Satellite radar interferometry time series analysis of surface deformation for Los Angeles, California

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1 GEOPHYSICAL RESEARCH LETTERS, VOL. 31, L23613, doi: /2004gl021294, 2004 Satellite radar interferometry time series analysis of surface deformation for Los Angeles, California Riccardo Lanari, 1,2 Paul Lundgren, 3 Mariarosaria Manzo, 1 and Francesco Casu 1 Received 18 August 2004; revised 12 October 2004; accepted 29 October 2004; published 14 December [1] The Los Angeles, California, metropolitan area is a tectonically active region with surface deformation that is a combination of fault related tectonics plus a variety of natural and anthropogenic signals. We apply the small baseline subset (SBAS) algorithm to produce an interferometric synthetic aperture radar (InSAR) time series analysis for the Los Angeles area using data acquired by the ERS satellites from late 1995 into The result is a space-time deformation product that can be exploited to view not only the smoothly varying long-term surface motion, but also its time varying patterns. Large seasonal oscillations of the Santa Ana aquifer observed in Southern California Integrated GPS Network (SCIGN) data are accurately matched in the InSAR time series, moreover, correlations of the InSAR time series with an annual sinusoid allows us to investigate the dynamics of the hydrologic system. INDEX TERMS: 1243 Geodesy and Gravity: Space geodetic surveys; 1294 Geodesy and Gravity: Instruments and techniques; 1803 Hydrology: Anthropogenic effects. Citation: Lanari, R., P. Lundgren, M. Manzo, and F. Casu (2004), Satellite radar interferometry time series analysis of surface deformation for Los Angeles, California, Geophys. Res. Lett., 31, L23613, doi: /2004gl [2] Satellite interferometric synthetic aperture radar (InSAR) time series analysis has been recently developed as a technique for mapping surface changes through space and time [Lundgren et al., 2001; Ferretti et al., 2001; Berardino et al., 2002; Usai, 2003], with recent applications investigating seasonal aquifer changes in San Jose, California [Schmidt and Bürgmann, 2003; Colesanti et al., 2003]. [3] The Los Angeles area (Figure 1) was the subject of several studies that sought to understand the spatial and temporal relationship between continuous GPS data from the Southern California Integrated GPS Network (SCIGN) and SAR interferograms [Bawden et al., 2001; Watson et al., 2002; D. F. Argus et al., Interseismic strain accumulation and anthropogenic motion in metropolitan Los Angeles, submitted to Journal of Geophysical Research, 2004, hereinafter referred to as Argus et al., submitted manuscript, 2004]. In each of these studies the InSAR observations consisted of individual and/or stacked interferograms, the former allowing for identification of seasonally varying spatial signals, while the latter allows for the longer term average to be obtained. In each of these studies the GPS and interferometry data are considered complementary: GPS sites give continuous measurements at a few sparse locations, whereas the radar interferograms give dense spatial maps over distinct time intervals. Time series analysis of a significant number of interferograms allows for a direct calculation of the temporal characteristics of the surface deformation over the entire area where InSAR maintains coherence, allowing for examination of both time-varying and long-term surface change. [4] We calculate the InSAR deformation time series, based on the small baseline subset (SBAS) algorithm [Berardino et al., 2002], for a large portion of the Los Angeles, California, metropolitan area using data from the ERS satellites acquired through the Winsar archive. We find that InSAR time series analysis agrees well with permanent GPS time series from the SCIGN network, in terms of both seasonal aquifer oscillations and long-term vertical deformation rates. Moreover, the availability of a joint temporal/spatial deformation data set allows for an additional level of signal analysis on the dynamics of the entire area. 1. Introduction 1 Istituto per il Rilevamento Elettromagnetico dell Ambiente, CNR, Naples, Italy. 2 Temporarily at Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA. 3 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA. Copyright 2004 by the American Geophysical Union /04/2004GL Time Series Analysis [5] To compute the InSAR time-series we inverted 102 interferograms, 1 computed from 42 ERS-1/ERS-2 SAR data between late 1995 and the beginning of 2002 (track 170, frame 2925), using the SRTM DEM and precise orbits provided by the University of Delft. We used the SBAS technique [Berardino et al., 2002], which relies on the use of small baseline (<300 m perpendicular orbit separation, <4 year temporal) interferograms; moreover, in order to mitigate the effect of possible atmospheric artifacts, a spacetime filtering operation, described by Berardino et al. [2002] can be performed. However, in the results presented here, no atmospheric filtering was carried out in order to more critically compare with the GPS time series data and to assess the information content of the raw InSAR time series. Accordingly, following the SBAS inversion, we have only removed the phase components due to residual topography, and orbital ramps. The latter have been estimated by performing an SVD inversion for the best-fitting ramp to the temporal high-pass/spatially low-pass time series signal component. 3. Results [6] The InSAR time series results can be viewed in a number of ways, each emphasizing potentially different 1 Auxiliary material is available at ftp://ftp.agu.org/apend/gl/ 2004GL L of5

2 Figure 1. Shaded relief map of the Los Angeles area that covers the same area shown in Figures 2 and 4. The large box outlines the interferogram area. Thin lines show mapped quaternary faults. SMF: Santa Monica fault. Thick dashed line roughly marks the boundary between the forebay and pressure area of the Santa Ana basin [after Herndon, 1992]. processes. Figure 2 shows the estimated mean displacement velocity map, in the radar line-of-sight (LOS), that highlights the major surface deformation features. They include aquifers such as the Santa Ana, and Pomona basins, oil related motion at a number of locations, and motion across the Newport-Inglewood, and San Jose faults where they serve as aquifer boundaries (Figure 1). [7] However, the real strength of InSAR time series lies in their potential for detecting surface change signals that are not steady with time. Figure 3 shows time series plots for a selection of points with different features in their time series. The first set of points (Figures 3a 3e) shows comparisons of the InSAR and the LOS projected GPS time series (relative to site ELSC, see Figure 2, which is also the reference area for each InSAR measurement), for corresponding SCIGN site locations. The locations of these selected sites extend from the northwestern end of the Santa Ana basin (BGIS) to near the center of the southernmost area of maximum basin subsidence (SACY). What we find is a very good agreement between the InSAR and GPS time series oscillations, both in amplitude and in data scatter. [8] Points g, h, j, k, and l, reflect different sources with large magnitude mean velocities. Point g lies in Pomona and appears to reflect water withdrawal, whereas point h lies north of the San Jose fault in the area surrounding the Puddingstone reservoir. This area shows greater seasonal effects, especially during 1998, 1999, and 2000, than does g, and exhibits a net uplift of the ground surface due to a net influx of water into the aquifer. Points j, k, and l, are in areas of oil extraction and show either subsidence or inflation effects depending on the net flux of fluids in the extraction process (California Department of Conservation, Division of Oil, Gas, and Geothermal Resources, ca.gov, 2003.). None of the oil related sites exhibits significant seasonal variability in their time series. [9] Up to this point the InSAR time series analysis has yielded information that is a refinement over previous individual or stacked InSAR analyses [Bawden et al., 2001; Watson et al., 2002; Argus et al., submitted manuscript, 2004], yielding results that are similar in resolution to time series analysis of aquifer oscillations in the San Jose, California, area [Schmidt and Bürgmann, 2003]. One advantage of InSAR over spatially sparse measurements such as GPS or well data lies in the potential for signal patterns to yield additional insight into physical processes. In Figure 4 we expand upon the time series seasonal oscillations seen in the Santa Ana basin area (Figures 3a 3e). We calculate the correlation coefficient of each pixel s detrended time series, with a sinusoid within ±60 days of March 10 in ten day time bins. We find that the maximum correlation (Figure 4a) is within the Santa Ana basin and a few other smaller locations north toward the San Gabriel Mountains. While the high correlation pattern of the Santa Ana basin is not surprising, it is interesting that some features such as the area of the San Gabriel valley north of the Raymond fault also correlate significantly with an annual oscillation. [10] Specifically, we can display the best fitting sinusoid amplitudes (Figure 4b), and maximum correlation time centered on March 10 (Figure 4c), in the areas with a correlation greater than 0.6. We find an interesting set of patterns when the best fitting sinusoid amplitudes and time shifts are displayed in map view. The oscillation amplitude map (Figure 4b) is greatest toward the middle of the southern end of the Santa Ana basin near GPS site SACY (point e in Figure 2, and Figure 3e). Farther north, toward the center of the basin there is a secondary maximum in the oscillation amplitude. Unlike its southerly counterpart, this Figure 2. InSAR mean LOS deformation velocity map, overlying the SAR amplitude image. Small black squares mark SCIGN GPS site locations. Those marked a through e correspond to their respective time series plots in Figure 3. ELSC marks the time series reference point. White squares mark the locations of plots g, h, j, k, l in Figures 3. 2of5

3 Figure 3. InSAR time series (black triangles) at selected points. (a e) Comparison of InSAR to SCIGN GPS (red *) time series for sites along the Santa Ana Basin. (f) InSAR time series and their respective best fitting sinusoids for points f (black line and triangles) and f 0 (red line and triangles) shown in Figure 4. (g, h, j, k, l) InSAR time series plots for other aquifer or oil related points shown in Figure 2. Figure 3i shows ground water data (California Department of Water Resources, Water Data Library, ) from two wells (05S10W14L001S for i; 06S10W09E003S for i 0 ) and their best fitting sinusoids for points i (black) and i 0 (red) in Figure 4. area of high oscillation amplitude extends westward to within close proximity of the Newport-Inglewood fault bounding the southwestern side of the basin. The extension of this high oscillation amplitude to the western edge of the basin explains the relatively high oscillation amplitude of GPS data for site LBC1 (point d in Figure 2, and Figure 3d). [11] More intriguing is the spatial signature of the best fitting time shift of the sinusoid (Figure 4c). This shows that the central portions, and areas of maximum oscillation amplitude generally peak earlier with respect to the edges of the area with significant annual oscillations. This is especially apparent for the southwest side of the aquifer that is bounded by the Newport-Inglewood fault. As a specific example of this shift we can examine the time series of two points with similar correlations at the center and edge of the basin to see how they differ (points f and f 0 in Figure 4, and displayed as black and red time series, respectively, in Figure 3f). We see that the higher amplitude (in black) peaks earlier relative to the lower amplitude point (in red) on the edge of the basin. 4. Discussion and Conclusions [12] This paper presents an application of InSAR time series analysis to the Los Angeles area. By exploiting the spatially varying behavior of the time series, we are able to extract additional information tied to the dynamics of the water within the aquifers. [13] The InSAR time series reveals ground water related surface deformation that occurs on two different time scales reflecting different processes in the Santa Ana basin. The longer timescale subsidence (Figure 2) is consistent with aquitard drainage models in which inelastic compaction occurs that is incompletely compensated by elastic expansion during winter recharge [Galloway et al., 1998; Amelung et al., 1999]. Thus, in the aquitard model for basin compaction, slow, permanent subsidence occurs that is dependent on the aggregate thickness of the aquitards, and the time constants for their compactions, typically tens to hundreds of years. Typical extensometer measurements (where they exist) show faster compaction during removal of water followed by neutral to very slow compaction during the winter [Galloway et al., 1998]. The shape of the subsidence region reflects both the structure of the basin and its distribution of wells. Based on the Santa Ana basin hydrogeology [Herndon, 1992; Orange County Water District, OCWD s groundwater model, com/_html/news.htm, 2002] we would expect groundwater to flow from the forebay area (Figure 1) of recharge through the pressure area to the coast. The forebay/pressure area boundary is roughly coincidental with the northeastern edge of the high sinusoid correlation areas shown in Figures 4b and 4c. [14] The second time scale relates to the annual recharge of the basin (Figures 3 and 4) and the fluid dynamics of flow, as reflected in the spatial distribution of the phase 3of5

4 delay of maximum uplift related to elastic inflation of the aquifer skeleton (Figure 4c), where the coastal area has the latest sinusoid time shift. This behavior is also found in ground water well data (Figure 3i) where we see a similar effect for a well located at the center of the basin compared to one located near the coast. However, for the Santa Ana aquifer, the large amplitude and negative delay of its center relative to its edges suggests a component of lateral diffusion toward both edges. One possible explanation might be that the area of greatest water extraction during the summer (and aquifer thickness) recharges fastest in the winter. However, the presence of injection barriers, such as the Orange County Water District s line of wells to prevent sea water infiltration through the Talbert Gap [Herndon, 1992] and the three dimensional structure of the basin requires a numerical analysis of the basin fluid dynamics to understand the magnitude and extent of this delay. [15] This InSAR analysis provides confirmation of signals previously recognized with GPS and, to a lesser degree, with individual SAR interferograms. InSAR time series, thus, provides dense space-time surface deformation maps with an accuracy that is similar to that of continuous GPS vertical time series. In addition, the dense spatial coverage of InSAR allows for pattern identification of subtle features, such as space-time dispersion of seasonal oscillation deformation in aquifers like the Santa Ana basin. Of key importance in future applications will be the ability to discriminate between competing surface deformation signals with differing spatial and temporal characteristics. [16] Acknowledgments. ERS data were provided courtesy of ESA through the WInSAR archive. We thank H. Zebker, G. Peltzer and M. Rosen for their help. Constructive reviews were provided by F. Amelung and an anonymous reviewer. This work has been partially supported by ASI and by the CNR. Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. This work was also supported by the EU within the project of the Regional Center of Competence AMRA. Figure 4. Maps related to the best fitting annual sinusoid maximum correlation coefficient (falling within ±60 days of March 10). White squares label f, f 0, and i, i 0, correspond to time series plots f and i in Figure 3, respectively. (a) Maximum correlation map. (b) Peak-to-peak amplitude of the sinusoid. (c) Time shift of sinusoid relative to the center date of March 10. Dashed line is location of forebay/ pressure area boundary shown in Figure 1. References Amelung, F., D. L. Galloway, J. W. Bell, H. A. Zebker, and R. J. Laczniak (1999), Sensing the ups and downs of Las Vegas: InSAR reveals structural control of land subsidence and aquifer-system deformation, Geology, 27, Bawden, G. W., W. Thatcher, R. S. Stein, K. W. Hudnut, and G. Peltzer (2001), Tectonic contraction across Los Angeles after removal of groundwater pumping effects, Nature, 412, Berardino, P., G. Fornaro, R. Lanari, and E. Sansosti (2002), A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms, IEEE Trans. Geosci. Remote Sens., 40, Colesanti, C., A. Ferretti, F. Novali, C. Prati, and F. Rocca (2003), SAR monitoring of progressive and seasonal ground deformation using the permanent scatterers technique, IEEE Trans. Geosci. Remote Sens., 41, Ferretti, A., C. Prati, and F. Rocca (2001), Permanent scatterers in SAR interferometry, IEEE Trans. Geosci. Remote Sens., 39, Galloway, D. L., et al. (1998), Detection of aquifer system compaction and land subsidence using interferometric synthetic aperture radar, Antelope Valley, Mojave Desert, California, Water Resour. Res., 34, Herndon, R. L. (1992), Hydrogeology of the Orange County groundwater basin: An overview, in The Regressive Pleistocene Shoreline: Coastal Southern California, edited by E. Heath and L. Lewis, pp , South Coast Geol. Soc., Santa Ana, Calif. Lundgren, P., S. Usai, E. Sansosti, R. Lanari, M. Tesauro, G. Fornaro, and P. Berardino (2001), Modeling surface deformation observed 4of5

5 with synthetic aperture radar interferometry at Campi Flegrei caldera, J. Geophys. Res., 106, 19,355 19,366. Schmidt, D. A., and R. Bürgmann (2003), Time-dependent land uplift and subsidence in the Santa Clara valley, California, from a large interferometric synthetic aperture radar data set, J. Geophys. Res., 108(B9), 2416, doi: /2002jb Usai, S. (2003), A least squares database approach for SAR interferometric data, IEEE Trans. Geosci. Remote Sens., 41, Watson, K. M., Y. Bock, and D. T. Sandwell (2002), Satellite interferometric observations of displacements associated with seasonal groundwater in the Los Angeles basin, J. Geophys. Res., 107(B4), 2074, doi: /2001jb F. Casu, R. Lanari, and M. Manzo, Istituto per il Rilevamento Elettromagnetico dell Ambiente, CNR, via Diocleziano 328, I-80124, Napoli, Italy. (lanari.r@irea.cnr.it) P. Lundgren, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA. 5of5

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