Joint Model and Phase Parameter Estimation in Sparse Synthetic Aperture Radar Interferometric Measurements
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Interferometric SAR modelling of near surface data to improve geological model in the Surat Basin, Australia
Here we present a study on geophysical inverse modelling for subsurface structural properties of an unconventional hydrocarbon site that was monitored previously by Interferometric Synthetic Aperture Radar (InSAR) technology for surface deformation. A static three-dimensional geomodel along with extracted property maps replicates the depth of each underlying stratigraphic unit and structural feature with the density of each geological layer. We examine the hypothesis that integration of elastic properties of each formation layer with InSAR observations in a stratified elastic medium will lead to a viscoelastic geophysical inverse problem that can be solved to estimate fractional volume change at the reservoir level. Moreover, we examine synthetic scenarios in which the elastic properties of the formations are perturbed before determining the resulting impact on the rate of surface deformation. The results show that although the slope of underlying formations, their density and depth can define the extent and pattern of a deformation signal, their properties have a marginal impact on volumetric change compared to the dense network of shallow depth Coal Seam Gas (CSG) mining wells. Besides, it is also demonstrated that the inversion of InSAR deformation maps can resolve the uncertainties associated with low-resolution seismic interpretation as well as filling the data gaps within seismic acquisitions. A significant contribution of this investigation to the geological basin modelling involves a) introducing a remote and non-invasive technology such as InSAR to improve geophysical mapping of subsurface structures such as faults in areas with sparse or no reflective seismic information, and b) applying a multi-layer viscoelastic geophysical source model for an unconventional hydrocarbon reservoir such as CSG.
58 GEOSCIENCES↗
Application of Radar Data to Remote Sensing and Geographical Information Systems
The field of synthetic aperture radar changed dramatically over the past decade with the operational introduction of advance radar techniques such as polarimetry and interferometry. Radar polarimetry became an operational research tool with the introduction of the NASA/JPL AIRSAR system in the early 1980's, and reached a climax with the two SIR-C/X-SAR flights on board the space shuttle Endeavour in April and October 1994. Radar interferometry received a tremendous boost when the airborne TOPSAR system was introduced in 1991 by NASA/JPL, and further when data from the European Space Agency ERS-1 radar satellite became routinely available in 1991. Several airborne interferometric SAR systems are either currently operational, or are about to be introduced. Radar interferometry is a technique that allows one to map the topography of an area automatically under all weather conditions, day or night. The real power of radar interferometry is that the images and digital elevation models are automatically geometrically resampled, and could be imported into GIS systems directly after suitable reformatting. When combined with polarimetry, a technique that uses polarization diversity to gather more information about the geophysical properties of the terrain, a very rich multi-layer data set is available to the remote sensing scientist. This talk will discuss the principles of radar interferometry and polarimetry with specific application to the automatic categorization of land cover. Examples will include images acquired with the NASA/JPL AIRSAR/TOPSAR system in Australia and elsewhere.
vanZyl, Jakob J.↗
The InSAR Scientific Computing Environment
We have developed a flexible and extensible Interferometric SAR (InSAR) Scientific Computing Environment (ISCE) for geodetic image processing. ISCE was designed from the ground up as a geophysics community tool for generating stacks of interferograms that lend themselves to various forms of time-series analysis, with attention paid to accuracy, extensibility, and modularity. The framework is python-based, with code elements rigorously componentized by separating input/output operations from the processing engines. This allows greater flexibility and extensibility in the data models, and creates algorithmic code that is less susceptible to unnecessary modification when new data types and sensors are available. In addition, the components support provenance and checkpointing to facilitate reprocessing and algorithm exploration. The algorithms, based on legacy processing codes, have been adapted to assume a common reference track approach for all images acquired from nearby orbits, simplifying and systematizing the geometry for time-series analysis. The framework is designed to easily allow user contributions, and is distributed for free use by researchers. ISCE can process data from the ALOS, ERS, EnviSAT, Cosmo-SkyMed, RadarSAT-1, RadarSAT-2, and TerraSAR-X platforms, starting from Level-0 or Level 1 as provided from the data source, and going as far as Level 3 geocoded deformation products. With its flexible design, it can be extended with raw/meta data parsers to enable it to work with radar data from other platforms
geodetic imaging↗