Estimating Fault Configurations From InSAR Data

Cameron Saylor

Submitted August 13, 2019, SCEC Contribution #9492, 2019 SCEC Annual Meeting Poster #209

Significant errors can occur in fault geometry and slip dislocation models as a result of volumetric distributions of sources not well represented by simple planar or rectangular fault models. For this reason, it is necessary to utilize all of the tools available to improve estimates of fault geometry and location. One such tool is interferometric synthetic aperture radar (InSAR), which provides maps of surface deformation that contain valuable information about the faults hidden beneath the surface, as well as the complexity of the fault system giving rise to the image. In a new approach, we fit the InSAR ground deformation from fault deformation interferograms using a combination of linear inverse theory and volumetric distributions of Okada green's functions for surface deformation. As a first step, we fit a sample interferogram with a small number of rectangular dislocations. Beginning from this model, we then add complexity by assuming a volumetric distribution of point sources. Thus a collection of Okada point sources are derived to optimize the fit to the data, taking account of the inevitable overfitting problems that may arise. Fault complexity of this type may be important for earthquakes such as the 2016 Kaikoura, NZ earthquake which involved a dozen or more faults. In fitting the data, we adjust various parameters of the point sources such as faulting type (strike-slip, dip-slip, etc.), dip angle and seismic moment to determine their effect on the resulting fault geometry. We further consider smoothing and filtering algorithms (essentially trade-off parameters) to reduce model noise produced by the inversions. To test our methods, we have first applied the method to synthetic data, followed by applications to observed UAVSAR and InSAR interferograms. We report first results and discuss problems and advantages of this approach.

Citation
Saylor, C. (2019, 08). Estimating Fault Configurations From InSAR Data. Poster Presentation at 2019 SCEC Annual Meeting.


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