The Nonlinear Differential Semblance Algorithm for Plane Waves in Layered Media
This work was also published as a Rice University thesis/dissertation.
This paper proposes an alternative approach to the output least-squares (OLS) seismic inversion for layered-media. The latter cannot guarantee a reliable solution for either synthetic or field data, because of the existence of many spurious local minima of the objective function for typical data, which lack low-frequency energy. To recover the low-frequency lacuna of typical data, I formulate waveform inversion into a differential semblance optimization (DSO) problem with artificial low-frequency data as control variables. To my knowledge, this approach is the first version of differential semblance with nonlinear modeling that may properly accounts for nonlinear effects of wave propagation, such as multiple reflections. Numerical experiments with synthetic data indicate the smoothness and convexity of the proposed objective function. These results suggest that gradient-related algorithms may successfully approximate a global minimizer from a crude initial guess for typical band-limited data.
Citable link to this pagehttps://hdl.handle.net/1911/102109
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