Abstract

In the Acoustic Thermometry of Ocean Climate (ATOC) program’s Acoustic Engineering Test (AET), broadband 75-Hz center frequency transmissions were recorded on a 700-m-long vertical array, 3252 km distant from a midwater source suspended from R/P FLIP. The transmissions occurred over a 6-day period. Previously reported results from the AET using 12.7-min averaged data by Colosi et al. [J. Acoust. Soc. Am. 105(6), 3202–3218 (1999)], hereafter referred to as Colosi99) revealed surprisingly weak acoustic scattering for early arriving identifiable wavefronts. Colosi99 found pulse time spreads on the order of 0–5 ms and the probability density function (PDF) of peak intensity was close to log normal. In this paper these results are confirmed using 1.8-min averaged data. It is also shown that scintillation index (SI) is a strong function of position along the pulse with the smallest values occurring at the peak and larger values occurring at the tails. Intensity PDFs of identifiable wavefronts are reanalyzed in terms of both peak intensity and integrated pulse energy (IE) where the integration is over ±50 ms from the wavefront peaks. While SI for the IE are somewhat smaller than for the peak intensity, the PDFs are both very closely log normal. Regarding multipathing along the wavefronts, it is found that on average there are 1.7 peaks per wavefront segment per hydrophone and the intensity PDF of all multipath peaks is log normal. The combined observation of weak scattering and multipathing is a novel result. A reanalysis of the scintillations in the AET transmission finale where no wavefronts are evident is presented. Colosi99 analyzed the finale in terms of peak scintillations and found a near log-normal intensity PDF. Reprocessing the full field without limiting data to intensity peaks and accounting for mean intensity nonstationarity yields an intensity PDF which is much closer to the exponential distribution associated with full saturation; these results show that the finale region can be expected to behave much like Gaussian random noise.

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