Uplift of the 2004 Sumatra-Andaman earthquake measured from differential hyperspectral imagery of...

transcript

Uplift of the 2004 Sumatra-Andaman earthquake measured

from differential hyperspectral imagery of coastal waters

Sebastien Smet,1,2 Remi Michel,1 and Laurent Bollinger1

Received 8 August 2007; revised 1 March 2008; accepted 21 May 2008; published 9 September 2008.

[1] We describe a procedure to measure coseismic change of shallow coastal bathymetryusing multispectral imagery. This technique is applied to HYPERION hyperspectralimages acquired along the shallow coast of the North Andaman Islands to estimate theuplift induced by the Mw 9.15, 26 December 2004, earthquake. Attenuation coefficient ofthe particularly clear coastal waters is estimated from two preevent images with a 22 cmtide level difference. Various sources of noise on the estimate of the uplift resulting fromatmospheric correction, data registration, sensor noise, and fundamental assumption ofstationary optical properties of the scene with time are studied. Average uplift over theshallow bathymetry covered by the imagery is 0.85 ± 0.10 m, increasing from south tonorth from 0.56 ± 0.10 to 1.12 ± 0.10 m. The uplift amplitude is consistent with local fieldmeasurements. These data place constraints on the width of the megathrust rupture in theAndaman area, estimated to about 160 km, and on the amount of coseismic slip thereestimated to about 10 m.

Citation: Smet, S., R. Michel, and L. Bollinger (2008), Uplift of the 2004 Sumatra-Andaman earthquake measured from differential

hyperspectral imagery of coastal waters, J. Geophys. Res., 113, B09403, doi:10.1029/2007JB005317.

1. Introduction

[2] The location of the pivot line (or neutral axis) divid-ing uplifted from subsiding regions as well as some crudeestimates of the uplift gradient are key constraints toevaluate the rupture parameters of large subduction earth-quakes (Figure 1). Some of these parameters such as rupturewidth, slip amplitude and azimuth as well as dipping of thesubduction interface have been estimated along the 1600 kmruptured by the Sumatra-Andaman mega-earthquake on thebasis of seismological and far field geodetical data set [e.g.,Banerjee et al., 2005; Vigny et al., 2005; Subarya et al.,2006; Chlieh et al., 2007]. Some of these studies alsobenefit from local estimates of ground displacement de-duced from near-field geodetical data [Gahalaut et al.,2006] and/or coastal subsidence and uplift observations[Chia et al., 2005; Subarya et al., 2006; Meltzner et al.,2006]. In spite of these efforts, the details of the ruptureextent and coseismic slip pattern remain poorly constrained.In this paper we show that uplift or subsidence of theseafloor can be estimated from remote sensing, and thatthis technique could be an efficient way to complementmore traditional ways of measuring coseismic grounddeformation.[3] Remote sensing imagery is commonly used to mea-

sure coseismic deformation from various techniques includ-ing SAR interferometry and correlation of optical images

acquired before and after the event [e.g., Massonnet et al.,1993; Michel et al., 1999; Van Puymbroeck et al., 2000;Wright et al., 2004; Zebker et al., 1994]. These techniquesare rarely used in coastal environments where subductionearthquakes can produce significant vertical deformationdue to difficult applications, induced by poor SAR imagescorrelations or very low frequency deformation difficult toconstrain while correlating optical images. However, pas-sive optical spectral imagery of coastal waters yields infor-mation about the bathymetry, radiative properties ofseawater and of seafloor. Some techniques have beendeveloped to recover these parameters from hyperspectralimages [e.g., Pozdnyakov and Grassl, 2003; Mobley, 1994;Adler-Golden et al., 2005; Mobley and Sundman, 2000].Absolute estimate of the water depth by optical spectralimagery is challenging however because of the strongtemporal and geographic variability of the optical propertiesof water and because of trade-off between parameters[Pozdnyakov and Grassl, 2003;Mobley, 1994; Adler-Goldenet al., 2005]. In this study we exploit three hyperspectralimages of the northwest Andaman Islands coastal areaacquired by the HYPERION satellite before and after theMw 9.15, December 2004, Sumatra-Andaman earthquake toestimate the tectonic uplift (Figure 2 and Table 1). The threeimages studied have been chosen because of their similarradiance.[4] In the present study we first provide a tectonic setting

of the study area. We then recall the basics of radiativetransfer useful for that study, quantify the optical propertiesof the seawater and measure the bathymetric changes fromcomparing the images and estimating tide levels at the timeof the images acquisition. Error analysis and evidence oftemporal stationary of the averaged scenes are then de-

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113, B09403, doi:10.1029/2007JB005317, 2008

1Laboratoire de Detection et de Geophysique, CEA, Bruyeres-le-Chatel,France.

2Now at Actimar, Brest, France.

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scribed. We finally use the new data set obtained to constrainthe rupture azimuth, width, dip and amplitude of the26 December 2004 rupture along the northern section ofthe North Andaman and further discuss implications of thetechnique for remote sensing of coastal regions.

2. Background

2.1. Tectonics Context

[5] The convergence velocity of about 5 cm a�1 betweenthe Indo-Australian plate and the Sunda block is absorbedby underthrusting of the Indo-Australian plate along theSunda subduction zone [e.g., Bock et al., 2003]. Thrusting ispredominantly accommodated by creep on the subductioninterface at depth greater than 50 km [e.g., Simoes et al.,2004]. The shallower part of this interface is locked in thetime period separating major earthquakes (the ‘‘interseismicperiod’’). The hangingwall of the thrust system behaveselastically and deforms generating localized uplift along thecoast [Simoes et al., 2004]. The energy stored during theinterseismic phase at midcrustal depths is released duringlarge devastating earthquakes. These events are well docu-mented along Sumatra coastal area by the dating of coralsmicroatoll growth, monitoring continuously uplift and sub-sidence of the coastal region [e.g., Sieh et al., 1999;Natawidjaja et al., 2004]. Further north along the Andamansegment of Sunda trench, the convergence rates is loweralthough not very well constrained (probably around 12 mma�1 [Paul et al., 2001]). These islands are also prone to

recurrent major earthquakes, such as the 1941 Ms 7.7Andaman or the 1881Mw 7.8 Carnicobar earthquakes [Ortizand Bilham, 2003]. Although these historical events haverecently allowed the unlocking of significant segmentsalong the trench, their rupture traces have been rerupturedon 26 December 2004, participating to the giant Mw 9.15Sumatra earthquake. The large coseismic moment release[Ammon et al., 2005; Stein and Okal, 2005] is associatedwith a seismic source corresponding to a 1300 km rupturelength inferred from the spatial extent and location of theaftershocks, of the T wave radiations along the trench[Guilbert et al., 2005] as well as characterized from tsunamiinduced altimetric signal monitored by satellite imagery andtide gauges [Titov et al., 2005]. The far field GPS displace-ments measured further constrain the amount of slip ac-commodated along the ruptured area [Vigny et al., 2005].These far field GPS data poorly resolve the slip amplitudesgiven to range from 0 to 10 m under the North Andaman.Complementary near-field GPS solutions [Subarya et al.,2006; Gahalaut et al., 2006] improved resolution of therupture characteristics, as well as local measurements ofsubsidence (or uplift) estimated from coral heads meandistance below (above) their highest level of survival[Briggs et al., 2006; Meltzner et al., 2006] or minimumcoastal uplift derived from optical imagery [Meltzner et al.,2006]. All these data sets have been inverted in detailedrupture models [Subarya et al., 2006; Chlieh et al., 2007].[6] However, it appears that several local estimates of the

rupture width, azimuth and amplitude along strike are stilldebated since the spatial coverage of the observations isinhomogeneous, a pattern largely induced by the poorspatial sampling possibilities available since most of therupture area is under water. The N012E orientation of theHyperion hyperspectral images at 10–15N latitudes, similarto the Andaman coastline orientation (Figure 2), near thenorthern edge of the rupture, as well as the presence of asuspected uplift gradient along the coastline due to thepresence of a pivot line oblique to the island [Meltzner etal., 2006] offer an opportunity to evaluate the potentiality ofhyperspectral differential bathymetry as well as furtherquantify the rupture parameters in that area.

2.2. Hyperspectral Imagery

[7] HYPERION provides nadir-viewing radiance imagesin the range (400–2500 nm) with a 10 nm spectral resolution,a pixel size of about 30� 30m in the ground and a noise levelof about 0.09mWcm�2 sr�1 nm�1 per pixel (EO-1 ValidationReport, http://eo1.gsfc.nasa.gov/new/validationReport/) (seeFigure 3). Useful spectral bandwidth for water analysis isreduced to (400–800 nm) because of near total absorptionof light for wavelengths greater than about 800 nm[Pozdnyakov and Grassl, 2003]. We further reduced it tothe range (570–690 nm) in order to minimize major andpoorly constrained contribution of aerosol at short wave-lengths and residual miscompensated contribution of atmo-spheric water vapor [Zhao and Nakajima, 1997].[8] Atmospheric corrections of sensor radiances are first

performed using ad hoc methods in a multiple scatteringtheory of atmosphere radiative transfer on the completeHyperion spectral range (400–2500 nm). A standard atmo-spheric correction using Fast Line-of-Sight AtmosphericAnalysis of Spectral Hypercubes (FLAASH), a MODTRAN

Figure 1. Inferred surface deformation induced by arupture at a subduction trench: a pivot line on the verticalcomponent divides uplifted from subsiding areas. Locatingthe pivot line yields information about the width and dipangle of the ruptured zone. Locating the pivot line, often farout at sea, is not straightforward and may be inferred fromuplift gradients estimates. In this study, uplift and its lateralgradient is estimated from hyperspectral imagery along thecoastline.

B09403 SMET ET AL.: DIFFERENTIAL HYPERSPECTRAL BATHYMETRY

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Figure 2

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based model [Kneizys et al., 1996] including the ability tocompensate for atmospheric adjacency effect, is performed[Adler-Golden et al., 1998]. A standard maritime aerosol isselected with an aerosol-scale height set to 2.0 km, while theCO2 mixing ratio is set to 390 ppm. Second, we furthertested the transmittance obtained with that first approach andoptimized the atmospheric correction by applying a gasretrieval methodology using the joint reflectance and gasestimator (JRGE) methodology [Marion et al., 2004]. Thistechnique estimates the variations in gas concentrationsrelatively to the standard atmospheric model used in thefirst step (applied here on land and vegetation areas, zone Cin Figure 3). It allows us to evaluate the biases on theestimates of the surface reflectance R generated by the firstapproach, evaluating p.e. a pixel-by-pixel water vapor con-tent with a precision better than 10%.

3. Estimating Bathymetric Changes FromHyperspectral Images

3.1. Estimating Bathymetric Changes From RadianceImages

[9] Surface reflectance, R, which can be estimated fromhyperspectral images, can provide information on waterdepth provided that the water is clear and depth shallowenough for the reflectance from the sea bottom to be nonnegligible.[10] Single scattering theory of seawater column transfer

function yield,

R ¼ Rg þ Rw þ Rb � Rwð Þe�2Kz; ð1Þ

where Rg is the sea surface contribution combining Fresnelreflection and foam spectral component, Rw is the deepwater subsurface scattering contribution of photons that didnot reach the sea bottom, Rb is the reflectance of theseafloor, K is an effective attenuation coefficient thatincludes contribution of upwelling and downwellingattenuation, z is the depth of the water column. For imagenumber i (see Table 1 and Figure 4) the depth zi is the sumof the bathymetry b and of the tide ti. In addition, thebathymetry of the postearthquake image is reduced by thetectonic uplift U.

[11] Let us now see how tectonic uplift might be retrievedby comparing images. As a preliminary, we make theassumption that the radiative transfer properties of theshallow water does not vary significantly between images.This assumption seems reasonable for the three imagesconsidered here (Figure 3). Moreover, differences in waterdepth between the studied images are typically metric orsubmetric [Meltzner et al., 2006; Chia et al., 2005] and theconsidered waters are exceptionally clear (Figure 3b andtides in Table 1). The difference between the single and themultiple scattering approximation increases with the num-ber of scatters. Hence, the radiative contribution of thedifference in water depth between the considered imagescan be modeled by the single scattering approximationof (1).

3.2. Estimate of the Uplift U From Radiance Images

[12] After pixel-to-pixel atmospheric correction, in orderto reduce the effect of noise, we first work on pixelsaveraged over three areas describing the deep waters (areaA: 8.4 km2), the shallow waters (area B: 8.7 km2), and theinland (area C: 5.7 km2); areas A, B, and C, respectively,defined in Figure 3a.[13] (Rg + Rw)i is estimated for each image i as the

average value of the reflectance within the deep waters areaA. This is because of negligible contribution of the seabottom in this region. Rg + Rw can be considered equal forthe deep and shallow waters areas A and B because of thelack of large sediment deposit from rivers and because ofthe kilometric size of the averaged pixels. In the followingR � (Rg + Rw) is referred to as the equivalent reflectance.[14] K is then estimated from (1) and from the equivalent

reflectances, R1 and R2 of preearthquake images 1 and 2,respectively, as

K ¼ 1

2 z1 � z2ð Þ logR2 � Rg þ Rw

� �2

R1 � Rg þ Rw

� �1

2 t1 � t2ð Þ logR2 � Rg þ Rw

� �2

R1 � Rg þ Rw

� �1

" #: ð2Þ

Table 1. Hyperion Hyperspectral Images and Sea Level Used in the Present Studya

Image Reference Date Site Longitude Site Latitude Sea Level (m)

1 EO11340512004049110PY 18 February 2004 92.8842 13.3422 1.422 EO11340512004065110PY 5 March 2004 92.8842 13.3422 1.643 EO11340512005042110KZ 11 February 2005 92.8000 12.5000 1.90

aImages acquired to minimize effects of seasonal variations on radiative transfer. Theoretical sea level from tide gauge at Port Blair, South AndamanIslands (http://www.shom.fr/).

Figure 2. (a) Tectonic setting of the North Andaman Island. Also shown are minimum andmaximum estimates of uplift andsubsidence derived from satellite imagery fromMeltzner et al. [2006] as well as GPS displacement field from Gahalaut et al.[2006]. Envelope of the pivot line (in gray) is derived from uplift estimates of these studies. Location of the pivot line in thenorthernmost region of the island is far out at sea and therefore poorly constrained (gray area). (b) Location of thehyperspectral Hyperion scenes considered (red box). Black squares Z1 to Z5 identify the five subzones considered in Figure 3.GPS displacements at Aerial Bay (AB) and Diglipur (DGLP) are from Gahalaut et al. [2006] and Jade et al. [2005],respectively. Green outlined white filled triangles are uplift estimates from Kayanne et al. [2007]. Minimum verticaldisplacement (blue circles) fromMeltzner et al. [2006]. The numbers in red (followed by d for days) correspond to the numberof postseismic days integrated in the measure. Location of cross section CC0 (Figure 7).

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[15] The tectonic uplift U is then estimated from images 1and 3 acquired, respectively, before and after the earthquakeas

U ¼ t3 � t1 þ1

R3 � Rg þ Rw

� �3

R1 � Rg þ Rw

� �1

" #: ð3Þ

U is estimated for each wavelength and then averaged overthe spectral range (570–690 nm).

3.3. Errors Analysis

[16] The procedure proposed here suffers uncertainties dueto various sources of noise (Table 2): sensor noise, registra-tion of images, atmospheric correction, tidal value and theassumption of stationary optical properties of the scene(including the water) with time. The uncertainties of the tidalvalue taken into account, is given by the Service Hydro-graphique et Oceanographique de la Marine (SHOM, http://www.shom.fr/) to be less than 5% of the tide differential(assuming no atmospheric perturbation), corresponding hereto a 0.01 m uncertainty on uplift.[17] The HYPERION data noise level is about 0.09 mW

cm�2 sr�1 nm�1 per pixel (EO-1 Validation Report, http://eo1.gsfc.nasa.gov/new/validationReport/). It corresponds toa signal-to-noise ratio (SNR) equal to about 40 db per pixel ofthe raw image for shallow water radiance of area B forwavelengths in the range (570–690 nm). Neglecting system-atic errors, the SNR is enhanced following a square root nprocedure by averaging the n pixels of shallow water area B.The uncertainty Dz on z is estimated from differentiation of(1) as

Dz ¼ DR

1ffiffiffin

p ¼ 1

1ffiffiffin

p : ð4Þ

[18] Overestimate of Dz is computed from the lowattenuation coefficient K of pure water [Pozdnyakov andGrassl, 2003], and we found an error due to sensorradiometry equal to 0.04 m for each pixel of 30 � 30 m

Figure 3. Quicklook of Hyperion images 1, 2, 3 (lR =570 nm, lG = 620 nm, and lB = 680 nm; see Table 1 forfurther description). (a) Deep bathymetry area used toestimate average oceanic scattering and sea surface radiativecontribution (area A). Averaged uplift is estimated from areaB, a region of shallow bathymetry. Atmospheric correction ischecked on inland area C. (b) Zoom along the coast on aclear water–shallow bathymetry region.

Figure 4. Water column depth zi at date i depends onbathymetry b, tides ti, and tectonic uplift U (Table 1).

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of the raw images yielding to negligible errors for the 9636averaged pixels of shallow water area B.[19] Misregistration in the spatial and spectral wavelength

domains yields uncertainty on Dz from (2) and (3). Weestimated the misregistration to be equal to about 0.2 pixels(rms) representative from the subpixel correlation procedureused [Van Puymbroeck et al., 2000]. In order to estimate theassociated uncertainty Dz on z we shifted one image by 0.2pixel and we computed z between the raw and shiftedimages. We found Dz equal to 0.03 m per pixel and thusnegligible when averaged over area B.[20] Had hoc procedure estimates the atmospheric contri-

bution from the radiance images and yield atmosphericcorrection with typical precision better than 10% [Marionet al., 2004]. In order to reduce the effect of residualuncertainty on the atmospheric contribution, we limit ourbandwidth to (570–690 nm). We estimate uncertainty Dz

on z from two equivalent reflectances of image 1 computedfor atmospheric water content varying randomly by anamount of 10%. We computed an estimate of Dz equal to0.01 m.[21] In order to estimate the effect of changes in optical

properties of the water column and sea bottom we simulatethe reflectance R3

0 averaged over area B from (1), image 1,tide parameters t1 and t3 and estimate of the uplift U as

R03 ¼ R1 � Rg þ Rw

� �1

� �e�2K t3�t1�Uð Þ þ Rg þ Rw

� �3: ð5Þ

Dz ¼ DR

R3 � R03

�� 2KR3

; ð6Þ

Table 2. Sources of Errors and Space Averaged Amplitudes of the Uncertaintiesa

Source of Error Comments Uncertainty on Uplift (m)

Sensor noise SNR about 40 0.04 per pixelAtmospheric miscorrection maximum error on water vapor: 10% 0.01Data misregistration amplitude about 0.2 pixel 0.03 per pixelOcean tide less than 5% of the tide differential 0.01

aVariations in optical properties of the sea yield about 0.08 m of uncertainty on estimated uplift. The uncertainty on the oceantide determination is evaluated assuming no atmospheric effects on the tide level. Global uncertainty on uplift is ±0.10 m. SNRis signal-to-noise ratio.

Figure 5. Averaged reflectances. (a) Averaged inland reflectances (region C) vary, between images 1and 2, and images 1 and 3, by 6% and 13%, respectively; 13% include both atmosphericmiscompensation and vegetation changes, compatible with reported 10% uncertainty on atmosphericcorrection (transmittance dotted line) (see section 3.3). (b) Averaged deep water reflectance over area A(Figure 1) used to compensate shallow reflectances estimated over area B (c) yields equivalent reflectance(d). Equivalent reflectances show slopes resulting from water attenuation and differences in water depth(tide and uplift). Absorption coefficient derived from images 1 and 2; uplift estimated from images 1 and3. Modeled postearthquake equivalent reflectance (dashed line) fits data (gray line) within 5%.

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[22] R30 and R3 differ by an averaged 5% (see section 4)

over the considered bandwidth (Figure 5d). Associated Dzis estimated to be equal to 0.08 m for area B. As aconclusion, we can estimate the resulting uncertainty Dzon z as the geometrical mean of reported errors. We foundDz equal to 0.10 m for pixels averaged.

4. Results

[23] Figure 5a shows the averaged reflectance computedfor inland area C from the three images used in this study.Reflectance varies by an average 6% between images 1 and2 (preearthquake) and by 13% between images 1 and 3(acquired, respectively, before and after the earthquake).Those differences may originate partly from changes inoptical properties of the vegetation. Even if we considerthat incorrect compensation of the atmospheric transferfunction is responsible for all the observed difference, theresult is still compatible with the 10% of errors described insection 3.[24] Figures 5b and 5c show averaged reflectances for

areas A (deep water) and B (shallow water), respectively.The reflectances over deep water only result from the termsRg + Rw defined in equation (1). Those reflectances varyless between image 1 and 2 than between images 1 and 3.The variations do not depend significantly on wavelengthand may originate from changes of scattering properties ofthe oceanic surface and shallow water. Reflectances inshallow water show slopes characteristic of the near-exponential attenuation of the incident photon with waterdepth (1). Differences between the plots in Figure 5c

depend on both the variations of the averaged deep waterreflectances of Figure 5b and on the differences in the depthof shallow water column.[25] Figure 5d shows the equivalent reflectance in shal-

low area B. The equivalent reflectance of image 2 is belowthat of image 1 because the depth of water column is largerfor image 2 because of a greater tide (Table 1). Theattenuation coefficient K is derived from those plots from(2). The equivalent reflectance of image 3 is above that ofimage 1 indicating that the water column for image 3 issmaller than for image 1 because of a combination of tideand uplift effects (Figure 4).[26] Figure 6a shows the uplift U estimated for the

wavelength in the range (570–690 nm). The average is0.85 m and the standard deviation is 0.08 m (consistent withassumption error). Variation of U with wavelength arepartially correlated with the water attenuation suggestingthat the optical properties of the water changed betweenimages, contributing to the uncertainty on the estimate of U.Figure 6b illustrates the differences in attenuation coeffi-cient K, respectively estimated from images 1 and 2 (K12)using (2) and estimated from images 1 and 3 (K13) from (3),once U has been estimated and averaged over wavelength(Figure 6a). K12 and K13 differ by a maximum of 20%which is typical of low variations of optical properties ofseawater [Pozdnyakov and Grassl, 2003]. Those uncertain-ties on U and K yield errors on models of equivalentreflectances. Modeled reflectance R3

0 of image 3 fromimage 1, K and U differs from R3 by 5% (Figure 5d). Thusa posteriori estimates of variation of optical parameters withtime are consistent with the assumptions that the radiativetransfer properties of the considered shallow waters did notvary significantly between the three studied images.

5. Comparison With Existing Uplift Data Set

[27] We describe in a first subsection local estimates ofcumulated coseismic and postseismic shoreline uplift andGPS displacement field available in the first months afterthe earthquake. We then assume that insignificant afterslipsignal variations bias the data set, a necessary assumptionto compare altogether the sparse uplift measures avail-able. Our uplift measures are finally used to evaluate amodel of integrated and homogeneous slip under ourregion of interest. We describe in a second subsectionthe evidences for a significant afterslip, moderating theresults interpretation.

5.1. Integrated Coseismic and Postseismic UpliftEstimations

[28] The 0.85 ± 0.10 m vertical ground displacementdetermined in this study is consistent with the first coarseestimates at 1–2 ± 1 m along the northern Andaman westcoast [Bilham, 2005] (M. Searle, personal communication,2006) as well as extensive estimates of minimum upliftalong the coast covered by our HYPERION scenes at 0.36 ±0.14 m [Meltzner et al., 2006] (Table 3). Furthermore, GPSmeasurements of the coseismic displacement lead to esti-mate nearby uplift at Aerial Bay (AB) as well as furthersouth in Diglipur (DGLP), on the east coast. Uplifts at thesesites are 0.49 ± 0.05 m [Gahalaut et al., 2006] and 0.59 ±0.01 m [Jade et al., 2005] (Figures 2b and 7). More

Figure 6. Uplift and temporal water variation.(a) Averaged uplift over wavelengths (570–690 nm) is0.85 m, and 0.08 m (rms) variations mainly result fromtemporal changes of water attenuation (b). Attenuationcoefficient K (K12) derived from images 1 and 2; K13

derived from images 1 and 3 and estimated uplift.

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recently, local estimates of the uplift have been obtained inthe area from studies involving measurements of coralmicroatolls and mussel and oyster bed elevations [Kayanneet al., 2007; Rajendran et al., 2007] (Figure 2b and 7).These authors estimated uplifts around 0.6 ± 0.1 m about10–25 km east of the image, in a region of presumedpositive uplift gradient toward the west (see Figures 1 and 2).An uplift amplitude above the range (0.49 to 0.6 ± 0.1 m)was therefore expected on the west coast. Similarly, an upliftamplitude below the range (1.1 to 1.3 ± 0.1 m) was expectedfrom surveyed coral microatolls on North Reef island, about5–10 km west of our region of interest.[29] Meltzner et al. [2006] as well as Kayanne et al.

[2007], interpolating their uplift observations, propose thatthe neutral axis should be oriented 35�E and around 15 to30�E in our region of interest. Assuming a constant slipalongside, these results suggest that the coast oriented at 05to 10�E may have recorded a northward positive upliftgradient, the northernmost sites (zone z1) being significantlyfarther from the neutral axis in a region of positive east-westuplift gradient.

[30] The division of our area B into five parts (Figure 3),allows us to evaluate uplift variations along the coast (Table 3).The uplift decreases from north to south, by a significant0.56 ± 0.2 m (Table 3) that is beyond noise estimates of U,as suggested by the geometry. Assuming a constant geom-etry along strike (i.e., constant dip and azimuth of theruptured zone) this gradient suggests that the slip variesalong strike (in amplitude or azimuth) or that the azimuth ofthe downdip end of the ruptured zone is slightly differentfrom the azimuth of the coastline. However, the azimuth ofthe GPS derived coseismic displacement field appears to beconstant along strike in the Andaman region [Gahalaut etal., 2006]. For this reason, we do not suspect much changein the azimuth of the slip at depth. Futhermore, assumingthat the increase in uplift from south to north is due to anorthward increase in slip on the subduction interface is notlikely since North Andaman Island is suspected to be nearthe tip of the ruptured area. Therefore, the uplift gradientmight have been generated by the existence of a clockwiseangle in between the azimuth of the downdip end of theruptured zone and the coastline (Figure 2b).

Table 3. Uplift Estimates for the Coastal Region Covered by the Images (Area B) as Well as for Five Subzonesa

Zone z1 z2 z3 z4 z5 Average

Longitude 92.826� 92.818� 92.808� 92.808� 92.795� -Latitude 13.23� 13.20� 13.15� 13.12� 13.07� -Minimum uplift (m) from Meltzner et al. [2006] - 0.36 ± 0.14 0.36 ± 0.14 0.36 ± 0.14 0.37 ± 0.14 0.36 ± 0.14Uplift (m) from Chia et al. [2005] about 1 m in average in the range [0.5, 1.5 m]Uplift (m) derived from HYPERION 1.12 ± 0.10 0.89 ± 0.10 0.80 ± 0.10 0.77 ± 0.10 0.56 ± 0.10 0.85 ± 0.10Uplift (m) derived from Elastic Dislocation Model 1.01 0.98 0.91 0.85 0.78 0.90

aSubzones; i.e., zones 1 to 5. Minimum uplift estimates from Meltzner et al. [2006]; modeled uplift from this study (Figure 7).

Figure 7. Comparison between the displacements estimated from this study (squares), measured on thefield (triangles), deduced from other remote sensing imagery techniques (circles) and a simplistic best fitelastic dislocation model (lines) projected along CC0 (Figure 2b). GPS velocities are from Gahalaut et al.[2006] and Jade et al. [2005], minimum uplift magnitude is from Meltzner et al. [2006], coral head andoyster bed uplifts are from Kayanne et al. [2007], and differential bathymetry estimates are from thisstudy. Numbers (followed by d for days) correspond to the number of postseismic days integrated in themeasure. Thick, dashed, and dotted lines correspond to vertical, E-W, and S-N displacements,respectively, predicted by a best fit (see section 5.1) constant slip and constant geometry elasticdislocation model of the surface displacement (35�N dislocation in an isotropic elastic half-space with a9 m slip, rake 48�, on a 160 km large 21�E dipping plane). Elastic parameters in the model have beentied to classical value, l = m = 0.33 � 1011 yielding a Poisson coefficient n = 0.5 and a crustal ratio of P

over S wave seismic velocities Vp/Vs =ffiffiffi3

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[31] Assuming the pivot line in our region of interestpresents an azimuth around N035 and a distance of about145 km from the subduction trench as given by [Meltzner etal., 2006], we varied these parameters around these values(±20� and ±20 km every 5� and 5 km). We further determinea range of dip angle, rake (i.e., the angle between the faultazimuth and the slip on the fault plane) as well as slipamplitude that fit both the estimated uplift as well as thevertical and horizontal component of the displacementmeasured at GPS monuments. The best fitting model isdetermined from minimizing a reduced c2 criterion, mea-suring the discrepancy between modeled and observedvelocities. The best fitting model (c2 = 2.43, with residualstypically less than about 15 cm, average less than 2 cm,showing no systematic pattern in terms of their geographicdistribution) leads to an estimate of a 9 m rupture disloca-tion at N035, dipping at 21�E over a 160 km widthencompassing a rake at 48� (Figure 7; see caption fordetails). This result should be interpreted with care: thispurely elastic dislocation model is based on strong assump-tions including a constant geometry of the dislocation, onlyjustified at small scale, as well as a constant integratedcoseismic and postseismic slip magnitude among others.Although locally satisfactory, its displacement predictionscannot be extrapolated at larger scales suggesting that someinitial assumptions are too simplistic for larger-scale mod-els, arguing for lateral and/or temporal variations of thecoseismic and/or postseismic slip.

5.2. Afterslip Biases

[32] Our measures of the uplift incorporate 47 days ofpostseismic slip, given the late acquisition of the 3rdHyperion image. Unfortunately, no near-field continuousGPS station network was continuously monitoring thepostseismic deformation, very few days after the event.First-order estimates of the surface displacement fieldoccurring over the first 50 days after the earthquake hasbeen monitored by far field continuous GPS stations,mainly in Thailand, Malaysia and Sumatra [Vigny et al.,2005]. Their study put in evidence large variations of theafterslip magnitude over that period, locally up to 1.25 timesthe initial coseismic displacement as well as a more impor-tant relative after- to coseismic slip in the northern, includ-ing Nicobar-Andaman Islands, than in the southern part ofthe rupture. Chlieh et al. [2007], on the basis of theinversion of far field GPS solutions, suggest that more than35% of the coseismic displacement is accommodated bypostseismic slip under the Andaman Islands 30 days afterthe event. Although difficult to ascertain, given the sparsefar field data used to constrain it, this postseismic slip seemsmainly restricted to the downdip end of the ruptured zone[Chlieh et al., 2007]. This result seems consistent with apossible postseismic uplift measured on a mid-January 200512 day period in Port Blair (at PB GPS station), coincidentto a 1.2 m a�1 N259� velocity of the station [Gahalaut etal., 2006]. However, near-field subsidence observations inthe northern part of North Andaman Island [Kayanne et al.,2007; Rajendran et al., 2007] suggest that significant slip isalso accommodated at the updip end of the ruptured zone,probably due to the upward propagation of the slip at thetrench [Kayanne et al., 2007]. Eyewitness accounts indicatea 90 day postseismic subsidence of the shoreline as large as

40% of the coseismic uplift [Kayanne et al., 2007] at theforest Office Camp in Interview Island as well as in Mayab-under. Interpretations of second-order differential upliftestimates integrating various time period within 3 monthsfrom the earthquake is therefore hazardous. Furthermore,coseismic slip, 5 day cumulated slip, and 30 day cumulatedslip do not appear to be proportional [Vigny et al., 2005;Chlieh et al., 2007]. Any decent correction of the upliftobservations data set, integrating 0 to 90 days of afterslip,needed to further constrain biases or uncertainties in ouruplift determinations, appears therefore illusive.

6. Discussion and Conclusion

[33] This study attempting to retrieve uplift from hyper-spectral reflectance data using ocean color techniquesillustrates the potential of differential multispectral imageryfor the monitoring of earthquake deformations withincoastal areas, mainly of interest in subduction context.[34] The availability of Hyperion data in the rupture area

of the 26/12/2004 Great Sumatra earthquake offered anopportunity to test such a technique. By averaging about10,000 pixels (in area B corresponding to shallow bathym-etry) in 12 bands we estimate the tectonics uplift and itsvariations along the coast with a precision of ±0.10 m. Themean uplift amplitude determined in this study, around0.85 ± 0.10 m and its spatial gradient, are consistent withthe sparse uplift data set available in the area. This tech-nique allows to measure pluri-decimetrical uplift and sub-sidence over large areas, with potentially the ability torevisit regularly the surveyed scenes.[35] Unfortunately, the method described here appears to

be applicable in a few opportunistic cases only since itsapplication requires clear waters to limit the influence ofsignal attenuation, large areas of shallow (typically <30 m)bathymetry depending on the sensors’ resolution, very fewspatial and temporal variations of coastal turbidity oftendriven by wind, as well as high Sun angle, and homoge-neous tidal response, among other parameters. It furthermakes necessary the constitution of large imagery databaseswith similar acquisition parameters to properly evaluate theaccuracy of the uplift measurement, depending on localenvironmental parameters, often seasonal and sometimestransient, influencing the ocean color. Variations in waterquality and bottom albedo at most places [Pozdnyakov andGrassl, 2003] are generally much more important than inthe present Andaman context so that enhanced techniquesincluding in situ measurement of optical properties of water,sea bottom albedo as well as the join use of bathymetricLidar with the hyperspectral data should be instrumented[Guenther et al., 2000]. Finally, variations of coefficient Kwith time could also be constrained from a regularizationprocedure that would ensure that U does not depend on thespectral wavelength (Figure 6).[36] It should be noted that hyperspectral imagery is not

mandatory for such a multispectral differential bathymetryapproach. We primarily benefited from an opportunisticallyrich Hyperion hyperspectral database, with clear water andcloud free preearthquake and postearthquake scenes. Hyper-ion imagery gives us here the opportunity to discussatmospheric transfer function biases and make atmosphericcorrections nearby gas absorption bands, including water

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vapor, using the potential use of as much as 240 spectralbands. However, only 12 bands, in the 570–690 nm range,weakly influenced by water vapor content of the atmo-sphere, allows us to work on the shallow bottom reflectancevariations given the total absorption of light for wavelengthsgreater than 800 nm. A range further reduced to minimizemajor unconstrained aerosol and molecular scatteringbiases. Most multispectral sensors, although possessinglower spectral resolution, allow working at higher spatialresolution with higher signal-to-noise ratio and may havethe capacity to properly measure the bathymetry differential.This suggests that both multi and hyperspectral data offernew potentials to monitor tectonic deformation in coastalareas.

[37] Acknowledgments. We are most grateful to two anonymousreviewers and Pedro Elosegui, the Associate Editor, for their commentsand suggestions, which greatly helped improve this paper.

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��L. Bollinger and R. Michel, CEA-DAM-DASE-SLDG, BP 12, F-91680

Bruyeres-le-Chatel CEDEX, France. (laurent.bollinger@cea.fr; remi.michel@cea.fr)S. Smet, Actimar, 36 quai de la douane, F-29200 Brest, France.

(smet@actimar.fr)

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