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Updated: Jul 6, 2026

Scattering And Absorption of Light in Planetary Regoliths

Scattering And Absorption of Light in Planetary Regoliths

Published on: July 1, 2019

Mie light-scattering granulometer with adaptive numerical filtering. I. Theory.

L Hespel¹, A Delfour

¹Département d'Optique Théorique et Appliquée, Office National d'Etudes et de Recherches Aérospatiales, 2 avenue E Belin, BP 4055, 31055 Toulouse, France. laurent.hespel@onecert.fr

|March 21, 2008

Summary

A new least-squares method accurately determines particle-size distributions from light-scattering data. This reliable technique works even with noisy measurements, offering robust solutions for aerosols and fogs.

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Area of Science:

Optics and Photonics
Atmospheric Science
Chemical Engineering

Background:

Accurate particle-size distribution is crucial for understanding aerosols, fogs, and combustion plumes.
Traditional methods can be limited by noise and complexity of scattering media.
Light-scattering techniques offer a non-invasive approach to particle characterization.

Purpose of the Study:

To present a novel search procedure for determining particle-size distributions.
To evaluate the method's reliability and performance using simulated data.
To enable accurate particle characterization in diverse scattering media.

Main Methods:

A least-squares method with numerical filtering and regularization was developed.
A nephelometer was integrated for angular static light-scattering measurements.

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Mie theory was used to match experimental data with theoretical patterns for distribution retrieval.

Main Results:

The developed method demonstrated reliable retrieval of particle-size distribution functions.
The procedure proved effective even when subjected to high levels of measurement noise.
Simulated data analysis confirmed the robustness of the inverse procedure.

Conclusions:

The presented least-squares method offers a reliable approach for particle-size distribution determination.
The technique is applicable to various scattering media, including aerosols and fogs.
The regularization scheme enhances the method's performance under noisy conditions.