11:00 AM - 1:00 PM
[STT39-P07] Mapping Postseismic and Interseismic Deformation in Himalaya using ALOS-2 Interferometry
Keywords:InSAR, Himalaya, Crustal Deformation, Split Spectrum Method
The rapid uplift of the Himalayas is essentially driven by the collision between the Indian and the Eurasian plates (e.g., Molnar and Tapponnier, 1975), which has been measured mainly by ground-based geodetic techniques (e.g., Jackson and Bilham, 1993). Monitoring such interseismic crustal deformation at high spatial resolution is important for understanding the processes of earthquake generation and mountain building. However, due to extremely rugged terrain in the Himalayas, the report of interseismic displacements using InSAR is very limited (Grandin et al., 2012; Sreejith et al., 2018). In this study, we apply InSAR analysis of ALOS-2 data to the Himalayas to detect (1) the postseismic deformation following large earthquakes and then (2) the inter-seismic crustal deformation.
Our first goal is to detect the postseismic deformation following the Gorkha earthquake (Mw 7.8), which has a larger amplitude than the inter-seismic deformation and has already been reported previously (e.g., Wang and Fialko, 2018). We used ScanSAR images because this area has been frequently acquired by ScanSAR mode. Since the terrain is extremely rugged, multiple image coregistration has been conducted using different methods, which greatly improves the interferometric coherence. For each interferogram, the contributions of ionospheric and tropospheric disturbances were modeled by a polynomial function and subtracted from the original interferogram. We further reduced the noise by stacking thus corrected images. Then we successfully obtained the postseismic displacements consistent with a previous study (Wang and Fialko, 2018).
Most of the InSAR images of the Himalayas are strongly affected by ionospheric disturbances. Therefore, it is the most important aspect of this study to reduce the ionospheric effects. Considering the relatively long wavelength of inter-seismic displacements, it is not preferable to model such noise by a polynomial function: it may also include a part of the crustal deformation. Therefore, we decided to apply the Split Spectrum Method (SSM, Gomba et al., 2016; Furuya et al., 2017), which utilizes the radio wave dispersion in the ionosphere. To apply the SSM, we must split the frequency band of radar pulses emitted by SAR satellites into two parts, which decreases the S/N ratio and interferometric coherence. In the case of ScanSAR data, we rarely obtained successful results due to its narrow bandwidth, while we obtained many successful results using Strip-map images because of wider bandwidth. Then we smoothed the dispersive phase image with a median filter and subtract it from the original interferogram. We further used these corrected images for the InSAR time series analysis based on the SBAS method (Berardino et al., 2002; Schmidt and Bürgmann, 2003). The results indicate the postseismic deformation of the Gorkha earthquake with better accuracy than the polynomial fitting method.
The above analysis flow was applied to the detection of interseismic crustal deformation. We applied SSM and InSAR time series analysis to the data taken in strip-map mode along the Thakkhola-Mustang Graben, which cuts across the Himalayas but is characterized by a relatively gentle terrain. With L-band SAR data, the overall coherence of our result is higher than the previous work using C-band SAR data (Grandin et al., 2012). The accuracy of our results is currently being verified.
Our first goal is to detect the postseismic deformation following the Gorkha earthquake (Mw 7.8), which has a larger amplitude than the inter-seismic deformation and has already been reported previously (e.g., Wang and Fialko, 2018). We used ScanSAR images because this area has been frequently acquired by ScanSAR mode. Since the terrain is extremely rugged, multiple image coregistration has been conducted using different methods, which greatly improves the interferometric coherence. For each interferogram, the contributions of ionospheric and tropospheric disturbances were modeled by a polynomial function and subtracted from the original interferogram. We further reduced the noise by stacking thus corrected images. Then we successfully obtained the postseismic displacements consistent with a previous study (Wang and Fialko, 2018).
Most of the InSAR images of the Himalayas are strongly affected by ionospheric disturbances. Therefore, it is the most important aspect of this study to reduce the ionospheric effects. Considering the relatively long wavelength of inter-seismic displacements, it is not preferable to model such noise by a polynomial function: it may also include a part of the crustal deformation. Therefore, we decided to apply the Split Spectrum Method (SSM, Gomba et al., 2016; Furuya et al., 2017), which utilizes the radio wave dispersion in the ionosphere. To apply the SSM, we must split the frequency band of radar pulses emitted by SAR satellites into two parts, which decreases the S/N ratio and interferometric coherence. In the case of ScanSAR data, we rarely obtained successful results due to its narrow bandwidth, while we obtained many successful results using Strip-map images because of wider bandwidth. Then we smoothed the dispersive phase image with a median filter and subtract it from the original interferogram. We further used these corrected images for the InSAR time series analysis based on the SBAS method (Berardino et al., 2002; Schmidt and Bürgmann, 2003). The results indicate the postseismic deformation of the Gorkha earthquake with better accuracy than the polynomial fitting method.
The above analysis flow was applied to the detection of interseismic crustal deformation. We applied SSM and InSAR time series analysis to the data taken in strip-map mode along the Thakkhola-Mustang Graben, which cuts across the Himalayas but is characterized by a relatively gentle terrain. With L-band SAR data, the overall coherence of our result is higher than the previous work using C-band SAR data (Grandin et al., 2012). The accuracy of our results is currently being verified.