Untangling the nonlinearity in inverse scattering with data-driven reduced order models

• Published in: Inverse problems 2018-06, Vol.34 (6), p.65008
• Main Authors: Borcea, Liliana, Druskin, Vladimir, Mamonov, Alexander V, Zaslavsky, Mikhail
• Format: Article
• Language: English
• Subjects: Born approximation; inverse scattering; model reduction; rational Krylov subspace projection
• ISSN: 0266-5611; 1361-6420
• DOI: 10.1088/1361-6420/aabb16

The motivation of this work is an inverse problem for the acoustic wave equation, where an array of sensors probes an unknown medium with pulses and measures the scattered waves. The goal of the inversion is to determine from these measurements the structure of the scattering medium, modeled by a spatially varying acoustic impedance function. Many inversion algorithms assume that the mapping from the unknown impedance to the scattered waves is approximately linear. The linearization, known as the Born approximation, is not accurate in strongly scattering media, where the waves undergo multiple reflections before they reach the sensors in the array. Thus, the reconstructions of the impedance have numerous artifacts. The main result of the paper is a novel, linear-algebraic algorithm that uses a reduced order model (ROM) to map the data to those corresponding to the single scattering (Born) model. The ROM construction is based only on the measurements at the sensors in the array. The ROM is a proxy for the wave propagator operator, that propagates the wave in the unknown medium over the duration of the time sampling interval. The output of the algorithm can be input into any off-the-shelf inversion software that incorporates state of the art linear inversion algorithms to reconstruct the unknown acoustic impedance.