Ground Fissure Monitoring in China Based on PALSAR Data 430 Zhiwei Li (1) Jianjun Zhu (2), Zhengrong Zou (2), Wujiao Dai (2) (1) School of Geosciences & Info-Physics, Central South University, Changsha 410083, Hunan, China, E-mail: zwli@mail.csu.edu.cn (2) School of Geosciences & Info-Physics, Central South University, Changsha 410083, Hunan, China, E-mail: zjj/zrzou/wjdai@mail.csu.edu.cn Abstract Synthetic Aperture Radar Interferometry (InSAR) has been widely used to detect and monitor surface displacements induced by earthquakes, volcanoes, anthropic activities, etc. However, it is often suffer from spatial and temporal decorrelation. This paper provides some case studies carried out by the L-band PALSAR, which has offered an opportunity to conquer these limitations. More specifically, we present a new model to better consider the relationship between elevation and zenith wet delay, which is promising to mitigate atmospheric effects on PALSAR interferograms, and a new filtering model to improve the quality of interferograms. We then use two pairs of PALSAR interferograms and one pair of ASAR interferogram to monitor and evaluate the ground fissures in Xi an, and we insert a set of PALSAR interferograms via the SBAS technique to retrieve displacement time series of Leizhou Peninsula, China. We also monitor the Yushu earthquake and Wenchuan earthquake using PALSAR data. More over, we infer 3D ground deformation due to L'Aquila earthquake, Italy with multi-platform SAR data. The result is consistent with GPS measurements. Through these work, we can better understand and utilize the PALSAR data on one hand and advance of InSAR technology on the other. 1. INTRODUCTION Northern and Eastern China has been suffering from serious ground fissure hazards, due mainly to underground water extraction, underground mining and construction of high-rise building [1]. Although InSAR has been a very promising technology for monitoring geological hazards, e.g., subsidence, landslides, earthquake, etc. [2-4], it has seldom been explicitly used for monitoring the deformation of a large number of gathered ground fissures. However, there have been considerable difficulties for C- band SAR to perform well in monitoring ground fissures in these regions due to the severe temporal decorrelation resulted from, e.g., ground fissures expansion, vegetation growth, rapid urban construction, etc. The L-band PALSAR has offered an opportunity to better monitor ground deformation along ground fissures in these regions. Moreover, ground fissure expansions are generally associated with 3D ground deformation. Conventional single platform DInSAR is difficult to model the deformation [1]. The research proposes to integrate multiplatform SAR data, e.g., JERS-1, ERS, ENVISAT, Radarsat, and PALSAR, etc., to model the 3D deformation. Finally, monitoring of the ground deformations along ground fissures can help to better understand the formation and development of a ground fissure. This will contribute to the effective mitigation of the damages caused by ground fissures hazards. It will also help to improve the planning and designing of underground activities (e.g., water extraction and mining) and therefore is very relevant to the needs of the society. 2. RESEARCH IMPLEMENTATION AND RESULTS 2.1. Developing algorithms and software for modeling long-wavelength atmospheric effects on PALSAR interferometry 2.1.1. Algorithms and software for calibrating longwavelength atmospheric noise At the preliminary stage, we take into account the relationship between Wet Zenith Delay (WZD) and the elevation in the Simple Cokriging with varying local means (SCKlm) algorithm as follows [5]: m ( u) = 0.0022768P + νu 10 γt h (1) SK h h
where ν and γ are site- and season- specific constants, and P, T and U are modeled as h h h 6 5.26 P h = P0 ( 1 22.6 *10 ( h h0 )) T h = T0 k( h h0 ) U 0 (2) U k = (3) where P 0, T0 and U are meteorological observations at 0 a reference point; k is the temperature elapse constant; and h is the elevation of the reference point. 0 We currently present a modified version of the Simple kriging with varying local means algorithm (SKlm+Baby), by substituting the Baby semi-empirical model with the Onn model (SKlm+Onn), which is an exponential law model of water vapor with the elevation as adopted by Onn [6] αh αh Z ( h) = m ( u) = Ce + hα Ce + Z (4) * * SK where Z * ( h ) is the regressed zenith wet delay at height h ; C is proportional to the amount of zenith wet delay measured at sea level; α is the delay rate of the vertical water vapor profile; and Z min is the zenith wet delay value at the highest location. C, α and Z min can be estimated by regression analysis. 2.1.2. Results We use six months GPS zenith wet delay measurements, spanning from 1 May to 31 October, 2002 over greater Los Angeles metropolitan area, to compare and evaluate the accuracy of the SKlm+Baby, the SKlm+Onn, the Best Linear Unbiased Estimator in combination with the water vapor Height Scaling Model (BLUE+HSM) [7], and the Best Linear Unbiased Estimator coupled with the Elevation-dependent Covariance Model (BLUE+ECM) [8]. Fig.1 illustrates the RMS differences between the measured and the interpolated zenith wet delays for date from 1 May to 31 October, 2002. It is very obvious from this figure that the SKlm+Onn interpolator has the lowest RMS errors, followed by the SKlm+Baby, while those of the BLUE+HSM and the BLUE+ECM are very large and with much greater variability min RMS (cm) 3 2.5 2 1.5 1 0.5 0 1 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 Days from 1 May 2002 BLUE + HSM BLUE + ECM SKlm + Baby SKlm + Onn Fig. 1 Comparison of the interpolation RMSs for the four interpolators. The abscissa is the number of days from 1 May 2002. The yellow, green, light blue, and dark blue lines denote the interpolation RMSs by the BLUE+HSM, BLUE+ECM, SKlm+Baby, and SKlm+Onn, respectively Table.1 lists the monthly averaged RMS errors of the four interpolators. The six months-averaged RMS error for the SKlm+Onn is only 0.55 cm, while those of the SKlm+Baby, the BLUE+HSM, and the BLUE+ECM amount to 0.77, 1.11, and 1.49 cm, respectively. Thus the SKlm+Onn outperforms the other interpolators by about 29% to 63%. Table 1 RMS differences between the measured and the interpolated zenith wet delays for different months (unit: cm) Model/RMS MAY JUNE JULY AUGUSTSEPTEMBEROCTOBERAVERAGE BLUE+HSM 0.71 0.8 1.6 1.18 1.49 0.89 1.11 BLUE+ECM 1.28 1.31 1.95 1.53 1.43 1.4 1.49 SKlm+Baby 0.54 0.79 0.91 0.84 0.81 0.74 0.77 SKlm+Onn 0.44 0.49 0.68 0.52 0.55 0.59 0.55 The above algorithms have been coded with the Matlab script and it is already used to mitigate the atmospheric effects on InSAR interferogram. Figure 2 shows the results for ASAR interferogram correction. we also compare the RMS differences between InSAR and GPS before and after atmospheric correction, which is shown in Figure 3. So we are confident that the new atmospheric correction model is suitable for mitigating PALSAR interferograms.
Fig.2 (a) Original Interferogram: 20040807_20051001; (b) Differenced wet delay map; (c) Corrected Interferogram σ is related to these two quantities, we suggest φ determining the filter parameter α by exploiting σ. φ More specifically [11], β σ φ α = (7) σ φ,max where σ is the mean phase SD over an effective patch; φ σ is the largest value of phase standard deviations, φ,max i.e., the phase standard deviation corresponding to coherence γ = 0 ; and β is an empirical constant that can be determined statistically based on a large number of numerical simulations. Eqs. (5) and (7) form the new filter developed to filter atmospheric noise (along with other noise).the above algorithms have also been coded with the Matlab script. It is ready to be used for filtering atmospheric noise (along with other noise) in the PALSAR interferogram. Fig.3 Comparison of LOS range change differences between InSAR and GPS before and after atmospheric correction 2.2. Developing Algorithms and software for filtering short-wavelength atmospheric noise (along with other noise) As noted in above section, it may be feasible to use the external data (e.g., MODIS, GPS, etc) to calibrate atmospheric noise with a wavelength of about 2 km or longer. For atmospheric noise with wavelength shorter than that, we can only develop filter to suppress them (along with other noise). 2.3. Processing of PALSAR data for monitoring onedimensional (1D) ground deformation along ground fissures 2.3.1. Ground fissure in Xi an, China Xi an of China has suffered from ground subsidence since 1950s, due mainly to underground water extraction, highrise building construction and possible tectonic motion. The ground subsidence has given rise to 13 ground fissures around Xi an city, most of them along the NE-SW direction (see Figure 4). The Frequency domain Goldstein radar interferogram filter has been very widely used [9]: α { Z( u, v) } Z( u, ) Z ( u, v) = S v (5) where α is the filter parameter; S {} is a smoothing operator; u and v are spatial frequencies. Baran et al. (2003) modified the filter parameter to [10] α = 1 γ (6) where γ is the mean coherence value over the effective patch (patch area minus the overlap area). We propose to modify the Goldstein filter to incorporate information contained in both the coherence γ and the look number L. Since the phase standard deviation (SD) Fig.4 Distribution of ground fissure in Xi an 2.3.2 Monitoring ground subsidence in Xi an with PALSAR FBS/FBD interferogram Two pairs of PALSAR scenes (i.e., 02/11/07-30/01/07, 02/11/07-17/09/07) and one pair of ASAR scene
(10/03/07-10/11/07) are chosen in this study. The PALSAR pairs and ASAR pair are processed with the Gamma software. The perpendicular baseline of PALSAR is about 1799m and 536m, respectively, while the perpendicular baseline of ASAR is around 259m. The originally generated PALSAR interferogram (two-pass) is shown in Figure 5, the generated PALSAR interferogram (three-pass) is illustrated in Figure 6, and the ASAR interferogram is shown in Figure 7. The deformation map of profile a and b are shown in Figure 8, whereas Figure 9 compare the STD difference between InSAR and collocated GPS. Fig. 7 Two-pass ASAR differential interferogram Fig. 5 Two-pass PALSAR differential interferogram Fig. 8 Profile a and b in Fig.5 and Fig.6, the black solid line in Fig.8 denotes the ground fissures Fig. 6 Three-pass PALSAR differential interferogram Fig. 9 STD differences between InSAR and collocated GPS (2007-2008) 2.4. Monitoring Yushu earthquake with PALSAR data We use Multi Aperture InSAR (MAI) technique [14] with PALSAR data to acquire the deformation pattern of Yushu earthquake.
This technique is based on split-beam processing of InSAR data to create forward- and backward-looking interferograms. The phase difference between the two modified interferograms provides the along-track displacement component. Thus, from each conventional InSAR pair we extract two components of the displacement vector: one along the line of sight, the other in the alongtrack direction. 4π Φ MAI =Φ f Φ b = nd az (8) l Where l is the antenna length, n is ratio of synthetic antenna. d az is the displacement in the along-track direction. In order to alleviate the effects of baseline and ionospheric perturbations, we use the polynomial model to fit the error phase induced by baseline errors, and we use a filter method is azimuth direction and interpolation model to reduce ionospheric perturbations Figure 12 demonstrates the spatial pattern of coseismic deformation of Yushu earthquake. It is clearly that the surface above the fault moved to the northwest direction, while the surface below the fault moved to the southeast direction, Maximum deformation occurs near the fault, so we believe that the earthquake belong to left lateral strikeslip. 2.5. Coseismic fault slip of the Wenchuan earthquake estimated from PALSAR interferogram We use L-band ALOS/PALSAR data from six ascending tracks 471 to 476, to form interferograms of coseismic deformation in regions that cover the LMS fault zone and the rupture of the Wenchuan earthquake. We process the SAR data from a level-1.0 product using the software package GAMMA [15]. To remove effects of topography, we use a mosaic of Shuttle Radar Topography Mission digital elevation models with 90-m postings. We use a fault model composed of three planar fault segments of the Beichuan fault, and one planar segment representing the parallel Pengguan fault. Maximum thrustslip is up to 6.7 m near the surface, and occurs in two locations, near Yingxiu in the south and Beichuan in the center of the rupture. Maximum strike-slip is over 4 m, and occurs near Pingtong and Nanba along the northern end of the rupture. We find that the ratio of coseismic thrust- to strike-slip on the Beichuan fault decreases from 1.5 to 0.7 from the SW to the NE. The coseismic ground deformation interferogram, the predicted interferogram, and the residuals between the observed and simulated interferograms are illustrated in Figure13 (a), (b), and (c), respectively. The distribution of coseismic fault slip is outlined in Figure 14. Fig. 12 The LOS displacement monitored by PALSAR (ascending) interferograms in Yushu, Italy (unit: cm), note that the arrow are 2D deformation, while the color means the displacement in the along-track direction calculated through MAI. Fig. 13 (a) InSAR and GPS observations of coseismic ground deformation; interferograms are shown without masking decorrelated regions, and GPS observations are shown by black vectors. (b) Interferograms predicted by the slip model in Figure 14 and simulated using the actual satellite geometry of each track. (c) Residuals between the observed and simulated interferograms; decorrelated regions of the interferograms are masked out.
Fig. 15 The LOS displacement monitored by ASAR (ascending & descending) and PALSAR (ascending) interferograms in L'Aquila, Italy (unit: m) Fig. 14 Distribution of coseismic fault slip from the joint inversion of the InSAR and GPS data. Colors in the 3D perspective plot indicate thrust-slip magnitude, and view is from the northwest. In the individual plots of the fault segments, color indicates the magnitude of the total fault slip, vectors indicate the direction of fault slip, and distance is relative to the start of each fault segment. Pale yellow star denotes the location of the hypocenter of the Wenchuan earthquake. Fig. 16 North-south, east-west, and vertical displacements (unit: m) 2.6. Processing of PALSAR data for monitoring threedimensional (3D) ground deformation Based on multi-platform, we combine SAR interferometric phase of ascending and descending orbits with Least Square solution to infer three-dimensional displacement [16]. Firstly, we process ASAR and PALSAR data acquired from ascending and descending orbits to generate differential interferograms and calculate the light-of-sight (LOS) displacements, then, co-register them, finally, we can estimate the three-dimensional displacement through Least Square solution. Figure 15 shows the LOS displacement monitored by ASAR (ascending & descending) and PALSAR (ascending) interferograms in L'Aquila, Italy, respectively. Figure 16 denotes north-south, east-west, and vertical displacements. The comparison between north-south, east-west, and vertical displacements and GPS measurements is illustrated in Figure 17. According to Figure 17, we can see that the accuracy of east-west (RMS=3.2cm), and vertical displacements (RMS=12.8cm) are higher than north-south displacement (RMS=96.4cm). Fig. 17 Comparison of three-dimensional displacements inferred from InSAR with GPS measurements. 4. DISCUSSION AND CONCLUTIONS We have spent much time on developing the algorithms and software for reducing the long- and short- wavelength atmospheric effects. The new topography-dependent correction model (i.e., SKlm+Onn) better represent the atmospheric water vapor elevation-dependent nature, thus it achieve the best result when comparing other methods. Although the algorithm and software for calibrating the long-wavelength atmospheric effects haven t been used actually in the PALSAR data, it is still promising for implement to PALSAR interferograms. With the merit of the L-band PALSAR can be less affected by temporal decorrelation (e.g., due to vegetation growth, rapid urban constructions, and ground fissures expansions), we monitor one-dimensional (1D) ground deformation along selected fissures Xi an and Leizhou
Peninsula with PALSAR and ASAR, the result is consistent with GPS measurements, so we can conclude that the performance of PALSAR in monitoring ground fissures and land subsidence is better than C-band ASAR. We use MAI technique to gain the 2D deformation pattern of Yushu earthquake successfully, and with the available of GPS measurements of coseismic deformation from the 2008 Wenchuan earthquake, we find that the amount of thrust-slip decreases to the NE along the Beichuan fault, and that the strike-slip increases to the NE. Hence, thrustslip dominates the southernmost region of the Beichuan fault near the hypocenter, with right-lateral slip dominating to the NE. We also find that there is a small amount of thrust-slip on the parallel and shallower dipping Pengguan fault to the SE of the Beichuan. Combining ascending and descending PALSAR data, we inferred the 3D ground deformation caused by L'Aquila earthquake, Italy. This research can help us to deduce 3D ground deformation along selected ground fissures in the future. 5. ACKNOWLEDGEMENT We thank Japan Aerospace Exploration Agency/Earth Observation Research Center for approving the project and for providing the ALOS/PALSAR data for this research. The hard and highly efficient work of the staffs of ALOS science program are greatly appreciated. The publications supported by this project are listed as follows: [1] G.C. Feng, E.A. Hetland, X.L. Ding, Z.W. Li, L. Zhang. Coseismic fault slip of the 2008 M-w 7.9 Wenchuan earthquake estimated from InSAR and GPS measurements. Geophysical Research Letters, 37, L01302, 2010. [2] X.M. Xing, J.J. Zhu, Y.Z Wang, Y.F. Yang. A new method of CRInSAR and PSInSAR combined calculation. 6th International Conference on Wireless Communications, Networking and Mobile Computing, 2010 [3] Q. Sun, Z.W. Li, J. Hu, J.J. Zhu, X.L. Ding. InSAR Observation of surface displacement over Southern California with ALOS PALSAR Data. 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