The Radon Transform-Based Sampling Methods for Biharmonic Sources from the Scattered Fields

TL;DR

This paper introduces three Radon transform-based sampling methods for reconstructing biharmonic sources from scattered field data, improving accuracy and efficiency in imaging source support and functions.

Contribution

The paper develops novel indicator functions that utilize scattered fields for source reconstruction, including a simplified formula and boundary support recovery, with theoretical uniqueness results.

Findings

High-resolution imaging of source support achieved

The second indicator function outperforms the first in speed and accuracy

Uniqueness of source boundary reconstruction established for certain shapes

Abstract

This paper presents three quantitative sampling methods for reconstructing extended sources of the biharmonic wave equation using scattered field data. The first method employs an indicator function that solely relies on scattered fields $ u^s$ measured on a single circle, eliminating the need for Laplacian or derivative data. Its theoretical foundation lies in an explicit formula for the source function, which also serves as a constructive proof of uniqueness. To improve computational efficiency, we introduce a simplified double integral formula for the source function, at the cost of requiring additional measurements $\Delta u^s$. This advancement motivates the second indicator function, which outperforms the first method in both computational speed and reconstruction accuracy. The third indicator function is proposed to reconstruct the support boundary of extended sources from the scattered fields $ u^s$ at a finite number of sensors. By analyzing singularities induced by the source boundary, we establish the uniqueness of annulus and polygon-shaped sources. A key characteristic of the first and third indicator functions is their link between scattered fields and the Radon transform of the source function. Numerical experiments demonstrate that the proposed sampling methods achieve high-resolution imaging of the source support or the source function itself.