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Related Experiment Video

Updated: Aug 30, 2025

Evanescent Field Based Photoacoustics: Optical Property Evaluation at Surfaces

Evanescent Field Based Photoacoustics: Optical Property Evaluation at Surfaces

Published on: July 26, 2016

Surface shape reconstruction from phaseless scattered acoustic data using a random forest algorithm.

Michael-David Johnson¹, Anton Krynkin¹, Giulio Dolcetti²

¹Department of Mechanical Engineering, University of Sheffield, Sheffield, United Kingdom.

The Journal of the Acoustical Society of America

|September 1, 2022

Summary

This summary is machine-generated.

This study introduces a robust data-driven method using random forest regression for acoustic surface reconstruction. It accurately estimates surface parameters from scattered sound data, even with significant noise.

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Area of Science:

Acoustics
Surface Metrology
Machine Learning

Background:

Acoustic waves can reconstruct rigid scattering surface roughness.
Analytical models (e.g., Kirchhoff integral) estimate surface properties but struggle with noise.
Robustness of surface reconstruction algorithms needs improvement.

Purpose of the Study:

To enhance the robustness of surface reconstruction algorithms.
To apply a data-driven approach using random forest regression.
To reconstruct parameters of 1D sinusoidal surfaces from acoustic data.

Main Methods:

Utilized airborne acoustic phase-removed pressure data.
Employed a random forest regression algorithm for surface parameter estimation.
Generated synthetic training data via Kirchhoff integral and validated with lab measurements.

Main Results:

Accurate recovery of surface parameters was achieved.
Successful reconstruction across various receiver configurations.
Robust performance demonstrated with noise levels from 0.1% to 30%.

Conclusions:

The data-driven random forest approach significantly improves surface reconstruction robustness.
This method offers accurate and reliable estimation of surface parameters in noisy conditions.
Validated effectiveness for 1D sinusoidal surfaces using both synthetic and experimental data.