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UAVSAR Science Validation Experiments: Volcanoes, Earthquakes, and Calibration

This article discusses the design and validation experiments that demonstrate the usefulness of the UAVSAR for geophysical research. It covers topics such as volcanoes, earthquakes, and calibration, with a focus on system performance limits and measurement accuracies.

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UAVSAR Science Validation Experiments: Volcanoes, Earthquakes, and Calibration

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  1. UAVSAR Science Validation Experiments Howard Zebker, Shadi Oveisgharan, Ana Bertran-Ortiz, Fayaz OnnStanford University November 9, 2005

  2. Summary • Purpose: design validation experiments that demonstrate the usefulness of the UAVSAR for geophysical research • Approach • Identify major science objectives • Determine measurement types and accuracies for each • Suggest test regions and experiment plan

  3. Approach • Evaluate the viability and limitations of an airborne system in measuring different types of earth deformations • Generic experiments and calibration • Primary Requirements- • Volcanoes • Earthquakes • Environmental artifacts- • Atmospheric noise • Secondary investigations- • Snow accumulation rate and volume • Specify system performance limits for useful research

  4. General Flight Tests • Covered previously, reviewed here • Concentrate on repeatability, coherence, amplitude calibration • Fly over corner reflector array, reflectors at various aspect angles • Assess amplitude and phase variation with steering angle

  5. General Flight Tests

  6. General Flight Tests – Calibration

  7. General Flight Tests – Calibration

  8. Volcanoes • Many volcanoes can be modeled as Mogi sources(Ex: Galapagos Islands- Darwin, Wolf & Cerro Azul) • Mogi source: an inflating point source in elastic half-space, specified by a pressure and depth at a particular location (4 parameters) • Examine interferograms for different Mogi source depths and volumes • Determine depth resolution vs. phase error, volume change resolution vs. phase error

  9. Volcanoes A Mogi source at (x’,y’) causes the following ground displacement [Mogi, 1958] at (x,y); : Poisson’s Ratio = 0.2 (assumed, can be between -1 & 0.5)(negative ratio of lateral extension to axial extension) V: Volume change (assumed here to be 1.8e6 if const.) d: Depth of Mogi source (from 1km to 8km) Swath: Assumed to be 20km

  10. Volcanoes For InSAR, we are interested in line of sight phase data at location (x,y) due to Mogi source: i: unit vector towards airborne system u(x,y,z): Mogi source 3D ground displacement Model Sanity Check: Darwin Volcano 3km deep magma chamber, Deformation: 3-6 cm LOS 4.7cm!

  11. Volcanoes Model surface deformation signature differences varying Mogi source depth and change in volume: (Linear relation between Mogi source deformation and change in volume) (Concave relation between Mogi source deformation and change in depth)

  12. Volcanoes Resolution of Magma Chamber Depth Base V=1.8e6 m3Look Angle=23° Conclusions: To resolve between 1km and 2km depth we need to resolve surface deformation of ~33 cm. To resolve between 3km and 4km depth we need to resolve surface deformation of ~3 cm.

  13. Volcanoes What happens if we change the look angle to 70 ° ? Base V=1.8e6 m^3Look Angle=70° Now we need 21 cm to resolve between 1 and 2 km depth; 2 cm between 3 and 4 km

  14. Volcanoes Mogi source depth-variable interferograms at 23° look angle: _ =

  15. Mogi source depth-variable interferograms at 70° look angle: Volcanoes _ =

  16. The effect of V on interferograms at 23° look angle: Volcanoes _ =

  17. The effect of V on interferograms at 70° look angle: Volcanoes _ =

  18. Sensitivity to aircraft flight lines (look angle): Error of 1o in look angle (actual angle of 24o, think we have 23o) Volcanoes Small sensitivity to look angle, thus absolute aircraft position relatively unimportant. As expected the sensitivity decreases with depth (deeper sources, more extensive ground deformation). Repeatability within interferometric tube critical to form interferograms.

  19. Volcanoes Interferograms for the previous analysis: - =

  20. Volcanoes - Experiments Select test sites with active volcanism Long Valley caldera Hawaii - Kilauea, Mauna Loa St. Helens (?) Collect repeat pass data with ~ 1 week separation Collect repeat pass data with ~ 1 year separation Form interferograms with <2 cm residual noise Again, no real need to measure deformation as long as system performance would support sensitivity required. But demonstration of utility requires deformation!

  21. Volcano Flight Tests

  22. Volcano Flight Tests

  23. Volcano Flight Tests

  24. Earthquakes Model: 10 km long fault segment- entirely within swath, strike at N45oE. The strike-slip fault is vertical, and breaks the surface. Fault-slip of 2 or 50 cm. San Andreas Fault: strike-slip, 960km long, 32km deep. North American plate slides SE against the Pacific plate which is sliding NW. Plate motion is average rate of ~4 cm/year. Look Angle = 23o , 45obetween fault and plane flight direction fault-slip of 50 cm

  25. Earthquakes Effect of fault plane depth Look angle 23°, 45° between fault and plane flight direction, Fault-slip of 50 cm _ =

  26. Earthquakes Look Angle = 70o , 45o between fault and plane flight direction, Fault-slip of 50 cm

  27. Effect of fault plane depth Look angle 70°, 45° between fault and plane flight direction, fault-slip of 50 cm Earthquakes _ =

  28. Look Angle = 23o , 0o between fault and plane flight direction, Fault-slip of 50 cm Earthquakes

  29. Effect of fault plane depth Look angle 23°, 0° between fault and plane flight direction, Fault-slip of 50 cm Earthquakes _ =

  30. Look Angle = 70o , 0o between fault and plane flight direction, Fault-slip of 50 cm Earthquakes

  31. Effect of fault plane depth Look angle 70°, 0° between fault and plane flight direction, Fault-slip 50 cm Earthquakes _ =

  32. Earthquakes Look Angle = 23o , 45obetween fault and plane flight direction fault-slip of 2 cm

  33. Earthquakes Effect of fault plane depth Look angle 23°, 45° between fault and plane flight direction, Fault-slip of 2 cm _ =

  34. Earthquakes Look Angle = 70o , 45o between fault and plane flight direction, Fault-slip of 2 cm

  35. Effect of fault plane depth Look angle 70°, 45° between fault and plane flight direction, fault-slip of 2 cm Earthquakes _ =

  36. Look Angle = 23o , 0o between fault and plane flight direction, Fault-slip of 2 cm Earthquakes

  37. Effect of fault plane depth Look angle 23°, 0° between fault and plane flight direction, Fault-slip of 2 cm Earthquakes _ =

  38. Look Angle = 70o , 0o between fault and plane flight direction, Fault-slip of 2 cm Earthquakes

  39. Effect of fault plane depth Look angle 70°, 0° between fault and plane flight direction, Fault-slip 2 cm Earthquakes _ =

  40. Earthquakes - Experiments Select test sites with active deformation Locked/unlocked transition of SAF near Parkfield Postseismic motion along recent earthquake sites in southern California Model against elastic rebound, viscoelasticity, poroelasticity Routine acquisitions along potential earthquake sites along SAF Collect repeat pass data with ~ 1 week separation Collect repeat pass data with ~ 1 year separation Fly parallel to, across, at 45° to fault Form interferograms with <1 cm residual noise Check inferred deformation against geologic constraints and other deformation measurements

  41. Earthquake Flight Tests

  42. Earthquake Flight Tests

  43. Atmospheric Noise Limitations of UAVSAR: Major error is atmospheric noise • Atmospheric phase noise in InSAR data due to inhomogeneities in the distribution of water vapor in the lower atmosphere • Atmospheric phase noise is spatially-variable (turbulence) and can display elevation-dependence (vertical stratification) when interferometer baseline is small (1 m for InSAR observations shown above) • Figure (a), (b) compares elevation-dependence of InSAR phase residuals and estimates of zenith wet delay from GPS. • Can use GPS to correct InSAR measurements for atmospheric delay

  44. Atmospheric Noise • SAR interferogram from observations over Los Angeles County on 27th Nov. 1999 and Feb 5th 2000. The perpendicular baseline was 1 meter. After correcting for topographic phase, the residuals (middle panel) are dominated by atmospheric noise. • Strong correlation between atmospheric phase and topography is observed (vertical stratification of the atmosphere) • Neutral atmospheric refractivity model decreasing exponentially with height from GPS data gives elevation-dependent phase (left). • Subtracting model from the unwrapped InSAR phase residuals (middle) yields difference (right), with 48% reduction in phase RMS

  45. Atmospheric Noise • In addition to correcting for vertical stratification of the atmosphere, GPS data can also be used to interpolate maps of spatial variation of atmospheric delay. • Correcting InSAR residuals with interpolated maps of atmospheric delay from GPS data acquired exactly at the SAR overflight time (b) yields another 17% reduction in phase fluctuation RMS. • With the “frozen-flow” hypothesis, map (c) corrects the InSAR image to 43% reduction in phase RMS

  46. Atmospheric Noise - Experiments • To get the most out of UAVSAR results, atmospheric influence on phase must be minimized • Demonstrate by flights over GPS-rich region • Limited number of flights needed • Single pair shows method • Multiple repeats allow other experiments such as lengthening or shortening of LA Basin extent • Continuous GPS networks • SCIGN is best bet for most experiments (LA Basin) • Decent continuous network in Hawaii, but water vapor problem significant • Japan for foreign deployment

  47. Atmosphere Flight tests

  48. Algorithms – Persistent scatterers • Will use system to develop/validate new data reduction methods • Persistent scatterers current focus of research • Real-time and other InSAR algorithm development as well

  49. Persistent Scatterers • Spaceborne application yields correlations where InSAR fails • PS locations using low-pass filter finds reasonable number of points Lost Hills Oilfield

  50. Persistent Scatterers • Improved algorithm using adaptive filter finds fast deformation field also

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