Abstract
A standardized, self-similar, multiresolution algorithm is developed for scaling infrasonic signal time, frequency, and power within the framework of fractional octave bands. This work extends accepted fractional octave band schemas to 0.001 Hz (1000 s periods) to facilitate the analysis of broadband signals as well as the deep acoustic-gravity and Lamb waves captured by the global infrasound network. The Infrasonic Energy, Nth Octave (INFERNO) multiresolutionEnergy Estimator is applied to computing the total acoustic energy of the Russian meteor signature recorded in the 45mHz-9 Hz frequency band by IMS array 131KZ, Kazakhstan.
Highlights
Infrasound is used to characterize manifold natural and anthropogenic sources, from tsunamigenesis in the mHz frequency range to wind turbines near the audio range
A standardized, self-similar, multiresolution algorithm is developed for scaling infrasonic signal time, frequency, and power within the framework of fractional octave bands
This work extends accepted fractional octave band schemas to 0.001 Hz (1000 s periods) to facilitate the analysis of broadband signals as well as the deep acoustic-gravity and Lamb waves captured by the global infrasound network
Summary
Infrasound is used to characterize manifold natural and anthropogenic sources, from tsunamigenesis in the mHz frequency range to wind turbines near the audio range. The spatial distribution of infrasonic sensing systems may vary from meters to kilometers Due to these diverse spatial, temporal, spectral, and intensity scales, it can be challenging to process infrasonic signals using consistent, reproducible parameters. This paper is an invitation to standardize infrasound metrics using historical and ongoing efforts by diverse communities as a compass, and an entreaty to extend familiar algorithms and accepted standards into a slightly different, more transportable framework. It proposes a scaling of time, frequency and amplitude that may permit comparative calibrations, data quality assessments, and tests on sensing systems, as well as the benchmarking and validation of propagation models, detection algorithms, and classification taxonomies. This paper should provide sufficient information to permit the computational implementation of the proposed methodology and algorithms for further evaluation and improvement
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