used to map subsidence zones in the Avcilar area nearby Istanbul [22]. The PSI technique, with the idea of selecting distinct pixels, named persistent scatterers (PS), characterized by a coherent signal over a long series of acquisitions, irrespective of the baseline, brought important advances in the ﬁeld, allowing millimetric precision of displacement detection [26]. Since then, different PSI and, in general, multi-interferogram InSAR techniques have been developed [25,27–31]. By means of PSI techniques, very long time series, such as available over the study area west of Istanbul, can be processed, and very dense PS measurements can be obtained in spite of temporal and geometrical decorrelation effects. In this work, an advanced PSI technique named persistent scatterer pair (PSP) [30] was used to process the full ENVISAT archive of SAR data, in order to study aseismic deformations to the west of Istanbul, a region that is located in the vicinity of an important bend of the NNAF.

2.1. SAR Interferometry Processing The PSP approach was recently proposed [29,30,32] for the identification of PSs in series of full resolution SAR images and the retrieval of the corresponding 3D positions and displacements. Differently from standard PSI techniques, the PSP method relies only on the relative signal in pairs of nearby points, both for identification and analysis of PSs. For this reason, the PSP technique is intrinsically robust to spatially-correlated disturbances (such as atmospheric and orbital artifacts) and does not need a preliminary processing aimed at compensating these contributions to the signal. In addition, deviations from the models of displacement evolution typically used in PSI techniques (for example, due to strong nonlinear evolutions) are mitigated by the PSP approach because close points likely have similar deviations. Against the mentioned advantages, working with arcs requires many more computations than analyzing single points: in fact, N points can form (N − 1) × N/2 different arcs. The PSP approach is based on a complex algorithm designed to find a set of valid (in the sense of the multi-acquisition coherence or analogous parameters) arcs to form a graph with given connectivity and cardinality. The PS points are identified as the nodes of this graph. The high number of arcs and the high connectivity of the graph guarantee reliable PS identification and accurate PS measurements. Moreover, it makes it possible to fully extract coherent information even from low-intensity SAR signals, even when strongly scattering structures are not present, the sensed signal is weak and the scattering distributed, as in the case of rather smooth surfaces or natural terrains. In this sense, PS are intended in the general sense of scatterers whose backscattering properties persist unaltered over time (at least for the period of interest), as the name suggests, but that can be also fully distributed. In fact, thanks to the higher range resolution and the stricter orbital Remote Sens. 2016, 8, 846 4 of 17

Remote Sens. 2016, 8, 846 4 of 17 control, in the recent satellite SAR systems, the interferometric baseline is below the critical limit, and thereThe are redundant no strong spatialinformation decorrelation provided phenomena. by the highly connected PSP graph is conveniently exploitedThe redundantto guarantee information more accurate provided measurements by the highly connected by a recent PSP graphmethod is conveniently for robust exploitedphase unwrappingto guarantee and more finite accurate difference measurements integration by a recent [33], methodwhich forare robust key phaseprocessing unwrapping steps andin finitethe reconstructiondifference integration of PS elevations [33], which and are displaceme key processingnts from steps InSAR in the reconstruction data. The use of of PS a elevations multi-scale and decompositiondisplacements method from InSAR [34] allows data. one The to use perform of a multi-scale a global processing decomposition approa methodch also [in34 large] allows areas one withto performhigh PS adensity, global processingdespite the approachhigh computational also in large demand areas withrequired high by PS the density, exploitation despite of the very high redundantcomputational information. demand required by the exploitation of very redundant information. TheThe advantages advantages of ofglobally globally exploiting exploiting redundan redundantt arcs arcs connecting connecting different different pairs pairs of ofPS PS can can be be particularlyparticularly relevant relevant in inorder order to toobtain obtain reliable reliable and and accurate accurate measurements measurements when when the the PS PS are are very very sparse,sparse, as as in inthe the considered considered region region of ofIstanbul, Istanbul, which which is ischaracterized characterized by by the the presence presence of ofwater water bodies, bodies, mountainsmountains and and large large vegetated vegetated areas. areas. ForFor this this study, study, an an area area of of about about 600 600 km km2 2surroundingsurrounding KL KL was was analyzed analyzed exploiting exploiting the the ESA ESA ENVISATENVISAT ascending ascending (Track (Track 429) 429) an andd descending descending (Track (Track 336) 336) data data archive. archive. The The ascending ascending dataset dataset consistedconsisted of of33 33 images images covering covering the the period period Dece Decembermber 2002–April 2002–April 2010 2010 and and the the descending descending dataset dataset consistedconsisted of of 32 32 images images covering covering the the period period AprilApril 2003–September2003–September 2010.2010. TheThe images images acquired acquired from from the theENVISAT ENVISAT ASAR ASAR sensor sensor have have the the following following characteristics: characteristics: strip strip map map image image mode, mode, a resolutiona resolution of of 5m 5 ×m 25× 25 m perm per pixel, pixel, VV VV polarization, polarization, a nominal a nominal incidence incidence angle angle of 23 degreesof 23 degrees and a maximumand a maximum baseline baselineof 1800 of m. 1800 The 3-arcsecm. The 3-arcsec SRTM DEM SRTM was DEM used was as anus initialed as guessan initial for theguess removal for the of removal the topographic of the topographiccontribution. contribution. During the During PSP processing, the PSP processing, the elevation the information elevation information was further was refined further from refined the SAR fromdata the stack, SAR by data estimating stack, by in estimating correspondence in correspon of eachdence PS the of correction each PS the to becorrection applied to thebe applied input DEM. to theThe input PSP DEM. (and, The in general, PSP (and, PSI) in processinggeneral, PSI) makes processing it possible makes to it reach possible a metric to reach elevation a metric precision elevation for precisioneach PS for with each ENVISAT PS with data.ENVISAT It is todata. be notedIt is to thatbe noted the use that of the a higher use of resolutiona higher resolution DEM as theDEM initial as theguess initial could guess reduce could the reduce computational the computational cost of the cost algorithm, of the butalgorithm, would notbutimprove would not the improve final elevation the finalaccuracy elevation of each accuracy PS. of each PS. ForFor each each dataset, dataset, the the interferogra interferogramsms with with respect respect to tothe the first first image image of of the the series series were were generated generated (single(single master master interferogram interferogram configuration). configuration). In Infact, fact, when when working working at atfull full resolution resolution with with PSI PSI techniques,techniques, there there is isno noadded added information information in the in theother other interferograms interferograms (all the (all possible the possible pairs pairs can be can exactlybe exactly obtained obtained as a linear as a linear combination combination of the of single the single master master interferograms). interferograms). Persistent Persistent scatterers scatterers are byare definition by definition quite quiteinsensitive insensitive to spatial to spatial and spec andtral spectral decorrelation decorrelation effects, effects, and large and temporal large temporal and spatialand spatial baseline baseline interferograms interferograms can be can used, be used, allowing allowing the thefull fullexploitation exploitation of long of long time time series series of of acquisitions.acquisitions. The The diagrams diagrams of ofthe the temporal temporal and and spatial spatial distribution distribution of ofSAR SAR images images corresponding corresponding to to thethe two two analyzed analyzed datasets datasets are are shown shown in in Figure Figure 2.2 .

FigureFigure 2. 2.SpatialSpatial and and temporal temporal distribution distribution of ofSAR SAR images images for for the the ENVISAT ENVISAT dataset dataset acquired acquired in in ascendingascending (a) ( aand) and descending descending (b ()b geometries.) geometries.

ByBy using using the the PSP PSP method, method, about about 240,000 240,000 and and 200,000 200,000 PS PS points points were were extracted for thethe studystudy areaarea in inascending ascending and and descending descending geometries, geometries, respectively. respectively. Based Based on the on statistical the statistical analysis analysis that we performed,that we performed,the standard the deviation standard of deviation the deformation of the deformat measuremention measurement error is in the error order is of in 3 mm,the order typically of 3 ranging mm, typicallybetween ranging 1 and 5between mm, depending 1 and 5 mm, on the depending quality of on the the speciﬁc quality PS of andthe specific on the typePS and of groundon the type cover, of ground cover, whereas the refined height measurements have a metric accuracy. The average measurement density is 100 PS/km2 over the whole area and reaches 1000 PS/km2 in the most urbanized areas.

Remote Sens. 2016, 8, 846 5 of 17

2.2. Signal Decomposition Due to the radar viewing geometry, ENVISAT observations are ~3-times more sensitive to vertical motion than to fault-parallel motion in this area, assuming a fault trend of N30°W and a strike-slip motion. Previous investigations show that the Avcilar district underwent clear subsidence [21,22]; this signal can overwhelm the horizontal motions induced by fault movement. In order to investigate the particularities of these deformations, we apply a signal decomposition approach that we designed for investigations related to the N30°W striking fault slip. By combining ascending and descending PS-InSAR observations, the LOS (line of sight) velocities can be projected

Remoteonto any Sens. desired2016, 8, 846 set of two orthogonal basis vectors under the assumption that the third component5 of 17 is zero [35]. Such an approximation is particularly suitable for areas around the middle of a straight fault segment as in the present case. whereasThe thetime-series reﬁned height data measurementsof the study haveregion a metric sugg accuracy.est vertical, The but average also measurementsignificant horizontal density is 100displacement PS/km2 over components. the whole In area order and to reaches improve 1000 the PS/km signal-to-noise2 in the most ratio, urbanized we estimate areas. the mean LOS velocity between 2002 and 2010. Then, we projected the obtained ascending and descending LOS 2.2. Signal Decomposition velocities (Figure 3) onto motions in the fault-parallel and vertical direction using a least square methodDue [35,36]. to the radar A uniform viewing fault-parallel geometry, ENVISAT direction observations of horizontal are movement ~3-times more was sensitiveassumed, to whereas vertical motionthe fault-perpendicular than to fault-parallel horizontal motion component in this area, was assuming not considered a fault trendin the ofmodelling. N30◦W and This a horizontal strike-slip motion.strike-slip Previous motion investigations was already showindependently that the Avcilar inferre districtd from underwent the GPS observation clear subsidence in this [21 area,22]; [37], this signalagreeing can with overwhelm the orientation the horizontal of the KL motions fault that induced was shown by fault by movement. seismic investigation In order to investigate[18], as well the as particularitiesby structural studies of these [16]. deformations, we apply a signal decomposition approach that we designed for investigationsThe decomposed related horizontal to the N30 and◦W vertical striking movement fault slip. Byare combining shown in ascendingFigure 4a,b. and Clear descending relative PS-InSARmovement observations,in the fault-parallel the LOS direction (line of was sight) observed velocities on two can sides be projected of the KL ontofault, anywhich desired agrees set well of twowith orthogonalthe tectonic basis background vectors under (dextral the character) assumption of that this the fault. third We component found that is relative zero [35 ].horizontal Such an approximationmovements striking is particularly N30°W suitablelocally forexceed areas 5.0 around mm/year. the middle This ofdeformation a straight fault pattern segment is asin ingood the presentagreement case. with horizontal velocities from GPS data (see Figure 4a). The quantitative comparison betweenThe the time-series horizontal data GPS of thevelocity study and region the inverted suggest vertical,horizontal but InSAR also signiﬁcantvelocity is horizontalshown in displacementFigure A1, from components. which we can In see order that tothe improve InSAR velo thecities signal-to-noise are generally ratio, comparable we estimate with that the meanof the LOSGPS observation velocity between in terms 2002 of andmagnitude 2010. Then,and direction, we projected though the a obtaineddifference ascending is found on and a few descending stations. LOSNote velocitiesthat the inverted (Figure3 horizontal) onto motions InSAR in thevelocities fault-parallel on two andcontinuous vertical directionstations (AVCT using aand least KCEK square in methodFigure A1) [35 fit,36 well]. A uniformwith that fault-parallel of the GPS observatio directionn. of Furthermore, horizontal movement clear subsidence was assumed, was observed whereas in thetwo fault-perpendicular areas (Figure 4b): the horizontal large one component is located at was the not west considered of KL aligning in the along modelling. a pronounced This horizontal valley; strike-slipthe small one motion is located was alreadyat the east independently of the lake faul inferredt. Subsidence from the along GPS KL observation thus is associated in thisarea with [ 37the], agreeingsteep topography with the orientationand clearly of associated the KL fault with that the was steeper shown slopes. by seismic Subsidence investigation east of [KL,18], in as turn, well asis byfound structural on a relatively studies [flat16]. plain.

Figure 3.3.Persistent Persistent scatterers scatterers (PS)-InSAR (PS)-InSAR velocity velocity in the in LOS the(line LOS of (line sight) of direction sight) fordirection ascending for (ascendinga) and descending (a) and descending geometry (b geometry). Positive ( meansb). Positive that the means ground that points the ground move toward points themove satellite. toward the satellite.

The decomposed horizontal and vertical movement are shown in Figure4a,b. Clear relative movement in the fault-parallel direction was observed on two sides of the KL fault, which agrees well with the tectonic background (dextral character) of this fault. We found that relative horizontal movements striking N30◦W locally exceed 5.0 mm/year. This deformation pattern is in good agreement with horizontal velocities from GPS data (see Figure4a). The quantitative comparison between the horizontal GPS velocity and the inverted horizontal InSAR velocity is shown in Figure A1, from which we can see that the InSAR velocities are generally comparable with that of the GPS observation in terms of magnitude and direction, though a difference is found on a few stations. Note that the inverted horizontal InSAR velocities on two continuous stations (AVCT and KCEK in Figure A1) ﬁt well with that of the GPS observation. Furthermore, clear subsidence was observed in two areas (Figure4b): the large one is located at the west of KL aligning along a pronounced valley; the small one is located at the east of the lake fault. Subsidence along KL thus is associated with the steep topography and clearly associated with the steeper slopes. Subsidence east of KL, in turn, is found on a relatively ﬂat plain. Remote Sens. 2016, 8, 846 6 of 17 Remote Sens. 2016, 8, 846 6 of 17

Remote Sens. 2016, 8, 846 6 of 17

Figure 4. DecomposedDecomposed InSAR InSAR velocity velocity in in the the horizont horizontalal (positive means that points move along N30°W)N30◦W) ( a)) and and the the vertical vertical direction direction ( (b);); ( (cc,,d)) are are the the horizontal horizontal (N30°W) (N30◦W) and and vertical vertical crustal crustal velocity velocity that was induced by thethe post-seismicpost-seismic viscoelasticviscoelastic relaxationrelaxation ofof thethe 19991999 Izmit/Düzce˙İzmit/Düzce earthquakes; (e,f) areare fault-parallelFigurefault-parallel 4. Decomposed and and vertical vertical InSAR velocity velocityvelocityafter in afterthe removing horizont removingal the (positive post-seismicthe post-seismic means that viscoelastic points viscoelastic move relaxation along relaxation effect effectof the ofIzmit/Düzce˙ theN30°W) İzmit/Düzce (a) and earthquakes; the earthquakes;vertical direction black black arrows (b); (c ,arrowsd)in are ( athe) in show horizontal (a) show GPS (N30°W) velocityGPS velocity and in vertical this in area thiscrustal [ 37area velocity]. [37]. that was induced by the post-seismic viscoelastic relaxation of the 1999 İzmit/Düzce earthquakes; 3. Effect of Post-Seismic(e,f) are fault-parallel Viscoelastic and vertical Relaxation velocity after removing the post-seismic viscoelastic relaxation 3. Effect of Post-Seismiceffect of the İzmit/Düzce Viscoelastic earthquakes; Relaxation black arrows in (a) show GPS velocity in this area [37]. The post-seismic post-seismic viscoelastic viscoelastic relaxation relaxation effect effect (PVRE) (PVRE) may may affect affect crustal crustal deformation deformation near large near 3. Effect of Post-Seismic Viscoelastic Relaxation earthquakeslarge earthquakes for decades for decades [38]. [ 38Studies]. Studies on onpost-seismic post-seismic deformation deformation of of the the 1999 Izmit/Düzce˙İzmit/Düzce The post-seismic viscoelastic relaxation effect (PVRE) may affect crustal deformation near large earthquakes suggest suggest that that the the PVRE PVRE induced induced by by these these events events is clear is clear and and should should notnot be ignored be ignored in the in earthquakes for decades [38]. Studies on post-seismic deformation of the 1999 İzmit/Düzce PS-InSAR observation period [39]. In this paper, we estimated and removed the PVRE using a the PS-InSARearthquakes observation suggest that period the PVRE [39]. induced In this by paper, these events we estimated is clear and andshould removed not be ignored the PVRE in the using a model-basedPS-InSAR methodmethod observation followingfollowing period [ 40[40]. ].[39]. The The In detailed this detailed pape modelr, modelwe estimated construction construction and removed and and PVRE thePVRE estimationPVRE estimation using havea have been beengiven given in [model-based40 in], and[40], some and method some key following steps key steps are [40]. described are The described detailed in this model in section. this construction section. and PVRE estimation have Firstly,been a given three-layer three-layer in [40], and viscoelastic viscoelastic some key steps model model are wasdescribed was set set up in up thistoto forward section. forward simulate simulate this this viscoelastic viscoelastic effect effect on Firstly, a three-layer viscoelastic model was set up to forward simulate this viscoelastic effect on eachon each PS-InSAR PS-InSAR point point within within the observation the observation period period (Figure (Figure 5). The5). Themodel model has one has elastic one elastic upper upper crust each PS-InSAR point within the observation period (Figure 5). The model has one elastic upper crust layer and two viscoelastic layers representing the lower crust and upper mantle, for which the crust layerlayer and and two two viscoelastic viscoelastic layers representing representing the thelower lower crust crust and upper and upper mantle, mantle, for which for the which the Maxwell Maxwellrheology rheology was applied was applied (Figure (Figure5 5).). 5).

Figure 5.FigureSketch 5. mapSketch shows map shows the viscoelasticthe viscoelastic model model used for for post-seismic post-seismic viscoelastic viscoelastic relaxation relaxation effect effect (PVRE) estimation. (PVRE) estimation.

Figure 5. Sketch map shows the viscoelastic model used for post-seismic viscoelastic relaxation effect (PVRE) estimation.

Remote Sens. 2016, 8, 846 7 of 17

Secondly, the lower crust and upper mantle viscosity of the Maxwell rheology play an important role inRemote estimating Sens. 2016, the8, 846 PVRE effect [38]. These parameters were inferred from the post-seismic7 of 17 GPS observation in a similar time period by employing a grid-search method [40]. Secondly, the lower crust and upper mantle viscosity of the Maxwell rheology play an important Thirdly,role in estimating we calculated the PVRE the PVREeffect [38]. at each These PS-InSAR parameters data were point inferred using from the the derived post-seismic optimal GPS viscosity parametersobservation and thein a numericalsimilar time codeperiod provided by employing by [a41 grid-search]. The results method show [40]. that the mean PVRE effect on groundThirdly, velocity we is calculated almost constantthe PVRE at in each the PS-InSAR observation data point period. using Using the derived the optimal optimal viscosities,viscosity the calculatedparameters PVRE and is the estimated numerical to code be provided ~2 mm/year by [41] horizontally. The results show in thethat the study mean area PVRE and effect is thereforeon considerableground velocity for inverting is almost the constant fault movement in the observation (Figure 4period.c,d). However, Using the theoptimal PVRE viscosities, reveals athe smooth spatialcalculated pattern in PVRE the areais estimated around theto be KL ~2 fault, mm/year so that horizontally its influence in the on study the estimation area and is of therefore the fault slip is considerable for inverting the fault movement (Figure 4c,d). However, the PVRE reveals a smooth not so significant. spatial pattern in the area around the KL fault, so that its influence on the estimation of the fault slip Atis last,not so we significant. removed the PVRE from the InSAR velocities, in order to obtain the deformation signal that is largelyAt last, free we of removed the post-seismic the PVRE effectfrom the (Figure InSAR4e,f). velocities, in order to obtain the deformation Tosignal avoid that the is largely effects free induced of the post-seismic by other local effect deformation(Figure 4e,f). processes (landsliding, groundwater extractionTo and avoid others), the effects we low-pass induced filteredby other thelocal data deformation using a simpleprocesses median (landsliding, filtering groundwater method. We got the velocityextraction of each and others), InSAR we point low-pass by calculating filtered the the data mean using value a simple of neighboringmedian filtering points method. within We agot distance (filteringthe length).velocity of After each filteringInSAR point the by small-scale calculating signal, the mean the value relative of neighboring fault movement points becomeswithin a more distance (filtering length). After filtering the small-scale signal, the relative fault movement becomes visible (Figure6), which highlights the relative motions of the KL fault. Other small-scale processes may more visible (Figure 6), which highlights the relative motions of the KL fault. Other small-scale slightlyprocesses affect the may deformation slightly affect pattern the deformation and play apattern secondary and play role a insecondary controlling role thein controlling crustal deformation the in thiscrustal area. deformation in this area.

Figure 6. Horizontal velocity field along N30°W near KL: (a) Without spatial filtering; (b–d) Filtered Figure 6. Horizontal velocity field along N30◦W near KL: (a) Without spatial filtering; (b–d) Filtered data with different spatial filtering length. Purple boxes show the location of the velocity profile data with different spatial filtering length. Purple boxes show the location of the velocity profile (A–A’) (A–A’) shown in Figure 7. shown in Figure7. 4. Modelling and Results 4. ModellingAfter and removing Results the PVRE, a velocity profile perpendicular to the fault orientation was Afterconstructed removing (Figure the 7), PVRE, which a velocitywas used profileto estimate perpendicular the fault slip to rate the and fault locking orientation depth based was constructedon a widely-used 2D screw dislocation model [42]. In this model, the fault-parallel velocity () as a (Figure7), which was used to estimate the fault slip rate and locking depth based on a widely-used function of its perpendicular distance to the fault ( ) can be drawn by: 2D screw dislocation model [42]. In this model, the fault-parallel velocity (v) as a function of its perpendicular distance to the fault (x) can(x) =(/)×arctan(/) be drawn by: (1) where and are the slip rate and the locking depth of the KL fault, respectively. A grid-search method was applied to search forv the(x )slip= rate(V/ andπ) ×lockingarctan depth(x/ Dover) ranges of 0.5–20 mm/year and (1) 0.5–20 km with 0.5-mm/year and 0.5-km intervals. The RMS (root mean square) misfit between the where V and D are the slip rate and the locking depth of the KL fault, respectively. A grid-search method was applied to search for the slip rate and locking depth over ranges of 0.5–20 mm/year and 0.5–20 km with 0.5-mm/year and 0.5-km intervals. The RMS (root mean square) misfit between the Remote Sens. 2016, 8, 846 8 of 17

Remote Sens. 2016, 8, 846 8 of 17 modelmodel and observationand observation for each for combinationeach combination of the of parametersthe parameters was was calculated, calculated, from from which which the optimalthe valuesoptimal of the values slip rate of the and slip locking rate and depth locking were depth ﬁxed were by minimizingfixed by minimizing the RMS the misﬁt. RMS misfit.

FigureFigure 7. Decomposed 7. Decomposed horizontal horizontal velocity velocity (N30 (N30°W)◦W) on pr profilesofiles perpendicular perpendicular to tothe the orientation orientation of the of the fault and comparisons to the model predictions that were inferred from the optimal fault parameters: fault and comparisons to the model predictions that were inferred from the optimal fault parameters: (a) Original fault-parallel velocity without spatial filtering; (b–d) Velocity profiles obtained from (a) Original fault-parallel velocity without spatial filtering; (b–d) Velocity profiles obtained from low-pass filtered data. The blue lines in each subfigure are the forward predictions based on the low-pass filtered data. The blue lines in each subfigure are the forward predictions based on the inverted optimal fault parameters. In each subfigure, the gray dots show the original or filtered data invertedalong optimal the profile, fault and parameters. the red dots Inrepresent each subfigure,the mean values the gray at each dots distance show along the originalthe profile. or The filtered data alonglocation the of profile,this profile and is theshown red in dots Figure represent 6. the mean values at each distance along the profile. The location of this profile is shown in Figure6. The uncertainty of the obtained optimal slip rate and locking depth were estimated statistically Theusing uncertainty the Monte Carlo of the method obtained [43]. optimal We firstly slip constructed rate and lockingthe 1D variance-covariance depth were estimated matrix statistically of the ascending and descending InSAR data using the method of [44] and generated spatially-correlated using the Monte Carlo method [43]. We firstly constructed the 1D variance-covariance matrix of the noise. Secondly, we obtained the perturbed dataset by adding the noise to the observation. Finally, ascendingwe searched and descending for optimal InSAR parameters data using based the on method each disturbed of [44] anddataset generated and could spatially-correlated analyze the noise.best-fitting Secondly, model. we obtained We run the these perturbed steps 500 dataset times byin order adding to theobtain noise a robust to the estimation observation. about Finally, the we searcheduncertainty for optimal of the parametersparameters. The based distribution on each disturbedof the slip rate dataset and locking and could depth analyze are inferred the best-fittingfrom a model.Monte We runCarlo these simulation steps 500and times show ina general order toconsistence obtain a robustwith the estimation RMS misfit about map (Figure the uncertainty 8). The of the parameters.result statistically The distribution reveals a mean of theslip sliprate rateof 5.1 and ± 0.4 locking mm/year depth and a are locking inferred depth from of 1.0 a Monte± 0.5 km Carlo simulation(the mean and value show and a general the one-sigma consistence uncertainty; with the Figure RMS 8), misfit suggesting map (Figurethat the8 KL). The fault result is active statistically with revealsa shallow a mean locking slip rate depth. of 5.1 Therefore,± 0.4 mm/year a tectonically and and a lockingpotentially depth seismically of 1.0 ± active0.5 kmfault (the is located mean in value and thethe one-sigma immediate uncertainty; vicinity of Figurewestern8 ),Istanbul, suggesting with that important the KL faultimplications is active as with discussed a shallow in lockingthe following section. depth. Therefore, a tectonically and potentially seismically active fault is located in the immediate In order to test the effect of possible small-scale deformations (such as landsliding), we also vicinityinverted of western the low-pass Istanbul, filtered with data important using the implications same method as mentioned discussed above. in the As following shown in section. Figure 8, we found that similar results as those inverted from the unfiltered data could be obtained, although

Remote Sens. 2016, 8, 846 9 of 17 the data misfits became smaller as the filtering length increasing. The estimated locking depths tend systematically to a very small value (<1 km), suggesting possible creep of this secondary fault. Similar faultRemote creep Sens. 2016 and, 8 shallow, 846 locking depth have been found on other strike-slip fault, such as the central9 of 17 section of the San Andreas fault [45] and western sections of the NAF [46].

FigureFigure 8. 8. InversionInversion results: results: the the first first column column shows shows the the slip slip rate rate versus versus fault fault locking locking depth; depth; the the second second andand third columnscolumns revealreveal the the statistic statistic distribution distribution of of the the slip slip rate rate and and locking locking depth depth inferred inferred from from each eachvelocity velocity profile. profile. In the Infirst the first column, column, the blackthe black circles circles represent represent the optimalthe optima solutionl solution inferred inferred from from 500 500Monte Monte Carlo Carlo simulations. simulations. (a) Shows(a) Shows the the result result that that was was inferred inferred from from the the original original data; data; (b– d(b)– showd) show the theresults results that werethat invertedwere inverted from filtered from datafiltered with data filtering with length filtering of 2 km,length 4 km of and 2 6km, km respectively.4 km and 6 km respectively. In order to test the effect of possible small-scale deformations (such as landsliding), we also 5. Discussion and Implications inverted the low-pass filtered data using the same method mentioned above. As shown in Figure8, we found that similar results as those inverted from the unfiltered data could be obtained, although 5.1. Limitations the data misfits became smaller as the filtering length increasing. The estimated locking depths tend systematicallyA number to of a verylimitations small value of th (<1e km),present suggesting study need possible to creepbe discussed of this secondary that may fault. affect Similar the interpretationfault creep and and shallow correct locking understanding depth have beenof our found results. on other Although strike-slip the fault,herein such used as thePS-InSAR central techniquesection of theprovides San Andreas very robust fault [results45] and at western a pixel sections resolution of thethat NAF is higher [46]. than hitherto presented results in the region, the signal investigated is still rather small. Horizontal motions in the sub-centimeter5. Discussion andscale Implications are very challenging to observe, which is why we have decided to exploit the average velocity rather than certain time-dependent increments of the data. The advantage of this 5.1. Limitations approach is that the total displacement values become significant. The disadvantage is that temporal A number of limitations of the present study need to be discussed that may affect the interpretation and correct understanding of our results. Although the herein used PS-InSAR technique provides Remote Sens. 2016, 8, 846 10 of 17 very robust results at a pixel resolution that is higher than hitherto presented results in the region, the signal investigated is still rather small. Horizontal motions in the sub-centimeter scale are very challenging to observe, which is why we have decided to exploit the average velocity rather than certain time-dependent increments of the data. The advantage of this approach is that the total displacement values become significant. The disadvantage is that temporal variations may not be interpreted. The strong seasonality of the subsidence region [22] or the temporal variations of the landslides [17] are not discussed, as is the nature of the strike-slip fault motion. In this, we cannot decipher if this inferred fault slip is a short-term behavior or a long-term process, warranting further studies. Furthermore, future studies may use the temporal variability of the different processes to investigate the different types of deformation (faulting, landsliding and subsidence). In addition, compared with the descending data, the ascending data are less sensitive to the horizontal movement of the KL fault due to the geometric setting. This limitation may highly affect the extraction of horizontal motion if only the descending data are available. In this paper, owing to the combined analysis of the ascending and descending data, such an effect would be decreased to a certain degree. Synthetic tests were carried out in order to investigate how the horizontal and vertical deformation signal depend on the geometric setting of the ascending, descending data and the fault orientation. As the results show in Figures A2–A5, we find that the input horizontal and vertical signals can generally be well recovered with the geometric setting of the InSAR data and the fault orientation in this area. Nevertheless, more close investigations, such as continuous cross-fault GPS and strain-meter observation, are strongly required in order to obtain a more robust estimation of the fault movement. This study is based on assumptions made from earlier studies on the KL fault mechanism. In addition, it is hard to further distinguish whether the KL fault is a simple fault or a narrow zone including a few small active faults [18] using the geodetic data shown in this paper. We assumed it to be a single dextral fault as a first order approximation.

5.2. Tectonic Implication Geodetic results in this paper show clear dextral movement of the fault near KL, revealing the movement of an active secondary fault north of the Marmara Sea. Our results of relative motion (~5 mm/year) on the two sides of KL provide an important indication of the existence of the secondary faults in this area. Combined with previous geological and seismic results [8,16–19], this active fault perhaps can be interpreted as an extension of the northern branch of the NAF (NNAF) [16,18]. This interpretation suggests two possibilities about the evolution of the north branch of the NAF: one is that the NNAF does not bend from NW to WSW at KL and connect to the Ganos fault at the west, but extends to KL northwest [16]; the other one is that the NNAF might separate into two branches, one running northwest into the KL and the other one bending to the WSW direction. For detailed analysis of the two possibilities, more close observations, such as more dense near-ﬁeld GPS [36] and numerical simulations [47], are required. However, no matter which possibility reveals the development of the north branch of the NAF, the active fault beneath KL would play an important role in regional tectonic evolution and related seismic hazard assessment. Fault complexities at fault bends are observed worldwide. These are commonly explained by perturbations induced by geometrical complexities in a main fault trace [4]. During multiple earthquake cycles residual stresses may accumulate and lead to the formation and dislocation of secondary faults. Whether the herein investigated N30◦W trending fault on land is seismogenically active is not known. However, due to the shallow locking depth shown by our modelling result, the KL fault might be subjected to aseismic creep. This perhaps can explain that only a few earthquakes were observed in this area [48]. Remote Sens. 2016, 8, 846 11 of 17

5.3. Local Landslides and Subsidence FaultingRemote Sens. processes 2016, 8, 846 are often associated with topography reshaping, steep erosion and11 of landslide17 processes. Many small-scale landslides have been observed in the Avcilar area (upper left panel in mainly result from the landslides, they should show general spatial correlation with the Figure1),terrain which gradient. may affect the local deformation process. Landslide inventory maps of that region suggest steeperWith slopesthis assumption, to be vulnerable we decomposed for flank the instability.LOS velocities For into this N120°W reason, and one vertical may directions suspect that the observedusing horizontal the method PS-InSAR mentioned deformation in Section 2.2, might where be the induced N120°W bydirection slow landslides.is close to the Many horizontal factors can affect thecomponent development of the ofmaximum landslides, terrain where gradient the in terrain this area. gradient We then has draw a dominant the decomposed role in horizontal controlling the velocities along the selected profile and compared them with the corresponding terrain. As shown in susceptibility and the final movement [9,49]. In other words, if the observed motions in this area mainly Figure 9, no clear spatial correlation was found between the horizontal motion and the slope of the result fromterrain, the even landslides, when filtered they shoulddata were show applied. general We spatialthus conjecture correlation that withthe modelled the terrain long-term gradient. ◦ WithN30°W this PS-InSAR assumption, velocity we is decomposed distinct from that the of LOS the velocitieslocal landslides, into but N120 mainlyW andinduced vertical by stable directions using thetectonic method movement. mentioned in Section 2.2, where the N120◦W direction is close to the horizontal componentThe of the subsidence maximum area terrainat the west gradient of the KL in thisfault area.agrees We well then with draw the results the decomposed of [21] in terms horizontal of velocitieslocation along and the subsidence selected profilerate; however, and compared our result suggests them with thatthe thiscorresponding subsidence shows terrain. also a high As shown spatial correlation with the Haramidere valley, which perhaps was induced by ground water in Figure9, no clear spatial correlation was found between the horizontal motion and the slope of redistribution [21], soli liquefaction [50] or other unknown mechanisms, which deserve future the terrain,investigations. even when The filtered subsidence data area were east of applied. the KL fault We is thus thought conjecture to be mainly that related the modelled to substratum long-term ◦ N30 Wmaterial PS-InSAR consolidation velocity isprocesses, distinct fromas well that as ofthe the human local activity landslides, of this but rapidly mainly growing induced urban by stable tectonicarea movement. [22].

Figure 9. Decomposed horizontal velocity (in the N120°W direction) on the profile and comparisons Figure 9. Decomposed horizontal velocity (in the N120◦W direction) on the profile and comparisons with the topography. The first column shows unfiltered data, while the second column shows the with the topography. The first column shows unfiltered data, while the second column shows the data data filtered with a filtering length of 1 km. In each subfigure, the gray dots show the original data filtered withalong athe filtering profile, and length the red of 1dots km. represent In each the subfigure, mean values the at gray each dotsdistance show along the the original profile. dataThe along the profile,location and of the the red profile dots is representshown in Figure the mean 6. values at each distance along the profile. The location of the profile is shown in Figure6. 6. Conclusions

The subsidenceIn this paper, area we at use the long-t westerm of thePS-InSAR KL fault observation agrees well(2002–2010) with the to resultsinvestigate of [crustal21] in terms deformation and related secondary fault activity southwest of Istanbul. The mean LOS velocity in of location and subsidence rate; however, our result suggests that this subsidence shows also a ascending and descending geometries was decomposed into fault-parallel and vertical direction high spatialmotions, correlation in which the with post-seismic the Haramidere viscoelastic valley, relaxation which effect perhaps of the 1999 was İzmit/Düzce induced earthquakes by ground water redistributionwas removed [21], based soli liquefactionon model-based [50 estimations.] or other Based unknown on the mechanisms,assumption of the which fault deservebehavior future investigations.(i.e., fixed The fault subsidence orientation areaand eaststrike-slip of the character) KL fault inferred is thought from to geological be mainly studies, related our to results substratum materialsuggest consolidation that a secondary processes, fault as near well KL as is the now human active activitywith a dextral of this slip rapidly rate of growing~5 mm/year urban and areaa [22]. shallow locking depth of <1.0 km. The results show the first geodetic evidence about the existence 6. Conclusionsand present activity of secondary faults north of the Marmara Sea, which has valuable implications for regional tectonic evolution and close seismic hazard assessment of the mega city Istanbul. In this paper, we use long-term PS-InSAR observation (2002–2010) to investigate crustal deformationAcknowledgments: and related We secondary thank the editor fault and activity the reviewers southwest for their of insightful Istanbul. comments. The mean This work LOS is velocity supported by the National Natural Science Foundation of China (Grant Numbers 41304017, 41674023 and in ascending41274104) and and descending the New Directions geometries in Seismic was Hazard decomposed Assessment through into fault-parallel Focused Earth Observation and vertical in the direction motions,Marmara in which Supersite the post-seismic (MARSite) project viscoelastic co-funded by relaxation the European effect Commission of the within 1999 Izmit/Düzcethe˙ Seventh Framework earthquakes was removedProgramme based (Grant on No. model-based 308417). Data estimations.processing and Basedanalysison were the made assumption as a contribution of the tofault the European behavior (i.e., ﬁxed fault orientation and strike-slip character) inferred from geological studies, our results suggest that a secondary fault near KL is now active with a dextral slip rate of ~5 mm/year and a shallow locking depth of <1.0 km. The results show the ﬁrst geodetic evidence about the existence and present Remote Sens. 2016, 8, 846 12 of 17 activity of secondary faults north of the Marmara Sea, which has valuable implications for regional tectonic evolution and close seismic hazard assessment of the mega city Istanbul.

Acknowledgments: We thank the editor and the reviewers for their insightful comments. This work is supported by the National Natural Science Foundation of China (Grant Numbers 41304017, 41674023 and 41274104) and the New Directions in Seismic Hazard Assessment through Focused Earth Observation in the Marmara Supersite (MARSite) project co-funded by the European Commission within the Seventh Framework Programme (Grant No. 308417). Data processing and analysis were made as a contribution to the European Space Agency (ESA) Scientiﬁc Exploitation of Operational Missions (SEOM) project Sentinel-1—INSAR performance study with TOPS data (INSARAP, ESA-ESRIN Contract 4000110587/14/I-BG), and to the Helmholtz Alliance on Remote Sensing and Earth System Dynamics (EDA).

Author Contributions:Remote Sens. 2016F.D., 8, 846 and T.R.W. contributed to the data analysis, modeling and12 manuscript of 17 writing. F.M. and M.C. contributed to the InSAR data processing. R.W., S.E., X.X. and P.P. contributed to the result analysis and manuscriptSpace revision. Agency All (ESA) authors Scientific reviewed Exploitation the of manuscript.Operational Missions (SEOM) project Sentinel-1—INSAR performance study with TOPS data (INSARAP, ESA-ESRIN Contract 4000110587/14/I-BG), and to the Helmholtz Conﬂicts of Interest:Alliance Theon Remote authors Sensing declare and Earth no System conﬂict Dynamics of interest. (EDA). Author Contributions: F.D. and T.W. contributed to the data analysis, modeling and manuscript writing. Appendix A F.M. and M.C. contributed to the InSAR data processing. R.W., S.E., X.X. and P.P. contributed to the result analysis and manuscript revision. All authors reviewed the manuscript.

Conflicts of Interest: The authors declare no conflict of interest. Appendix A.1. Quantitative Comparison between GPS and InSAR Observation Appendix A Figure A1 shows the comparison between the GPS observation and the InSAR observation. A.1. Quantitative Comparison between GPS and InSAR Observation We projected the horizontal GPS velocity onto the N30◦E direction and ﬁnd that the horizontal Figure A1 shows the comparison between the GPS observation and the InSAR observation. We InSAR velocitiesprojected are generallythe horizontal comparable GPS velocity onto with the thoseN30°E direction of the and GPS find observation that the horizontal in terms InSAR of magnitude and direction,velocities though are agenerally difference comparable is found with those for of a the few GPS stations. observation Notein terms that of magnitude the inverted and horizontal InSAR velocitiesdirection, on two though continuous a difference is stations found for (AVCTa few stations. and Note KCEK that inthe Figure inverted A1horizontal) ﬁt well InSAR with that of the velocities on two continuous stations (AVCT and KCEK in Figure A1) fit well with that of the GPS observation.GPS observation.

Figure A1. Comparison between the obtained InSAR velocity and the observed GPS velocity in the Figure A1. Comparisonstudy area. The between observed horizontal the obtained GPS velocities InSAR (black velocity arrows) andwere theprojected observed onto the GPSN30°Evelocity in the ◦ study area. Thedirection observed (red arrows). horizontal The horizontal GPS InSAR velocities velocity (black (blue arrows) arrows) on each were GPS projectedstation was the onto the N30 E direction (redaverage arrows). value Theof InSAR horizontal points within InSAR 1 km velocity surrounding (blue the GPS arrows) site. AVCT on eachand KCEK GPS are station was the continuous stations, and others are survey sites. average value of InSAR points within 1 km surrounding the GPS site. AVCT and KCEK are continuous stations, andA.2. othersSynthetic are Tests survey sites. Synthetic tests were carried out in order to investigate how the horizontal and vertical Appendix A.2.deformation Synthetic signals Tests depend on the geometric setting of the ascending and descending data and the fault orientation. The steps are as follows: Synthetic testsWe were firstly carriedassumed a out right in lateral order slip to rate investigate of 5 mm/year how of the the KL fault horizontal (locking depth and = vertical 0) and deformation generate the horizontal velocity field (Figure A2a). Two rectangular subsidence areas with a vertical signals dependvelocity on the of geometric−5 mm/year (Figure setting A2b) of were the added ascending to the horizontal and descending velocity field. data and the fault orientation. The steps are as follows:Secondly, we projected the velocity field that was obtained in the first step to the LOS directions of the ascending track and descending track (Figure A2c,d). We firstly assumedThirdly, we a rightadded noise lateral to the slip generated rate of LOS 5 mm/yearvelocities (Figure of theA2e,f). KL A Gaussian fault (locking noise with depth = 0) and generate the horizontala standard deviation velocity of 2 mm/year field (Figure was used A2 here.a). Two rectangular subsidence areas with a vertical velocity of −5 mm/yearFourthly, we (Figure inverted A2 theb) LOS were velocities added to get to the horizontal horizontal and vertical velocity velocities field. on each point (Figure A2g,h) using the least square method that was mentioned in Section 2.2. Secondly, we projected the velocity field that was obtained in the first step to the LOS directions of the ascending track and descending track (Figure A2c,d). Remote Sens. 2016, 8, 846 13 of 17

Thirdly, we added noise to the generated LOS velocities (Figure A2e,f). A Gaussian noise with a standard deviation of 2 mm/year was used here. Fourthly, we inverted the LOS velocities to get the horizontal and vertical velocities on each point (Figure A2g,h) using the least square method that was mentioned in Section 2.2. Remote Sens. 2016, 8, 846 13 of 17 At last, we low-pass filtered the obtained horizontal and vertical field to remove the high-frequencyAt last, signalswe low-pass as has filtered been done the inobtained the main horizontal text. A and filtering vertical length field of to 1 kmremove is used the here, but similarhigh-frequency results signals can be as obtained has been withdone filteringin the main lengths text. Aof filtering 2 km andlength 3 kmof 1 km (as is shown used here, by Figure but 6). The detailedsimilar results comparison can be obtained of the input with filtering and the leng recoveredths of 2 km horizontal and 3 km signal (as shown along by the Figure selected 6). The profile was showndetailed in comparison Figure A3 of. the input and the recovered horizontal signal along the selected profile was shownOther in Gaussian Figure A3. noise levels (e.g., with standard deviations of 1 mm/year and 3 mm/year) Other Gaussian noise levels (e.g., with standard deviations of 1 mm/year and 3 mm/year) were were also tested, and a similar result can be obtained. In addition, we also used the also tested, and a similar result can be obtained. In addition, we also used the random random spatially-correlated noise generated from the variance-covariance matrix (as mentioned spatially-correlated noise generated from the variance-covariance matrix (as mentioned in Section 4) in Sectioninstead4) insteadof the Gaussian of the Gaussian noise in noise the insyntheti the syntheticc test, and test, similar and similar results results can be can obtained be obtained (Figures(Figures A4 andA4 and A5 ).A5). FromFrom the the synthetic synthetic tests, tests, we we find find that that thethe input signal signal can can generally generally be well be well recovered recovered with withthe the geometricgeometric setting setting of theof the InSAR InSAR data data and and thethe faultfault orientation in in this this study. study.

FigureFigure A2. A2.Synthetic Synthetic data data that that was was used used forfor thethe test. ( (aa,b,b) )Synthetic Synthetic horizontal horizontal ((N30°W) ((N30 ◦andW) vertical and vertical velocityvelocity fields; fields; (c,d ()c, Generatedd) Generated LOS LOS velocity velocity fieldsfields in in the the ascending ascending and and descending descending geometric geometric setting; setting; (e,f) Inferred from (c,d) by adding noise; (g,h) Horizontal and vertical velocity that were inverted (e,f) Inferred from (c,d) by adding noise; (g,h) Horizontal and vertical velocity that were inverted from from noised synthetic LOS velocities (e,f); (i,j) Low-pass filtered horizontal and vertical velocity field noised synthetic LOS velocities (e,f); (i,j) Low-pass filtered horizontal and vertical velocity field (with a (with a filtering length of 1 km) that were obtained from (g,h). filtering length of 1 km) that were obtained from (g,h).

Remote Sens. 2016, 8, 846 14 of 17 Remote Sens. 2016, 8, 846 14 of 17 Remote Sens. 2016, 8, 846 14 of 17

◦ FigureFigureFigure A3.A3. Input Input (black (black(black dot) dot)dot) and andand recovered recoveredrecovered (red (red(red dot) dot)dot) horizontal horizontalhorizontal velocity velocityvelocity (N30°W) (N30(N30°W)W) on onon profiles proﬁlesprofiles perpendicularperpendicularperpendicular to to to the the orientation orientation of of the the fault fault (Figure (Figure A2a,i).A2 A2a,i).a,i).

Figure A4. Similar to Figure A2, but for the synthetic test using the spatially-correlated noise instead FigureFigure A4. A4. SimilarSimilar to to Figure Figure A2,A2, but but for for the the synthetic synthetic test test using using the the spatially-correlated spatially-correlated noise noise instead instead of the Gaussian noise. (a,b) Synthetic horizontal ((N30°W) and vertical velocity fields; (c,d) Generated ofof the the Gaussian Gaussian noise. noise. ( (aa,,bb)) Synthetic Synthetic horizontal horizontal ((N30°W) ((N30◦W) an andd vertical vertical velocity velocity fields; fields; ( (cc,,dd)) Generated Generated LOS velocity fields in the ascending and descending geometric setting; (e,f) Inferred from (c,d) by LOSLOS velocity velocity fields fields in in the the ascending ascending and and descending descending geometric geometric setting; setting; ( (ee,f,f)) Inferred Inferred from from ( (cc,,dd)) by by adding noise; (g,h) Horizontal and vertical velocity that were inverted from noised synthetic LOS addingadding noise; noise; ( (gg,,hh)) Horizontal Horizontal and and vertical vertical velocity velocity that that were were inverted inverted from from noised noised synthetic synthetic LOS LOS velocities (e,f); (i,j) Low-pass filtered horizontal and vertical velocity field (with a filtering length of 1 velocitiesvelocities ( (ee,,ff);); ( (ii,j,j)) Low-pass Low-pass filtered filtered horizontal horizontal and and vertical vertical velocity velocity field field (with (with a filtering a filtering length length of of1 km) that were obtained from (g,h). km)1 km) that that were were obtained obtained from from (g (,gh,).h ).

Remote Sens. 2016, 8, 846 15 of 17 Remote Sens. 2016, 8, 846 15 of 17

Figure A5. InputInput (black (black dot) dot) and and recovered recovered (red (red dot) horizontal velocity (N30(N30°W)◦W) on profiles proﬁles perpendicular to the orientation of the faultfault (Figure(Figure A4A4a,i).a,i). Similar Similar to to Figure A3,A3, but using the spatially-correlated noise instead of the Gaussian noisenoise inin thethe syntheticsynthetic test.test.

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