Computationally robust and noise resistant numerical detector for the detection of atmospheric infrasound.

This work reports on a performance study of two numerical detectors that are particularly useful for infrasound arrays operating under windy conditions. The sum of squares of variance ratios (SSVR1)-proposed for detecting signals with frequency ranging from 1 to 10 Hz-is computed by taking the ratio of the squared sum of eigenvalues to the square of largest eigenvalue of the covariance matrix of the power spectrum. For signals with lower frequency between 0.015 and 0.1 Hz, SSVR2 is developed to reduce the detector's sensitivity to noise. The detectors' performances are graphically compared against the current method, the mean of cross correlation maxima (MCCM), using the receiver operating characteristics curves and three types of atmospheric infrasound, corrupted by Gaussian and Pink noise. The MCCM and SSVR2 detectors were also used to detect microbaroms from the 24 h-long infrasound data. It was found that the two detectors outperform the MCCM detector in both sensitivity and computational efficiency. For mine blasts corrupted by Pink noise (signal-to-noise ratio = -7 dB), the MCCM and SSVR1 detectors yield 62 and 88 % true positives when accepting 20% false positives. For an eight-sensor array, the speed gain is approximately eleven-fold for a 50 s long signal.