What question did this study set out to answer?

The aim is to develop and showcase a machine learning tool, MIDWAZE, for extracting velocity information from PDV data.

January 27, 2026Journal of Dynamic Behavior of Materials0 citationsOpen Access

Velocity Extraction Using Complete Time-Domain Waveform Data and Audio Machine Learning

ASA. SusserLos Alamos National Laboratory AAA. AlbertLos Alamos National Laboratory JDJ. DanielsonLos Alamos National Laboratory

Key Points

The aim is to develop and showcase a machine learning tool, MIDWAZE, for extracting velocity information from PDV data.
Developed MIDWAZE for waveform-based velocity extraction.
Performed comparative analysis with traditional spectrogram methods.
Tested accuracy and speed of velocity extraction against human analysts.
MIDWAZE extracts velocities approximately 350 times faster than human analysts.
Achieves near-human accuracy in velocity extraction.
Demonstrates initial ability to discriminate between different solid objects.

Abstract

Abstract We developed a new machine learning-based tool for extracting information from interferometry measurements: MIDWAZE (Modular Interferometry Direct Waveform AnalyZEr). This paper showcases MIDWAZE’s ability to extract an object’s velocity information from Photonic Doppler Velocimetry (PDV) data at near-human accuracy with little to no human intervention. MIDWAZE can extract velocities roughly 350 times as fast as a human analyst "rushing" to complete their extractions, with similar extraction accuracy. MIDWAZE’s most outstanding feature is that it operates directly in waveform/temporal space, freeing analysis from certain limitations imposed by traditional spectrogram-based approaches and opening the way to "phase aware" PDV analysis. MIDWAZE also has limited ability to discriminate between different solid objects, which we develop as a first step towards automated discrimination of different kinds of objects such as ejecta clouds.

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