Acoustic waveform inversion and uncertainty analysis for explosion yield estimation

Abstract

Waveform inversion techniques are widely used to extract source information from acoustic signals. The methods exploit full-waveform information and provide further constraints on source inversion than other parameter-based inversions. Infrasound waveform inversion was recently applied to chemical explosions and showed promising results for the determination of explosion yield. The method can improve the accuracy of the estimated yield by using full 3-D numerical Green's functions and incorporating the propagation path effects into the inversion. However, the inversion results are often given without any uncertainty estimation, which is critical for a post-detonation forensic analysis. In this study, we evaluate the yield estimation errors associated with the uncertainty in the atmospheric representation. An ensemble of atmospheric realizations is obtained by adding random turbulence to an atmospheric model and the uncertainty of inversion solution depending on atmospheric variation is quantified. In addition, different atmospheric models derived from local weather data or numerical weather prediction models are validated for yield estimation by using a series of chemical explosion data.