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Title: Exploration Seismology with Sparsity Constraints using Adaptive Dictionaries
Committee:
Dr. McClellan, Advisor
Dr. AlRegib, Chair
Dr. Peng
Abstract: The objective of the proposed research is to improve the quality of seismic signals and the efficiency of seismic inversion algorithms so as to deliver high-fidelity Earth models that can be used for oil and gas reservoir characterization. This dissertation presents a new reconstruction method to mitigate noise and interpolate missing traces in the acquired seismic dataset, as well as a new framework for full waveform inversion to estimate subsurface models more accurately and efficiently. Both contributions involve sparse approximation of various types of data over adaptive dictionaries that are learned by efficient strategies. The reduced dimensionality lowers computational cost and provides better performance compared to other state-of-the-art methods.
