CLEAN beamforming for the enhanced detection of multiple infrasonic sources

Abstract

The detection and characterization of signals of interest in the presence of (in)coherent ambient noise is central to the analysis of infrasound array data. Microbaroms have an extended source region and a dynamical character. From the perspective of an infrasound array, these coherent noise sources appear as interfering signals which conventional beamform methods may not correctly resolve. This limits the ability of an infrasound array to dissect the incoming wavefield into individual components. In this paper, this problem will be addressed by proposing a high-resolution beamform technique in combination with the CLEAN algorithm. CLEAN iteratively selects the maximum of the f/k spectrum (i.e., following the Bartlett or Capon method) and removes a percentage of the corresponding signal from the cross-spectral density matrix. In this procedure, the array response is deconvolved from the f/k spectral density function. The spectral peaks are retained in a ’clean’ spectrum. A data-driven stopping criterion for CLEAN is proposed that relies on the framework of Fisher statistics. This allows the construction of an automated algorithm that continuously extracts coherent energy until the point is reached that only incoherent noise is left in the data. CLEAN is tested on a synthetic data-set and is applied to data from multiple IMS infrasound arrays. The results show that the proposed method allows for the identification of multiple microbarom source regions in the Northern Atlantic, that would have remained unidentified if conventional methods had been applied.