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Related Experiment Video

Updated: Nov 19, 2025

Three-dimensional Optical-resolution Photoacoustic Microscopy

Three-dimensional Optical-resolution Photoacoustic Microscopy

Published on: May 3, 2011

Acoustic imaging using unknown random sources.

Aaron C Prunty¹, Roel K Snieder¹, Christoph Sens-Schönfelder²

¹Department of Geophysics, Colorado School of Mines, Golden, Colorado 80401, USA.

The Journal of the Acoustical Society of America

|January 30, 2021

Summary

This summary is machine-generated.

Imaging localized velocity contrasts is feasible using random acoustic sources. This method effectively images targets from multiply scattered coda waves, even with unknown illumination.

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Area of Science:

Acoustic imaging
Wave propagation
Inverse problems

Background:

Conventional acoustic imaging relies on controlled sources.
Imaging velocity contrasts in complex media presents challenges.
Understanding wave scattering is crucial for subsurface characterization.

Purpose of the Study:

To investigate the feasibility of imaging velocity contrasts using volume-distributed random acoustic sources.
To develop and validate a processing workflow for random-source acoustic imaging.
To demonstrate the capability of imaging targets from multiply scattered coda waves.

Main Methods:

Utilizing volume-distributed random point sources for acoustic illumination.
Employing a two-step processing flow based on the linear sampling method.
Conducting Monte Carlo simulations for generating random source realizations.
Analyzing multiply scattered coda waves in recorded data.

Main Results:

Successful imaging of localized velocity contrasts was achieved using random sources.
The proposed two-step method accurately inverted for target locations.
Images obtained from random-source experiments were comparable to controlled-source methods.
Multiply scattered coda waves proved effective for imaging despite random illumination.

Conclusions:

Volume-distributed random acoustic sources are feasible for imaging velocity contrasts.
The linear sampling method, applied to random-source data, provides accurate subsurface imaging.
This approach offers a novel alternative for acoustic imaging in non-ideal conditions.