We analyze the properties of Wasserstein metric, a new mis-fit function for full waveform inversion (FWI) and prove such preperties as convexity in different aspects. Considering the observed data and predicted data as two density functions, the quadratic Wasserstein metric corresponds to the optimal cost or rearranging one function into the other with a cost fuction that id quadratic in distance. In other words, we match the observed data and the predicted data by the optimal map which takes the information geometry of the data sets into consideration. The inversion follows the normal scheme of FWI as a PDE-constrained optimization. The velocity model can be updated using a gradient-based optimization with the new adjoint source.
Presentation Date: Monday, October 17, 2016
Start Time: 1:00:00 PM
Location: 141
Presentation Type: ORAL
