Additional file 1 of Three-dimensional deformation and its uncertainty derived by integrating multiple SAR data analysis methods
Creators: name: Morishita, Yu
name: Kobayashi, Tomokazu
Publisher: name: figshare
Year: 2022
Subjects: subject: Artificial Intelligence and Image Processing
subject: FOS: Computer and information sciences

description: Additional file 1. Fig. S1. Wrapped interferograms before and after tropospheric and ionospheric noise correction and their resulting corrections. (a) Kumamoto, (b) Tottori. Fig. S2. Azimuth deformation data with significant ionospheric noise from SBI and the pixel offset method before and after ionospheric noise correction and their corrections. (a) Kumamoto, (b) Tottori. Fig. S3. Observations, calculations, ramps, and residuals for each deformation dataset in the 3D decomposition for Kumamoto. (a) InSAR. (b) Range SB (c) Azimuth SBI. (b) Range offset (e) Azimuth offset. Fig. S4. Observations, calculations, ramps, and residuals for each deformation dataset in the 3D decomposition for Tottori. (a) InSAR (b) Range SBI. (c) Azimuth SBI. Fig. S5. Same as Fig. 3a–c but rewrapped to 20 cm/cycle to visualize small changes. Fig. S6. As for Fig. 6(a)(b)(d) but with the SAR-derived deformation masked. Fig. S7. 3D deformation derived without weighting. (a)–(c) Kumamoto. The data are rewrapped to 20 cm/cycle to visualize small changes. (d)–(f) Tottori. Fig. S8. (a)–(c) 3D deformation decomposed from all data including noisy data with significant decorrelation or azimuth ionospheric noise (Table 1 and Fig. S2) and masked based on noise metrics for Kumamoto. The data are rewrapped to 20 cm/cycle to visualize small changes. (d)–(f) Estimated standard errors of the 3D components. (g)–(i) Histograms of the standard errors of the 3D components. The light-gray parts denote the frequency distribution including the masked pixels. Fig. S9. As for Fig. S8 but for Tottori. Fig. S10. RMS of the residuals. (a), (b) Derived from all methods without and with noisy data for Kumamoto, respectively. (c), (d) Derived from all methods without and with noisy data for Tottori, respectively. Fig. S11. (a)–(c) 3D deformation derived using conventional deramping instead of iterative deramping and masked based on noise metrics for Kumamoto. The data are rewrapped to 20 cm/cycle to visualize small changes. (d)–(f) Estimated standard errors of the 3D components. (g)–(i) Histograms of the standard errors of the 3D components. The light-gray parts denote the frequency distribution including the masked pixels. Fig. S12. As for Fig. S11 but for Tottori. Fig. S13. (a)–(c) 3D deformation decomposed from InSAR data alone and masked based on noise metrics for Kumamoto. The data are rewrapped to 20 cm/cycle to visualize small changes. (d)–(f) Estimated standard errors of the 3D components. (g)–(i) Histograms of the standard errors of the 3D components. The light-gray parts denote the frequency distribution including the masked pixels. Fig. S14. (a)–(c) 3D deformation decomposed from SBI data alone and masked based on noise metrics for Kumamoto. The data are rewrapped to 20 cm/cycle to visualize small changes. (d)–(f) Estimated standard errors of the 3D components. (g)–(i) Histograms of the standard errors of the 3D components. The light-gray parts denote the frequency distribution including the masked pixels. Fig. S15. (a)–(c) 3D deformation decomposed from only the pixel offset data and masked based on noise metrics for Kumamoto. The data are rewrapped to 20 cm/cycle to visualize small changes. (d)–(f) Estimated standard errors of the 3D components. (g)–(i) Histograms of the standard errors of the 3D components. The light-gray parts denote the frequency distribution including the masked pixels. Fig. S16. As for Fig. S13 but for Tottori. Fig. S17. As for Fig. S14 but for Tottori. Fig. S18. As for Fig. S15 but for Tottori.