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compressed_representations_for_subsurface_imaging [2016/09/01 19:15] (current)
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 +Accurate subsurface imaging is critical for appraisal and development of several hydrological,​ environmental,​ and energy resources. These subsurface systems are complex, heterogeneous,​ and inherently uncertain due to costly and inconvenient sampling. In addition, simulations of fluid flow and transport in these systems typically involves models with strong nonlinearity,​ high dimensionality,​ and considerable computational complexity. However, geologic formations often exhibit large-scale spatial continuity and correlations that make them amenable to low-dimension or compressed representations,​ a property that we exploit in estimating them from nonlinear data. In this talk, I will review several popular image compression techniques for sparsifying subsurface property maps and show how these compact descriptions transform the imaging problem into sparse reconstruction from limited nonlinear flow measurements. I will discuss the solution of the resulting nonlinear imaging problems using guidelines provided by recent developments in sparse signal processing under linear measurements,​ e.g. compressed sensing.
 +Biography ​
 +Behnam Jafarpour is an assistant professor in the Viterbi School of Engineering at USC. He obtained an S.M. in Electrical Engineering and Computer Science, and a Ph.D. in Environmental Engineering both from Massachusetts Institute of Technology in February 2008. Before joining USC, he was an assistant professor at Texas A&M University from October 2007 to August 2011. His current research interests focus on the application of signal processing, estimation and control to characterization,​ modeling, and management of energy resources and environmental systems.
compressed_representations_for_subsurface_imaging.txt ยท Last modified: 2016/09/01 19:15 (external edit)