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Reliable methods for imaging the subsurface are crucial in solving geotechnical problems. Imaging reduces the economic risk of drilling for natural resources (oil and gas, and geothermal energy) and is essential for environmental site characterization and verification of remediation practices. Geophysical imaging provides information regarding the subsurface, where borehole sampling is restricted and where information on the state of the subsurface cannot be obtained otherwise. It produces a map of the geophysical attributes (electrical conductivity, density, and seismic velocity), which the geophysical measurements sense on or above the Earth’s surface, on or above the ocean seafloor, and in isolated boreholes.
Both deterministic and stochastic imaging techniques have been successfully applied to a wide variety of geotechnical problems. We have expertise in Monte-Carlo Markov-Chain stochastic imaging methods and large-scale 3D imaging practices using gradient and Newton-based optimization schemes. Our imaging algorithms also exploit large parallel computing systems—utilizing tens of thousands of processors (necessary for realistic 3D imaging)—and effectively deal with imaging and data volumes of industrial size. Subsurface imaging research is now focused on combining multiple types of geophysical data sets to better quantify the subsurface and reduce ambiguity. The degree to which joint images of geophysical attributes can be used successfully to infer other rock properties (fracture orientation, fracture density, temperature and fluid saturations) is also an active area of research. Such research depends on how uniquely these parameters are related to the geophysical attributes and requires incorporation of rock physics and geomechanical models, which are also core capabilities in the Geophysics Department.