Abstract
Model calibration is viewed in the sense of adapting the full set of model parameters in order to get better resemblance between observations and major end-predictions. In this paper, we present a new probabilistic method to calibrate normal-mode-based propagation models using some observed data and sources of uncertainties. The unknown parameters are estimated using a multiple parallel Markov Chain Monte-Carlo method, for which convergence diagnostics are available. Using a few normal modes allows to rapidly estimate the statistical distributions of the arrival characteristics, on a mode-by-mode basis. In a sense, the unknown inputs "propagate" through the plausible waveguides with each mode and alters its amplitude and phase structure. The resulting waveform is obtained as a combination of individual wavepackets which depends continuously of the input parameters. Further, once the maximum likelihood has been identified, the reduced model can be extended to higher dimensions (with a larger number of modes) to better refine the calibration process. Numerical results are obtained using the FLOWS platform (Fast Low Order Wave Simulation), that integrate advanced spectral numerical methods and realistic representations of atmospheric disturbances. The method is used to revisit the infrasound signals recorded at I37NO during campaigns of ammunition destruction explosions at Hukkakero.
Published Version
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