Lippmann-Schwinger-Lanczos algorithm for inverse scattering problems with unknown reflectivity and loss distributions: One-dimensional Case

TL;DR

This paper introduces an advanced Lippmann-Schwinger-Lanczos algorithm for one-dimensional inverse scattering in media with unknown reflectivity and loss, improving internal solution reconstruction accuracy and robustness over traditional methods.

Contribution

The work extends the Lippmann-Schwinger-Lanczos method to dissipative media, linking data-driven reduced-order models with port-Hamiltonian systems for better inverse problem solutions.

Findings

More accurate internal reconstructions than Born approximation.

Faster and more robust contrast recovery demonstrated.

Two methods for constructing internal solutions from spectral and frequency data.

Abstract

We consider one-dimensional inverse scattering in attenuating media where both the reflectivity and loss distributions are unknown. Mathematically, this corresponds to recovering the coefficients of a damped wave operator, or equivalently, a quadratic operator pencil in the frequency domain. The Lippmann-Schwinger equation maps the unknown reflectivity and loss distribution to the measured scattered data. This mapping is nonlinear, as it requires knowledge of the internal wavefield, which itself depends on the reflectivity and loss distribution. The Lippmann-Schwinger-Lanczos method addresses this nonlinearity by approximating the internal solutions through the lifting of states from a reduced-order model constructed directly from the measured data. In this work, we extend the method to dissipative problems, enabling the approximation of internal partial differential equation (PDE) solutions in media with both reflectivity and loss distributions. We present two complementary constructions of such internal solutions: one based on spectral data and another on frequency-domain measurements over a finite interval. This development establishes a direct link between data-driven reduced-order models for inverse problems and port-Hamiltonian dynamical systems, with reduced models obtained either from the associated spectral measure or via rational approximation. Compared to the Born approximation, which replaces the internal field with the background field, our approach yields more accurate internal reconstructions and enables faster and more robust recovery of the contrast as evidenced by our numerical experiments.