Parameterizations for Ensemble Kalman Inversion
It addresses the challenge of parameterization in derivative-free ensemble inversion methods for applied inverse problems, offering practical improvements but is incremental.
The paper demonstrates how geometric and hierarchical parameterizations improve ensemble Kalman inversion for inverse problems in electrical impedance tomography, groundwater flow, and source inversion, enabling reconstruction of piecewise continuous fields and learning of key parameters like length-scales.
The use of ensemble methods to solve inverse problems is attractive because it is a derivative-free methodology which is also well-adapted to parallelization. In its basic iterative form the method produces an ensemble of solutions which lie in the linear span of the initial ensemble. Choice of the parameterization of the unknown field is thus a key component of the success of the method. We demonstrate how both geometric ideas and hierarchical ideas can be used to design effective parameterizations for a number of applied inverse problems arising in electrical impedance tomography, groundwater flow and source inversion. In particular we show how geometric ideas, including the level set method, can be used to reconstruct piecewise continuous fields, and we show how hierarchical methods can be used to learn key parameters in continuous fields, such as length-scales, resulting in improved reconstructions. Geometric and hierarchical ideas are combined in the level set method to find piecewise constant reconstructions with interfaces of unknown topology.