How To Determine The Ability Of Anos/Palsar To Detect Deformation In Iceland

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1 RECENT DEEP-SEATED MAGMATIC ACTIVITY AND THE 2008 M6.3 EARTHQUAKE: APPLICABILITY OF ALOS/PALSAR IN ICELAND Andrew Hooper 1, Benedikt Ofeigsson 2, Freysteinn Sigmundsson 2, and Halldór Geirsson 3 1 Department of Earth Observation and Space Systems, Delft University of Technology, Delft, Netherlands 2 Nordic Volcanological Centre, University of Iceland, Reykjavik, Iceland 3 Icelandic Meteorological Office, Reykjavik, Iceland ABSTRACT Between February 2007 and April 2008, intensive swarms of small deep-seated earthquakes were recorded within the Kverfjöll Volcanic System in north-eastern Iceland. The focal depths lie in the lower, ductile region of the crust, at depths of km. We formed interferograms from SAR data acquired by ALOS/PALSAR and Envisat/ASAR and modelled the detected displacements, together with GPS data, in terms of an opening tilted planar dislocation. We interpret this as intrusion of magma into the seismically active region. We also formed interferograms from PALSAR data for the region of the South Iceland Seismic Zone that was struck by a Mw=6.3 earthquake on 29 May Through times series analysis of the interferograms we were able to reduce the impact of digital elevation model errors and extract the coseismic signal. In general the use of PALSAR data extends the potential of InSAR to detect deformation in Iceland into the winter months. However, high levels of atmospheric noise sometimes limits the usefulness of these data. Key words: PALSAR; Iceland; Upptyppingar; SISZ. 1. INTRODUCTION The divergent plate boundary between the North- American and Eurasian plates in Iceland is expressed as a series of seismic and volcanic zones. Spreading across rift zones and shearing across seismic zones, of about 2 cm/yr, continuously builds up stress that is released by tectonic and magmatic processes. Eruptions occur at intervals of a few years (most recent in 2004) but emplacement of magma at depth within the rift zones without eruptions is also common. Crustal deformation associated with plate spreading, earthquakes, magmatic processes and glacial rebound has been extensively studied with a variety of techniques, including GPS and InSAR. Data collected by ALOS/PALSAR and Envisat/ASAR in 2008 document well the deformation associated with two major events along the plate boundary, a deep seated magma intrusion at the divergent plate boundary in north Figure 1. Iceland in shaded relief. The Kverfjöll volcanic system activity lies within the right inset box and the 29 May 2008 earthquake occurred within the left inset box. White shading indicates the permanent ice caps, yellow shading indicates the volcanic systems, the red dashed lines indicate zones of divergence and the black solid lines indicate transform zones. Iceland, and deformation due earthquakes in the South Iceland Seismic Zone. An overview of study areas within Iceland is shown in Figure 1. In February 2007, intensive swarms of small deep-seated earthquakes began to be recorded within the Kverfjöll volcanic system in north-eastern Iceland. The activity originated in the region of Upptyppingar, and migrated with time north-eastwards towards Álftadalsdyngja [1] (Figures 2 to 4). The swarms continued until early April The focal depths lie in the lower, ductile region of the crust, at depths of km. Previous episodes of deep-seated earthquake activity in Iceland have usually been linked with magma unrest, for example at Eyjafjallajökull, Vestmannaeyjar, and Askja. Continuous and campaign GPS measurements suggest this is also the case for this latest activity, with horizontal velocities of up to 30 mm/yr towards SSE observed from early summer 2007 [2] until early The scarcity of the GPS network, however, makes it difficult to distinguish between potential geometries for the deformation source.

2 2 ) ) ) Figure 2. Cumulative displacement and seismicity during the summer of Left, 12 June, middle, 17 July and right, 21 August. The date of the master acquisition is 6 June Deformation is in the line-of-sight toward the satellite with respect to June 2005, and is derived from time series analysis of multi-looked interferograms data acquired by ASAR on descending track 9. Some atmospheric signal remains, but there is evidence of graben type structures opening. The earthquake epicentre locations are from the SIL database of the Icelandic Meteorological Office. On 29 May 2008, an Mw=6.3 earthquake struck the western part of the South Iceland Seismic Zone (SISZ) near the Hengill triple junction, where the SISZ, the Western Volcanic Zone and the Reykjanes Peninsula intersect. The rupture commenced at 15:46 UTC beneath the west side of Mt. Ingólsfjall (Figure 5). A second rupture was triggered on an adjacent parallel N-S oriented fault within 1 second of the first event. The aftershock locations from the Iceland Meteorological Office SIL database indicate that there are at least two N-S faults zones, spaced about 5 km apart (Figure 5). In addition, aftershocks occurred along an E-W zone extending about 20 km to the west. The May 2008 sequence comes relatively soon after the previous earthquake sequence in the SISZ in June 2000, when two Mw=6.5 earthquakes ruptured two N-S striking faults, located about 17 km apart. Prior to the June 2000 earthquakes there was intense seismic activity in the Hengill area, and there was also uplift of approximately 20 mm/yr during We have formed interferograms from SAR data acquired by ALOS/PALSAR and Envisat/ASAR to measure the deformation associated with both the Upptyppingar deepseated activity and the May 29 earthquake. In the first case, the use of L-band PALSAR data allows us to extend the use of InSAR into the month of December, when coherence using C-band data is too low to extract the signal due to snow cover. However, atmospheric artifacts limit the usefulness of these data. We model the deformation in terms of a opening tilted planar dislocation which we interpret as intrusion of magma into the seismically active region. In the second case we are able to extract the signal from a number of winter PALSAR images using time series analysis. This analysis leads to the estimation of errors in the digital elevation model (DEM), enabling us to extract the earthquake signal from PALSAR data despite the fact that all possible coseismic interferograms have long perpendicular baselines. 2. UPPTYPPINGAR DEFORMATION We processed ASAR data from track 9 using time series analysis of small baseline interferograms. Pixels were multi-looked 4 times in range and 20 times in azimuth, and those with a coherence greater than 0.25 in at least 30% of all interferograms were selected. Phaseunwrapping was performed using a novel approach allowing the application of algorithms developed for regularly gridded data to sparse data. First, the sparse phase measurements were resampled to a grid using a nearest-neighbour interpolation routine. We then applied the optimisation routines of SNAPHU [3]. SNAPHU uses a generalised cost function approach to search for the most likely positions of phase jumps (phase changes between adjacent pixels of more than π in magnitude) within an interferogram. Usually the cost functions are derived within SNAPHU itself, but we set them externally such that (1) phase jumps cannot be placed between grid cells interpolated from the same sparse value, (2) the probability of phase jumps between other cells depends on the evolution of the phase difference between the cells with time. This approach is implemented, since version 2.2, in the StaMPS processing package ( ahooper/stamps) and was applied in Hooper (2008) [4]. Three time steps from the time series analysis are shown in Figure 2. Up until 12 June 2007, no deformation was visible at the surface, but by 17 July, apparent small scale grabens had begun to form. These structures were further pronounced by 21 August. Seismicity continued throughout this period and was confined to beneath the Upptyp-

3 3 09 Sep Mar Dec Jul 2008 Figure 3. Interferograms formed from PALSAR data acquired on ascending track 9, with respect to a single master image acquired on 27 July One colour cycle represents 240 mm of displacement in the line-of-sight. The black circles represent epicentres of earthquakes between February 2007 and the data of the slave image. 16 Jul 2008 Ascending Orbit 28 Jun 2008 Descending Orbit Figure 4. Interferograms formed from ASAR data. Left, ascending track 230 spanning 27 June 2007 to 16 July 2008 and right, descending track 467 spanning 14 July 2007 to 28 June One colour cycle represents 28 mm of displacement in the line-of-sight. The black circles represent epicentres of earthquakes between February 2007 and the date of the slave image.

4 4 Figure 5. Deformation associated with the 29 May 2008 earthquake sequence. Left, Phase of coherent pixels for ascending PALSAR interferogram spanning 27 July 2007 to 14 September 2008, corrected for DEM error, unwrapped and converted into line-of-sight displacement. Right, wrapped phase of descending ASAR interferogram spanning 7 July 2007 to 14 September 2008, with one colour cycle representing 28 mm of deformation in the line-of-sight. In both images, earthquakes between 29 May and 31 July 2008 are plotted as black circles. pingar area. No further images were acquired on this track until May 2008, but between September 2007 and July 2008 there were four images acquired by PALSAR over the Upptyppingar area, also, coincidentally, on track 9. Interferograms were formed between these four acquisitions and 27 July 2007 (Figure 3). It is particularly notable that there is reasonable coherence over most of the image for the December 2007 interferogram. There was considerable snow cover at this time, and we would expect C-band interferograms covering this interval to exhibit complete decorrelation. By March 2008, however, the snow cover is apparently too deep to maintain correlation even at L-band. Of the three coherent images, two exhibit very strong spatially-correlated noise due presumably to changes in the path delay through the atmosphere. As signals this large are not usually seen in C-band interferograms, it is likely that the majority is due to the passage through the ionosphere. Other than the grabenlike structures, no deformation is detectable above the noise in any of PALSAR images. There was however a marked change in the velocities of nearby continuous GPS stations commencing sometime in June 2007 [2] and in September 2007, the seismicity began to migrate northeastwards, towards the region beneath Álftadalsdyngja. The seismicity rate dropped abruptly in April At about the same time, GPS velocities returned to background rates [5]. The signal detected by the change in GPS velocities between July 2007 and April 2008 is also visible in ASAR interferograms spanning the interval between summer 2007 to summer 2008 (Figure 4). 3. UPPTYPPINGAR, MODELLING We simultaneously modelled the phase of the ASAR ascending and descending interferograms, together with the horizontal GPS velocities. To reduce the number of data to a manageable number, we resampled the interferometric phase to a 1 km by 1 km grid. Aside from the deformation we wished to model, there is also displacement present in the interferograms due to the elastic and viscoelastic response to changes in the ice-mass balance of the Vatnajökull ice cap to the south. This motion is visible in the time series analysis of ASAR track 9 data and to a first order approximates a bilinear function over the region we are analysing. For both interferograms, therefore, we estimated a bilinear phase ramp from the pixels outside the deforming region, and subtracted this from the phase of all pixels. To first order this also removes the phase present due to errors in orbit estimation. We converted the phase to mean LOS velocity on the assumption that the deformation lasted until the end of April. To quantify the variance-covariance of the interferogram pixels, we calculated a 1D experimental variogram from the detrended pixels outside the deforming region in each case, and fit a covariance model. We then constructed a

5 Probability distribution of tilted sheet locations Maximum Likelihood tilted sheet location Earthquake location Data Model Residuals 65.0 Depth (km) mm/yr Figure 6. Probability distribution of intruded sheet locations. Left shows a map view and right shows a vertical profile looking from SWW (245 ). The offset between the intrusion depths and depths of seismicity is likely an artifact of the halfspace rheology assumed in the modelling. Preliminary modelling including vertical heterogeneity in elastic parameters indicates that the intrusion locations deepen. variance-covariance matrix for both interferograms from these covariance models. GPS data were collected at a number of continuous and campaign stations in the deforming region. We estimated horizontal velocities from the position measurements relative to stable Eurasia, calculated between June 2007 and April 2008, by weighted least-squares estimation. The secular velocities prior to June 2007 were also calculated by least-squares estimation and subtracted. The error estimates include both the propagated formal errors and unmodelled errors from the seasonal variation in the velocities due to seasonal ice-mass variation. The unmodelled errors are much larger in the case of campaign benchmarks, were the seasonal signal is poorly constrained. We did not use the vertical GPS measurements to constrain our inversion as they are dominated by the seasonal signal, which is generally too poorly sampled to allow its adequate estimation. We modelled the source as a simple rectangular opening dislocation in an elastic halfspace, using the solutions of Okada (1985) [6]. As the interferograms are in a floating frame of reference, we also modelled a constant phase offset for each interferogram. Starting with a very general a priori model probability distribution, we used the Monte Carlo Metropolis algorithm to build up an a posteriori probability distribution that was constrained by the data [7]. We found that at 95% confidence, the opening dislocation source is a tilted sheet dipping with volume km 3 at a depth to mid-point of km. The distribution of models and bestfitting model are shown in Figures 6 and 7, along with the residuals between the model predictions and the data. In plan view, the model distribution matches the seismicity to a high degree. In depth, the model distribution is 3 km shallower than the seismicity, but this is likely simply an artifact of treating the medium as an elastic halfspace. Typically, when layering of the elastic parameters is included in a dislocation model, the depth increases by a few km [8] and preliminary modelling suggests this is mm/yr Figure 7. Maximum likelihood tilted sheet model. Left shows InSAR data, ascending track 230 above, and descending track 467 below. The surface projection of the tilted sheet is shown in white. A linear trend has been estimated from the data surrounding the deformation zone and removed. Middle shows predicted InSAR values from maximum likelihood model, and GPS horizontal motions (data in black, predictions in blue). Ellipses represent 2- sigma confidence bounds. The black points represent epicentres of earthquakes with Mw=0 and greater, between January 2007 and July Right shows residuals between the data and model predictions for the InSAR and GPS data. also the case here. We can therefore say with reasonable confidence, that the deformation is caused by opening along a tilted structure, in a region approximately coincident with the seismicity, presumably due to the intrusion of magma at this depth. The orientation of the intrusion is somewhat puzzling. The regional stress field is controlled by the divergence of the plate boundary, and shallow intrusions in the past have reflected this by being in the form of vertical dikes striking along the plate boundary [e.g., 9]. The depth of the Kverfjöll intrusion puts it within the ductile region where we do not expect local perturbations in the stress field to persist for any significant time. This would suggest that stress is not playing a role in the orientation of the intrusion, but rather that it is controlled by contrasting mechanical properties of the rocks at this depth. Possibly, there exists a former cone sheet intrusion of the same orientation, as has been observed elsewhere in Iceland [10], which has guided this intrusion. 4. SISZ PALSAR TIME SERIES In order to image the May 29 earthquake, we processed data from PALSAR ascending track 20. At the time of processing there was only one post-earthquake acquisition, acquired in October Due to a manoeuvre by ALOS prior to this acquisition, the minimum perpendicular baseline of any interferogram spanning the earthquake is 2520 m (Figure 8). The phase due to errors in the DEM

6 Jan Dec Jan Perpendicular Baseline (m) Earthquake 14 Mar Apr Sep Jan07 Apr07 Jul07 Oct07 Jan08 Apr08 Jul08 Oct08 Figure 8. Baseline plot for PALSAR track 20. Circles represent acquisitions and lines connecting the circles represent the interferograms formed. The perpendicular baseline is calculated with respect to the acquisition of 28 July caused by such a large baseline prohibits reliable phaseunwrapping. In order to reduce the contribution due to the DEM errors, we analysed the entire time series of images. We formed 6 interferograms with respect to a master image acquired on 28 July 2007 (Figure 9). The interferograms were corrected for topographic phase and multilooked 10 times in range and 30 times in azimuth. Coherence was calculated over the same box size, and multilooked pixels with coherence greater than 0.1 in at least four interferograms were selected. The phase of the selected pixels was unwrapped using the 3D unwrapping algorithm described in Section 2. Phase due to errors in the DEM was then estimated from the unwrapped phases of the five interferograms that did not span the earthquake, under the assumption that deformation was negligible, and atmosphere and orbit errors were uncorrelated with baseline. The model is thus φ i = 4π h sinθ B,i + φ m + ε i, λr where φ i is the phase of the ith interferogram h is the DEM error, θ is the incidence angle, λ is the centre wavelength, r is the range B,i is the perpendicular baseline, φ m is the phase due to atmosphere, orbit error and other nuisance terms present in the master image and ε i is the uncorrelated error. The inversion was performed for each pixel using linear least-squares. Note that due to the strong correlation of perpendicular baseline with time, if there is any deformation signal present, it will also be interpreted as due to DEM error. The estimated phase due to DEM error and nuisance terms in the master image was then subtracted from the interferogram spanning the earthquake, and the phase of this interferogram unwrapped (Figure 5). The resulting unwrapped phase gives the full pattern of displacement, even in the area of strongest deformation, where interferograms formed from ASAR data exhibit decorrelation. Figure 9. Interferograms for the region indicated approximately by the left box of Figure 1, with respect to a master image acquired on 27 July The images are in radar geometry and formed from PALSAR data acquired on ascending track 20. The pixels are multi-looked, with 10 looks taken in range, and 30 looks in azimuth. Each colour cycle represents the equivalent of 12 cm line-ofsight displacement. 5. CONCLUSIONS The deep-seated seismic activity in Kverfjöll volcanic system apparently relates to the intrusion of magma at about the same depth, in the form of a tilted sheet. The orientation of the intrusion differs to that we would expect from the regional extensional stress field, i.e. a vertical dike following the strike of the volcanic system, which has been observed during shallower intrusions into other volcanic systems. The use of PALSAR data extends the potential of In- SAR to detect deformation in Iceland into the winter months. However, the presence of high levels of atmospheric noise sometimes limits the usefulness of the PAL- SAR data. Using time series analysis we are able to to estimate errors in the DEM from PALSAR data acquired in, although the strong correlation of perpendicular baseline with time means that deformation might also be interpreted as DEM error. After correction for DEM error the PALSAR data provide the pattern of deformation associated with the May 29 earthquake more completely than interferograms formed from ASAR data. ACKNOWLEDGMENTS PALSAR and ASAR data were provided by ESA, courtesy of JAXA in the PALSAR case. Focused SAR images were produced using the ROI_PAC software package developed by the Jet Propulsion Laboratory. Interferometric processing was performed using the Doris software package developed by the Department of Earth Observation and Space Systems, Delft University of Technology.

7 7 [10] A. Gudmundsson. Emplacement and arrest of sheets and dykes in central volcanoes. J. Geophys. Res., 116(3-4): , π π Figure 10. Coherent pixels from the interferograms shown in Figure 9. Pixels with coherence greater than 0.1 in at least four interferograms are selected. The images are in geocoded geometry and each colour cycle represents the equivalent of 12 cm line-of-sight displacement. REFERENCES [1] S. S. Jakobsdóttir, M. J. Roberts, G. B. Guðmundsson, H. Geirsson, and R. Slunga. Earthquake swarms at Upptyppingar, North-East Iceland: a sign of magma intrusion? Studia Geophysica et Geodaetica, 52, in press. [2] H. Geirsson, F. Sigmundsson, B. Ófeigsson, E. Sturkell, T. Árnadóttir, A. Hooper, P. Einarsson, and G. B. Guðmundsson. Crustal deformation associated with the deep-seated seismic swarm at Upptyppingar, north of Vatnajökull, Iceland. In Proceedings IAVCEI 2008 General Assembly, [3] C. W. Chen. Statistical-cost network-flow approaches to two-dimensional phase unwrapping for radar interferometry. PhD thesis, Stanford University, [4] A. Hooper. A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches. Geophys. Res. Lett., 35:L16302, [5] H. Geirsson. Iceland Meterological Office website. [6] Y. Okada. Surface deformation due to shear and tensile faults in a half-space. Bulletin of the Seismological Society of America, 75: , [7] K. Mosegaard and A. Tarantola. Monte Carlo sampling of solutions to inverse problems. J. Geophys. Res., 100(B7): , [8] A. Hooper, P. Segall, K. Johnson, and J. Rubinstein. Reconciling seismic and geodetic models of the 1989 Kilauea south flank earthquake. Geophys. Res. Lett., 29(22): , [9] E. Tryggvason. Widening of the Krafla fissure swarm during the volcano-tectonic episode. Bulletin of Volcanology, 47(1):47 69, 1984.