Interseismic strain accumulation across the central North Anatolian Fault from iteratively

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Hussain, E. and Hooper, A. and Wright, T. J. and Walters, R. J. and Bekaert, D. P. S. (2016) ’Interseismic
strain accumulation across the central North Anatolian Fault from iteratively unwrapped InSAR
measurements.’, Journal of geophysical research : solid earth., 121 (12). pp. 9000-9019.
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https://doi.org/10.1002/2016JB013108
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Journal of Geophysical Research: Solid Earth
RESEARCHARTICLE Interseismic strain accumulation across the central North
10.1002/2016JB013108 Anatolian Fault from iteratively unwrapped InSAR
measurements
KeyPoints:
•Newiterativephase-unwrapping
procedureimprovescoverageof Ekbal Hussain1,Andrew Hooper1,TimJ. Wright1,RichardJ. Walters2,andDavidP.S. Bekaert3,4
InSARmeasurements
•Surfacevelocitiesatthecentral
1COMET,SchoolofEarthandEnvironment,UniversityofLeeds,Leeds,UK,2COMET,DepartmentofEarthSciences,
NorthAnatolianFaultshoweastward
decreaseinsliprate DurhamUniversity,Durham,UK,3JetPropulsionLaboratory,CaliforniaInstituteofTechnology,Pasadena,California,USA,
•FaultcreepnearIsmetpasaisreleasing 4FormerlyatCOMET,UniversityofLeeds,Leeds,UK
only30-40%oflong-termstraininthe
shallowcrust
Abstract TheNorthAnatolianFault(NAF)isamajortectonicfeatureintheMiddleEastandisthemost
SupportingInformation: activefaultinTurkey.ThecentralportionoftheNAFisaregionofGlobalNavigationSatelliteSystems
•SupportingInformationS1 (GNSS)scarcity.Previousstudiesofinterseismicdeformationhavefocusedontheaseismiccreepnearthe
townofIsmetpasausingradardataacquiredinasingleline-of-sightdirection,requiringseveralmodeling
Correspondenceto: assumptions.WehavemeasuredinterseismicdeformationacrosstheNAFusingbothascendingand
E.Hussain, descendingdatafromtheEnvisatsatellitemissionacquiredbetween2003and2010.Ratherthanrejecting
[email protected]
incorrectlyunwrappedareasintheinterferograms,wedevelopanewiterativeunwrappingprocedure
forsmallbaselineinterferometricsyntheticapertureradar(InSAR)processingthatexpandsthespatial
Citation: coverage.Ourmethodcorrectsunwrappingerrorsiterativelyandincreasestherobustnessofthe
Hussain,E.,A.Hooper,T.J.Wright,
unwrappingprocedure.WeremovelongwavelengthtrendsfromtheInSARdatausingGNSSobservations
R.J.Walters,andD.P.S.Bekaert(2016),
Interseismicstrainaccumulationacross anddeconvolvetheInSARvelocitiesintofault-parallelmotion.Profilesoffault-parallelvelocityreveala
thecentralNorthAnatolianFault systematiceastwarddecreaseinfaultslipratefrom30mm/yr(25–34,95%confidenceinterval(CI))to
fromiterativelyunwrappedInSAR
21mm/yr(14–27,95%CI)overadistanceof∼200km.Directoffsetmeasurementsacrossthefaultreveal
measurements,J.Geophys.Res.
SolidEarth,121,9000–9019, faultcreepalonga∼130kmsectionofthecentralNAF,withanaveragecreeprateof8±2mm/yranda
doi:10.1002/2016JB013108. maximumcreeprateof14±2mm/yrlocated∼30kmeastofIsmetpasa.Asfaultcreepisreleasingonly
30–40%ofthelong-termstrainintheshallowcrust,thefaultisstillcapableofproducinglarge,damaging
Received20APR2016 earthquakesinthisregion.
Accepted27NOV2016
Acceptedarticleonline7DEC2016
Publishedonline19DEC2016
1.Introduction
TheNorthAnatolianFault(NAF)isamajorcontinentalright-lateraltransformfaultlocatedinnorthernTurkey.
TogetherwiththeEastAnatolianFault,itfacilitatesthewestwardmotionofAnatolia,caughtintheconver-
gencezoneoftheEurasianplatewiththeArabianplate[McKenzie,1972].Sincethe1939M 7.9Erzincan
w
earthquakeineasternTurkey,theNAFhasrupturedinasequenceoflarge(M >6.7)earthquakeswithadom-
w
inantwestwardprogressioninseismicity[Barka,1996;Steinetal.,1997].Steinetal.[1997]andHubert-Ferrari
etal.[2000]haveinterpretedthissequencetoresultfromstresstransferalongstrike,whereoneearthquake
bringstheadjacentsegmentclosertofailure.
InordertounderstandtherolethattheNAFplaysinregionaltectonicsandseismichazard,therehavebeen
numerousestimatesofthefaultslipratefortheNAFusingpresent-daydeformationmeasuredwithGlobal
NavigationSatelliteSystems(GNSS)[e.g.,Straubetal.,1997;Reilingeretal.,2006;Ergintavetal.,2009]oroffset
geologicalfeatures[e.g.,Hubert-Ferrarietal.,2002;Puccietal.,2008;Kozacietal.,2009].Therehavealsobeen
severalinterferometricsyntheticapertureradar(InSAR)-derivedestimatesofthefaultsliprate,whichhave
focusedonthewesternoreasternregionsoftheNAFwheretheInSARcoherenceisbetter[e.g.,Wrightetal.,
2001a;Cakiretal.,2005;Waltersetal.,2011;Kanekoetal.,2013;Cakiretal.,2014;Cetinetal.,2014;Waltersetal.,
©2016.TheAuthors.
Thisisanopenaccessarticleunderthe 2014;CavaliéandJónsson,2014;Hussainetal.,2016].
termsoftheCreativeCommons
AttributionLicense,whichpermitsuse, However,sliprateestimatesforthecentralNAFarerelativelypoorlyconstrained,withsparseGNSSdatanorth
distributionandreproductioninany
ofthisportionofthefault(Figure1)andwideranginggeologicalandgeodeticestimates.Geologicalfault
medium,providedtheoriginalworkis
properlycited. slipraterangesfromaslowas5mm/yrtoashighas44mm/yr[e.g.,BarkaandHancock,1984;Barka,1992;
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Figure1.(a)ThecentralsectionoftheNorthAnatolianFault.TheredarrowsarepublishedGNSSvelocitiesfromthe
GlobalStrainRateModelproject[Kreemeretal.,2014].Thecoloredsectionsindicatepreviousrupturesalongthissection
ofthefault.(b)TheEnvisatsatellitedatatracksusedinthisstudy.Descendingtracksarecoloredinredandascending
tracksinblue.
Hubert-Ferrarietal.,2002;Kozacietal.,2007;Kozacietal.,2009],whileGNSSstudiesestimatetheslipratefor
theregiontoarangeof17–34mm/yr[e.g.,Oraletal.,1993;Noomenetal.,1996;Ayhanetal.,2002;Reilinger
etal.,2006].
Shallowaseismicsliponthefaultplane,i.e.,faultcreep,onthecentralportionoftheNAFwasfirstdocumented
byAmbraseys[1970],whoobservedincreasingdisplacementsofawallthatwasbuiltacrossthefaultnear
thetownofIsmetpasa,overmultipleyears.Ambraseys[1970]estimatedafaultcreeprateof∼20mm/yrfor
thetimeperiod1955–1969.Sincethisoriginalinvestigation,thefaultcreephasbeenthefocusofnumerous
geodeticstudies[e.g.,Cakiretal.,2005;Kutogluetal.,2010;Karabacaketal.,2011;Ozeneretal.,2013;Cetinetal.,
2014].Cetinetal.[2014]suggestedthatthefaultcreepratehasbeendecayingsincethefirstmeasurementsin
1970toacurrentsteadystatevalueof∼6–8mm/yr.MostpreviousInSARstudiesinthisregionhaveonlyused
satellitedatafromasingle-lookdirection,e.g.,theuseofdescendingEnvisatdatabyCakiretal.[2005]and
Cetinetal.[2014].Kanekoetal.[2013]usedacombinationofascendingtracksfromtheALOSsatelliteandone
descendingframefromEnvisattrack207,limitingtheirobservationalperiodto2007–2011.Theysuggested
thataseismiccreepatarateof∼9mm/yrislimitedtotheupper5.5–7kmofthecrust,whichexhibitsvelocity
strengtheningfrictionalbehavior.
Recently,Roussetetal.[2016]usedhigh-resolutionCOSMO-SkyMedsatellitedataspanningthetimewindow
betweenJuly2013andMay2014toshowevidenceofperiodsofelevatedfaultcreepspanningamonth
withtotalslipof20mm,indicatingthatepisodiccreepeventsmaybeanimportantmechanismproducing
aseismicslip.
InthisstudyweuseamorecompletedatasetcoveringtheentirecentralNAFinbothascendinganddescend-
inggeometriesandspanningthe∼8yeartimewindowbetween2003and2010.Weremovelongwavelength
trendsfromtheInSARdatausingpublishedGNSSvelocities[Kreemeretal.,2014]anddeconvolvetheInSAR
line-of-sightvelocitiesintofault-parallelandverticalmotion.
Weusesimpleelasticdislocationmodelstoestimategeodeticfaultslipratesandlockingdepthsandinves-
tigatethespatialvariationoffaultcreepalongthecentralNAF.Wealsodevelopandapplyanewiterative
unwrappingalgorithmthatminimizesunwrappingerrorsduringtheInSARprocessing.
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Table1.DataCoverageforEachEnvisatTrackUsedinThisStudy
Track Geometry TimeSpana No.ofImages TotalIntsCreated IntsUsed
250 Descending 2003/12/12–2010/7/23 38 115 59
479 Descending 2003/12/28–2010/7/4 30 90 50
207 Descending 2004/1/13–2010/9/28 40 88 53
436 Descending 2003/7/3–2010/3/18 36 96 65
28 Ascending 2004/7/28–2010/7/7 14 30 21
71 Ascending 2004/1/3–2009/8/29 19 48 29
343 Ascending 2004/6/10–2010/4/15 14 27 20
aDatesareformattedasyear/month/day.
2.InSARProcessing
Ourdatasetconsistsof191Envisatimagesfromfourdescendingtracks(250,479,207,and436)andthree
∘ ∘
ascendingtracks(28,71,and343)(Figure1b).TogetherthesecoverthecentralNAFbetween31.5 Eand35 E,
andspanthetimeinterval2003–2010.DetailsoftheprocesseddataforeachtrackaregiveninTable1.
WefocustheEnvisatimagesusingROI_PAC[Rosenetal.,2004]andusetheDORISsoftware[Kampesetal.,2003]
toconstruct494interferograms.Foreachtrackweproducearedundantconnectednetworkofinterferograms
whileminimizingthetemporalseparationbetweenacquisitionsandthespatialseparationofthesatellite(the
perpendicularbaseline)(FigureS1inthesupportinginformation).Wecorrecttopographiccontributionsto
theradarphaseusingthe90mSRTMDigitalElevationModel[Farretal.,2007]andaccountfortheknown
oscillatordriftforEnvisataccordingtoMarinkovicandLarsen[2013].Weunwraptheinterferometricphase
usinganewiterativeunwrappingprocessdescribedinsection3.
WeapplytheStaMPS(StanfordMethodforPersistentScatterers)smallbaselinetimeseriestechnique[Hooper,
2008;Hooperetal.,2012]toremoveincoherentpixelsandreducethenoisecontributiontothedeformation
signal,byselectingonlythosepixelsthathavelowphasenoiseonaverageinthesmallbaselineinterferograms
usedintheanalysis.
Theatmosphericcontributionisoftenthelargestsourceoferrorinradarinterferograms[e.g.,Doinetal.,2009;
Waltersetal.,2013;Jolivetetal.,2014;Bekaertetal.,2015a].Tomitigatethisweestimatedatropospherecorrec-
tionusingauxiliarydatafromtheERA-Interimglobalatmosphericmodelreanalysisproduct[Deeetal.,2011].
WeusetheTRAIN(ToolboxforReducingAtmosphericInSARNoise)softwarepackage[Bekaertetal.,2015c]to
correcteachindividualinterferogramfortroposphericnoise.Afterremovingaplanarphaserampfromeach
interferogram,theERA-Icorrectionreducesthestandarddeviationofourtracksby8%onaverage.Theaver-
agereductioninstandarddeviationissmallaftercorrection,implyingthatsomeresidualatmosphericsignals
remainintheinterferogramsaftertheERA-Icorrection.Theaveragereductioninstandarddeviationforeach
trackare10%fortrack207,1%fortrack250,2%fortrack436,12%fortrack479,10%fortrack28,16%for
track71,and6%fortrack343(FiguresS2andS3).
Ourfinalredundantsmallbaselinenetworksconsistofatotalof297interferogramsovertheseventracks
(FigureS1).Weusethesenetworkstocalculatetheaverageline-of-sight(LOS)velocitymapforeachtrack.
Anynontectoniclongwavelengthsignals(>100km),includingthoseduetoorbitalerrors,areeffectively
removedfromeachtrackwhentheInSARline-of-sight(LOS)velocitiesaretransformedintoaEurasia-fixed
GNSSreferenceframe(detailsinsection4).Theuncertaintiesonthefinalvelocityforeachpixelarecalcu-
latedusingbootstrapresampling[EfronandTibshirani,1986]andarepresentedatthe1sigmalevelinthe
followingwork.
WecalculatetheLOSvariance-covariancematrixofthenoiseforeachInSARtrackbycomputingtheaverage
radialcovarianceversusdistance(autocorrelation)usingthevelocitiesina50kmby50kmregion∼250kmto
thesouthofthefault.Thisregionisassumedtohavenotectonicdeformationandcontainsonlyatmospheric
noise.Wefitanexponentialcovariancefunction[e.g.,LohmanandSimons,2005;Parsonsetal.,2006],C(r),as
C(r)=𝜎2e−𝜆r, (1)
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Table2.TheCenterofthe50kmby50kmRegionUsedtoEstimatetheNoise
CovarianceFunctionParameters
Track Center(Lon,Lat) Variance,𝜎2(mm/yr)2 CharacteristicLength,𝜆(km)
207 33∘E,39.5∘N 8.91 53
250 31.75∘E,39.5∘N 4.95 27
436 34∘E,39.5∘N 3.91 22
479 32.5∘E,39.5∘N 2.88 10
28 34.5∘E,39.5∘N 6.12 25
71 33.2∘5E,39.5∘N 4.00 19
343 32.5∘E,39.5∘N 1.00 4
whereweestimatethevariance(𝜎2)andthecharacteristiclength(𝜆),whichgivethespatialcorrelationof
noiseasafunctionofdistancebetweenpixels(r).Ourvaluesforeachtrackandthecenteroftheregion
usedtocalculatethecovariancefunctionareshowninTable2.Thesecovariancesareusedinsection5when
modelingthehorizontalvelocitiesandfaultcreeprates.
3.IterativePhaseUnwrapping
3.1.MethodDescription
Phase unwrapping is the process of recovering continuous phase values from phase data that are mea-
suredmodulo2𝜋radians(wrappeddata)[GhigliaandPritt,1998].Original2-Dphase-unwrappingalgorithms
unwrappedthephaseofeachindividualinterferogramindependently[e.g.,Goldsteinetal.,1988;Costantini,
1998; ZebkerandLu, 1998]. However, a time series of selected interferogram pixels can be considered a
3-D data set, the third dimension being that of time.HooperandZebker [2007] showed that treating the
unwrappingproblemasone3-Dproblemasopposedtoaseriesof2-Dproblemsleadstoanimprovement
intheaccuracyofthesolutioninasimilarwaytowhich2-Dunwrappingprovidesanimprovementover
one-dimensionalspatialmethods.
Fully 3-D phase-unwrapping algorithms commonly assume that the phase difference between neighbor-
ingpixelsisgenerallylessthanhalfaphasecycle(2𝜋radians)inalldimensions[HooperandZebker,2007].
However,duetoatmosphericdelays,InSARsignalsareeffectivelyuncorrelatedintime,violatingthisassump-
tion.Otherunwrappingalgorithmsrequiretheassumptionofatemporalparametricfunction,suchasalinear
phaseevolutionintime[Ferrettietal.,2001],tounwrapthephasesignals.
Thestandard unwrappingalgorithmused inthe StanfordMethodfor PersistentScatterers (StaMPS)soft-
ware[Hooper,2010]usestheactualphaseevolutionintimetoguideunwrappinginthespatialdimension
without assuming a particular temporal evolution model. The phase difference between nearby pixels
(double-differencephase)isfilteredintimetogiveanestimateoftheunwrappeddisplacementphaseforeach
satelliteacquisitionandanestimateofthephasenoise.Thisisusedtoconstructprobabilitydensityfunctions
foreachunwrappeddouble-differencephaseineveryinterferogram.Anefficientalgorithm(SNAPHU)[Chen
andZebker,2000,2001]thensearchesforthesolutioninspacethatmaximizesthetotaljointprobability,i.e.,
minimizesthetotal‘cost’.
Foraconnectednetworkofsmallbaselineinterferograms,thephaseunwrappingofindividualinterferograms
canbecheckedfornetworkconsistencybysummingthephasearoundclosedinterferometricloops[e.g.,Pepe
andLanari,2006;Biggsetal.,2007;Cavaliéetal.,2007;Jolivetetal.,2011](Figure2).Inthestandardunwrapping
approachusedinStaMPS,anyinterferogramsidentifiedtohavelargeunwrappingerrorsareremovedfrom
thesmallbaselinenetwork,whichcanresultinlossofinformationand/orreductioninnetworkredundancy.
NotethatsomeotherInSARpractitioners[e.g.,Biggsetal.,2007;Wangetal.,2009;Waltersetal.,2011]generally
donotdropbadlyunwrappedinterferograms,butattempttocorrectunwrappingerrorsbymanuallyadding
integermultiplesof2𝜋tobadlyunwrappedregionsofpixels.However,thisisatime-consumingprocess.
In our method, we iterate the standard StaMPS unwrapping procedure while calculating the sum of the
unwrappedphasearoundclosedloopsforeverypixelineveryinterferogram,usingthefollowingequation:
∑n−1
UW{𝜙 −𝜙}+𝜖=0, (2)
(i+1)modn i
i=0
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Figure2.Asimpleinterferometricloopconsistingofthreeacquisitions(redpoints)withphase𝜙 .Theinterferograms
0∶2
aredenotedbythebluelinesandarethedifferenceinphasefortwoacquisitions.UWistheStaMPSunwrapping
operator,seetextfordetails.Foreverypixelunwrappedcorrectlyineachinterferogramthephasesumaroundtheloop
isequaltozero,i.e.,UW(𝜙 −𝜙 )+UW(𝜙 −𝜙 )+UW(𝜙 −𝜙 )=0.
1 0 2 1 0 2
whereUWistheStaMPSunwrappingoperator,nisthenumberofinterferogramsonthepatharoundaninter-
ferometricloop,(𝜙 −𝜙)aretheinterferometricphasevaluesofapixelintheinterferogramscreatedbycal-
i+1 i
culatingthephasedifferencebetweenimagei+1andirelativetoareferencepoint,and𝜖istheerrorterm.The
referencepointischosentobenorthofthefaultforalltracks.Anypixelssatisfyingtherequirementof|𝜖|<1
radaredefinedas“error-freepixels”andareassumedtobecorrectlyunwrapped.Anerrortermisneeded
becausetheinterferogramsaremultilookedbeforeunwrapping,andsowedonotexpecttohaveperfectclo-
surearoundeachinterferometricloop.Using𝜖=1isreasonableasitiswellbelowthe2𝜋radiansrequiredto
produceunwrappingerrorsandallowsforasmallamountofclosureerrorintroducedbythenonlinearnature
ofmultilooking.Inourtestssetting𝜖to0.5madenosignificantimpactontheacceptancerates.
In each iteration, we keep all unwrapping parameters fixed (such as the number of interferograms and
filtering)butassumethatpixelsidentifiedaserror-freeinthepreviousiterationarelikelyunwrappedcorrectly
andapplyahighcosttochangingthephasedifferencebetweenthesepixelsinthenextiteration.TheStaMPS
unwrappingalgorithmusesthedouble-differencephaseevolutionintimetocalculateaprobabilitydensity
functionofunwrappedphaseforeachpixelpairineachinterferogram.Forinterferogramswherebothpixels
inapairareidentifiedasunwrappedcorrectly,wesettheweightingto100timesthoseoftheotherinterfer-
ograms,toeffectivelyensurethattheevolutionintimeisfixed.Inthisway,theiterativeunwrappingmethod
usestheerror-freepixelsasaguidetounwrappingtheregionsthatcontainedunwrappingerrorsinprevious
iterations.
López-Quirozetal.[2009]describeaprocesswhereunwrappingisiteratedontheresidualinterferogramafter
theremovalofanestimateofthedeformationsignalwhileourtechniqueiteratestheStaMPSunwrapping
procedureontheactualinterferometricphase.
3.2.TestingtheIterativeUnwrappingProcedure
We tested the new algorithm on data from Envisat descending track 207, which covers a region roughly
100kmby400kmincentralTurkey(Figure1b).Eachiterationconsistsofthefollowingsteps:runningthe
StaMPSunwrappingalgorithm,determiningthepixelsunwrappedcorrectlyineachinterferogramusingthe
methoddescribedaboveandinAppendixA,applyingahighcosttounwrappingacrossthesepixels,and
rerunningtheunwrappingalgorithmagain.Weiteratethisprocedure30times.Theresultsfromstandard
unwrappingdoesnotchangeasnomodificationsaremadetoitsinputsandisrepresentedbythestraight
lineindicatingnochangeinthenumberoferror-freepixelsperiteration.Figure3showsthatthepercentage
oferror-freepixelsintheentiresmallbaselinenetworkincreasessharplywiththefirsteightiterationsfrom
70%to83%,reachingamaximumof84%after30iterations;meaningthattherearesomeunwrappingerrors
themethodisunabletofix.Thisisalsoevidentfromtheindividualinterferograms(Figure4),whichshowthis
samerapidincreaseinthepercentageoferror-freepixelsfollowedbyaplateau.Itisclearthattherearesome
unwrappingerrorsthatcannotbecorrected(bluecolorsinFigure5)usingtheiterativemethod.However,
theiterativeproceduregreatlyreducesthetotalnumberofunwrappingerrorsandthusincreasestheInSAR
coveragewhileminimizingerrors.
Aftereightiterationsthepercentageoferror-freepixelsincreasedfrom90%to94%fortrack250,from65%
to80%fortrack436,from92%to95%fortrack479,from83%to87%fortrack343,from71%to77%fortrack
28,andfrom91%to93%fortrack71.
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Figure3.Totalpercentageofpixelsinthesmallbaselinenetworkfordescendingtrack207thatwereidentifiedas
closed,i.e.,correctlyunwrapped,usingouriterativeunwrappingprocedure(blue)andthestandardunwrapping(red)
algorithm.Thereisarapidincreaseinthenumberoferror-freepixelsforthefirsteightiterationsafterwhichitreachesa
plateau.Asnomodificationismadetotheinputoftheunwrappingalgorithm,thereisnochangeforeachiterationof
thestandardunwrappingalgorithm.
4.InterseismicVelocityFieldAcrosstheCentralNAF
Toinvestigatethepatternofinterseismicstrainaccumulationalongthefault,wedecomposeourfullInSAR
velocityfieldintothefault-parallelandfault-perpendicularcomponentsofmotion.Followingthemethod
describedinHussainetal.[2016],wedothisfirstbyresamplingourInSARLOSvelocities(Figure6)ontoa
1kmby1kmgridencompassingthespatialextentofallourtracks.Weuseanearestneighborresampling
techniqueincludingonlythosepersistentscattererpixelswithanearestneighborwithin1kmofthecenter
ofeachgridpoint.WereferenceeachtracktoaEurasia-fixedGNSSreferenceframebyfirstaveragingthe
InSARvelocitiesthatfallina1kmradiusaroundeveryGNSSstationwithintheboundariesofeachInSAR
track.WeprojecttheGNSSvelocitiesintothelocalsatellitelineofsightandcalculatethedifferencefromthe
Figure4.Changesinthepercentageoferror-freepixels(correctlyunwrappedpixels)periterationshownforselected
interferograms.Inbluearethechangesfortheiterativeunwrappingalgorithm,whileredindicatesthestandard
unwrapping.
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Figure5.Evolutionofthenumberoferror-freepixels(correctlyunwrappedpixels)periterationshownforinterferogram29.Error-freepixelsareidentifiedinred,
whilepixelsthatdidnotclose,i.e.,haveunwrappingerrors,areinblue.TheunwrappedphaseforeachiterationisshowninFigureS7inthesupporting
information.
InSARvelocities.TheverticalcomponentoftheGNSSvelocitiesarenotavailableontheGlobalStrainRate
Modelwebsite.Ergintavetal.[2009]showedthattheverticalGNSScomponentissmallandverynoisyover
westernTurkey;therefore,weonlyusethehorizontalvelocitiesinouranalysis.Wedeterminethebestfitplane
throughtheresidualvelocitiesandremovethisfromtheInSARvelocitiestotransformtheLOSvelocitiesinto
aEurasia-fixedGNSSreferenceframe.Thisprocedureisdoneseparatelyforeachtrack.
Toestimatetheuncertaintiesinthedata,wecalculatetheRMSresidualinhorizontalvelocitiesintheover-
lappingareasbetweenneighboringtracksassumingnegligibleverticalmotion(FigureS4).Theresidualsare
approximatelyGaussianwithmeanvaluesclosetozero.TheaverageRMSmisfitis5mm/yr,whichgivesan
empiricaluncertaintyof∼4mm/yrfortheindividualtracks.
Foreverypixelwhereinformationfrombothascendinganddescendinggeometriesareavailable,weuse
equation(3)toinvertfortheeast-westandverticalcomponentsofmotionfollowingthemethoddescribed
byWrightetal.[2004]andHussainetal.[2016]whiletakingintoaccountthelocalincidenceangles:
⎡D ⎤
E
D =[sin(𝜃)cos(𝛼) −sin(𝜃)sin(𝛼) −cos(𝜃)]⎢D ⎥, (3)
LOS ⎢ N⎥
⎣D ⎦
U
whereD istheLOSvelocity,𝜃isthelocalradarincidenceangle,𝛼istheazimuthofthesatelliteheading
LOS
vector,and[D ,D ,D ]Tisavectorwiththeeast,north,andverticalcomponentsofmotion,respectively.
E N U
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Figure6.Descendingandascendingline-of-sightvelocitieswitheachtrackreferencedtoaEurasiafixedGNSSreference
frame.Redcolorsindicatemotionawayfromthesatellite,whilebluecolorsindicatemotiontowardthesatellite.
Equation (3) contains three unknowns (D ,D and D ), but we only have two input velocities with large
E N U
differences in satellite look angle in the inversion (the ascending and descending InSAR LOS velocities).
Therefore,itisimpossibletocalculatethefull3-Dvelocityfieldwithoutapriorassumption.Thecommon
assumptionmadeinpreviousstudiesisthatthereisnoverticalmotionacrosstheregionofinterest[e.g.,
Waltersetal.,2014;Hussainetal.,2016].Inourcasewenotethatboththeascendinganddescendingtracksare
equallyinsensitivetomotioninthenorth-southdirection.Wethereforeusethesmoothinterpolatednorth
componentoftheGNSSvelocities(FigureS5)toconstrainthenorth-southcomponent(D )intheinversion,
N
andsolvefortheeast-westandverticalcomponentsofmotionusingtheInSARLOSvelocities.Wecalculate
thefault-parallelcomponentofthehorizontalvelocitybyassumingthatmotionoccursonastrike-slipfault
∘
trendingatN81 E.
Ourfault-parallelvelocities(Figure7a)showtheexpectedright-lateralinterseismicmotionacrosstheNAF,
with red colors representing motion to the northeast and blue to the southwest. Our estimated vertical
componentshowsthatthereislittleverticalmotionacrosstheNAFinthisregion(Figure7b).
Thereisarelativelysharpchangeinfault-parallelvelocitysouthoftheNAF(Figure7)thatcoincideswiththe
B-B′profileline.Webelievethatthisisduetoacombinationofpostseismicdeformationfromthe2000Orta
HUSSAINETAL. INTERSEISMICCENTRALNAF 9007
Journal of Geophysical Research: Solid Earth
10.1002/2016JB013108
Figure7.LOSInSARvelocitiesdecomposedintothe(a)fault-paralleland(b)verticalcomponentsofmotion,wherethe
north-southcomponentisconstrainedbytheGNSSnorthcomponent(FigureS5),seetextfordescription.Negative
fault-parallelvelocitiesindicatemotiontowardthewest,andnegativefault-perpendicularvelocitiesindicatemotionto
thesouth.UncertaintymapsforthesecomponentsareinFigureS6.ThelineslabeledA-A′,B-B′,andC-C′areprofiles
throughthefault-parallelvelocityshowninFigure8.EarthquakemomenttensorsarefromtheGlobalCentroidMoment
Tensorcatalogforalleventsgreaterthanmagnitude4between1976and2016.The2000M 6Ortaearthquake
w
locationisshowninFigure7a.
earthquake(M 6)[Taymazetal.,2007],residualatmosphereintroducedmainlyfromascendingtrack71,and
w
postseismicdeformationfromthe1999IzmitandDüzceearthquakes.
5.ModelingProfileVelocities
Weanalyzethreeprofilesacrossthefaultwherevelocitiesfromwithin20kmareprojectedontotheprofiles
showninFigure7a.Waltersetal.[2014]notedthatthereisavariationinthefault-parallelvelocityawayfrom
thefaultthatisnotduetointerseismicloadingbutduetotheproximitytotheEulerpoleofrotation.For
example,GNSSvelocitiespresentedbyNocquet[2012]showfault-parallelvelocityvectorswithmagnitude
∼25mm/yrclosetotheNAFbut∼8mm/yrinCyprusroughly800kmawayfromthefault.Thisvariationis
mostlyduetotheproximityoftheCyprusGNSSstationstothepoleofrotationofAnatoliawithrespectto
Eurasia.WeusethepoleofrotationcalculatedforAnatoliawithrespecttoEurasiabyReilingeretal.[2006],
∘ ∘ ∘
whoestimatedarotationrateof1.23 /Myraboutapolelocatedat32.1 E,30.8 NneartheNiledelta.Ina
Eurasia-fixedreferenceframethisrotationeffectonlyappliestotheregionsouthoftheNAFandcorresponds
toavalueof𝜃 =0.0215mm/yr/kmor2.15mm/yratadistanceof100kmfromthefault.
rot
Assumingthefault-parallelvelocitiesfartosouthofthefault(>200km)aremostlyduetoatmosphericnoise
and contain no tectonic deformation, we calculate the variance-covariance matrix of the noise using the
∘ ∘
methoddescribedinsection2,usingvelocitiesfroma50kmby50kmregioncenteredon32.5 E,39 N.The
estimatedvariance(𝜎2)andcharacteristiclength(𝜆)forthecovariancefunction(equation(1))is6.35(mm/yr)2
and35.8km,respectively.
ProfilesA-A′ andC-C′ donotcrossthecreepingsectionofthefault.Fortheseprofileswefita1-Dmodel
[Savage and Burford, 1973] through the profiles where the fault-parallel velocity, v , at a fault normal
par
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