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Noise Reduction of InSAR Time-series Analysis and Application to Kilauea Volcano

Noise Reduction of InSAR Time-series Analysis and Application to Kilauea Volcano. Yo Fukushima, DPRI, Kyoto University. …. InSAR Time-Series Analysis. Time. …. Time. Small BAseline Subset (SBAS) analysis (Berardino et al., 2002). Use SVD to obtain the min. norm for velocity. Time.

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Noise Reduction of InSAR Time-series Analysis and Application to Kilauea Volcano

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  1. Noise Reduction of InSAR Time-series Analysis and Application to Kilauea Volcano • Yo Fukushima, DPRI, Kyoto University

  2. … InSAR Time-Series Analysis Time … Time

  3. Small BAseline Subset (SBAS) analysis (Berardino et al., 2002) Use SVD to obtain the min. norm for velocity Time

  4. Typical (?) interferogram Atmospheric noise correlated with altitude Long-wavelength ramp due to orbital inaccuracy etc. + Offset + artifact due to DEM error Deformation We want to separate them.

  5. Observation Equation UNWRAPPED phase at the k-th pixel of the i-th ifg: Phase (Obs.) Correlated w/altitude LOS displ. Offset Ramp （bilinear） DEM error contribution i k

  6. Observation Equation UNWRAPPED phase at the k-th pixel of the i-th ifg: Phase (Obs.) Correlated w/altitude LOS displ. Offset Ramp （bilinear） DEM error contribution

  7. Observation Equation UNWRAPPED phase at the k-th pixel of the i-th ifg: Phase (Obs.) Correlated w/altitude LOS displ. Offset Ramp （bilinear） DEM error contribution

  8. Observation Equation UNWRAPPED phase at the k-th pixel of the i-th ifg: Phase (Obs.) Correlated w/altitude LOS displ. Offset Ramp （bilinear） DEM error contribution

  9. Observation Equation Bperp UNWRAPPED phase at the k-th pixel of the i-th ifg: Phase (Obs.) Correlated w/altitude LOS displ. Offset Ramp （bilinear） DEM error contribution Range Inc. angle

  10. Observation Equation UNWRAPPED phase at the k-th pixel of the i-th ifg: What to estimate: ・v：Velocity time-series at every px ・a, b, c, f：coefficients for every ifg ・δh：DEM error for every px

  11. This can be solved in principle by a linear inversion Problem 1 The matrix is too large Problem 2 Rank deficiency Need constraints or SVD. How can we weight the model params. of different dimensions? -> Maximum likelihood Two-step approach: Inversion with down-sampled data. px-by-px inversion using corrected ifgs

  12. Baseline M = 12 N = 21

  13. Utrue (all color scale [-5 5] radian)

  14. Dtrue Note: Range increase is positive (red) throughout my talk

  15. topo

  16. offset

  17. ramp

  18. DEM error

  19. DEM error effect

  20. Unmodeled noise

  21. Input for simulation (sum of all)

  22. Result of denuisance:Estimated offset + ramp + topo

  23. Result of denuisance:Corrected unwrapped ifgs

  24. Dtrue Dnoise Dinput=Dtrue+Dnoise D corrected

  25. Dtrue Dnoise Dnoise estimated (except DEM correction) D=Dtrue+Dnoise

  26. 2nd step: invert the corrected ifgs pixel-by-pixel

  27. Utrue Uestimated DEM error DEM error estimated

  28. Application to Kilauea volcano Mauna Loa Kilauea

  29. ALOS/PALSAR, θ~10° 3 24 acquisitions 37 ifgs

  30. Ascending Descending

  31. June 2007 event Pre-event Co-event Post-event

  32. Pre-event Uplift

  33. Co-event Subsidence Dike intrusion

  34. Post-event Subsidence

  35. Consistency w/GPS（RMS misfit 2.0cm）

  36. Summary • I proposed a noise reduction method in SBAS InSAR time-series approach • Applied to Kilauea data. • with respect to the 2007 father’s day event, we found: • - Pre-event: Uplift SW of the summit • - Co-event：Uplift due to dike intrusion and • subsidence at the summit • - Post-event：Subsidence at the summit & PuuOo • Acknowledgement: The used PALSAR data are shared by the PIXEL consortium and provided from JAXA. The GPS data The data are provided from JAXA under cooperative research contract with the GSI and with the Earthquake Research. I thank the Hawaiian Volcano Observatory for the GPS data processing and sharing.

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