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Research and Development of High-performance Explosives

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Published on: February 20, 2016

Uncertainty analysis for infrasound waveform inversion: Application to explosion yield estimation.

Keehoon Kim¹, Arthur Rodgers¹, Melissa Wright²

¹Geophysical Monitoring Program, Lawrence Livermore National Laboratory, Livermore, California 94550, USA.

The Journal of the Acoustical Society of America

|January 3, 2019

Summary

This summary is machine-generated.

This study introduces a probabilistic method for acoustic waveform inversion to quantify uncertainty in explosion yield estimates. Local infrasound analysis reveals that source model uncertainty significantly impacts yield accuracy.

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Area of Science:

Geophysical acoustics
Seismology
Infrasound analysis

Background:

Acoustic waveform inversion is crucial for geophysical source parameter estimation.
Current methods often lack rigorous uncertainty analysis, limiting result reliability.

Purpose of the Study:

To develop a probabilistic framework for acoustic waveform inversion.
To evaluate inversion uncertainty using ground-truth data and assess its impact on explosion yield estimation.

Main Methods:

Probabilistic representation of acoustic waveform inversion using a priori waveform misfit covariance and source model variance.
Application to local explosion infrasound data for yield and uncertainty estimation.
Quantification of yield uncertainty by combining waveform inversion and source model uncertainties.

Main Results:

Estimated explosion yields showed good agreement with true values (under 25% error).
Local infrasound inversion (within 10 km) yielded uncertainty comparable to 10% acoustic source model prediction errors.
Source model uncertainty was identified as a critical factor in accurate yield estimation.

Conclusions:

Probabilistic acoustic waveform inversion provides essential uncertainty quantification.
Local infrasound is a valuable tool for understanding and constraining source uncertainty in geophysical source analysis.
Accurate explosion yield estimation necessitates considering both inversion and source model uncertainties.