Extended and Unscented Kitchen Sinks

Extended and Unscented Kitchen Sinks

Example results of the extended kitchen sinks (EKS) algorithm on an interpreted seismic inversion problem, where we wish to infer the below ground structure of the Earth from sound wave reflection times. The inferred rock-type layer boundaries (left) and seismic velocities (right) are shown in blue, indicating the predictive means and standard deviation envelopes. Draws from the MCMC inversion are overlaid in dotted black.

In this work we extended our Bayesian nonparametric algorithms for inverse problems, the unscented and extended Gaussian processes, to work with multiple outputs and over large datasets. The new algorithms are called unscented and extended kitchen sinks (EKS and UKS) since they use the random kitchen sink (or basis function) approximation for scaling kernel machines. This approximation allows us to straightforwardly enable the EKS and UKS to work in multiple output scenarios as well, enabling these algorithms to be useful for a wide variety of complex nonlinear inversion problems, such as geophysical inversions.