Scatteringbased

Scattering-based refers to methods, models, and analyses that rely on the scattering of waves or particles to probe, characterize, or reconstruct the properties of a medium or object. In practice, scattering-based approaches study how incident waves—such as light, sound, X-rays, neutrons, or electrons—interact with inhomogeneities and then use the scattered signal to infer structure, composition, or dynamics. The central premise is to connect measured scattered fields or intensities to the underlying properties through forward models derived from scattering theory, including Maxwell's equations, the wave equation, or the radiative transfer equation.

These methods are widely used in imaging, materials science, and geophysics. Examples include diffuse optical tomography in turbid media, small-angle X-ray and neutron scattering to probe nanoscale structure, and scattering-based tomography in ultrasound or seismic imaging. They are particularly valuable when direct transmission is challenging or when internal contrast manifests mainly in scattered signals.

Challenges include the inherent ill-posedness of inverse scattering problems, sensitivity to noise, and the need for accurate forward models and calibration. Computational approaches often combine physical priors with regularization, iterative reconstruction, Bayesian inference, or data-driven techniques.

In related areas of signal processing, scattering-based concepts appear in the scattering transform, a representation built from cascade wavelet operators that yields stable, deformation-tolerant features. The scattering-based paradigm thus spans experimental science, imaging, and data analysis, emphasizing information extracted from scattered responses rather than direct measurements.