Florian Aulanier

Analyzing sound speed fluctuations in shallow water from group-velocity versus phase-velocity data representation

Data collected over more than eight consecutive hours between two source-receiver arrays in a shallow water environment are analyzed through the physics of the waveguide invariant. In particular, the use of vertical arrays on both the source and receiver sides provides source and receiver angles in addition to travel-times associated with a set of eigenray paths in the waveguide. From the travel-times and the source-receiver angles, the eigenrays are projected into a group-velocity versus phase-velocity (Vg-Vp) plot for each acquisition. The time evolution of the Vg-Vp representation over the 8.5-h long experiment is discussed. Group speed fluctuations observed for a set of eigenrays with turning points at different depths in the water column are compared to the Brunt-Väisälä frequency.

Time-angle sensitivity kernels for sound-speed perturbations in a shallow ocean.

Acoustic waves traveling in a shallow-water waveguide produce a set of multiple paths that can be characterized as a geometric approximation by their travel time (TT), direction of arrival (DOA), and direction of departure (DOD). This study introduces the use of the DOA and DOD as additional observables that can be combined to the classical TT to track sound-speed perturbations in an oceanic waveguide. To model the TT, DOA, and DOD variations induced by sound-speed perturbations, the three following steps are used: (1) In the first-order Born approximation, the Fréchet kernel provides a linear link between the signal fluctuations and the sound-speed perturbations; (2) a double-beamforming algorithm is used to transform the signal fluctuations received on two source-receiver arrays in the time, receiver-depth, and source-depth domain into the eigenray equivalent measured in the time, reception-angle and launch angle domain; and finally (3) the TT, DOA, and DOD variations are extracted from the double-beamformed signal variations through a first-order Taylor development. As a result, time-angle sensitivity kernels are defined and used to build a linear relationship between the observable variations and the sound-speed perturbations. This approach is validated with parabolic-equation simulations in a shallow-water ocean context.

Mapping probability of shipping sound exposure level

Mapping vessel noise is emerging as one method of identifying areas where sound exposure due to shipping noise could have negative impacts on aquatic ecosystems. The probability distribution function (pdf) of sound exposure levels (SEL) is an important metric for identifying areas of concern. In this paper a probabilistic shipping SEL modeling method is described to obtain the pdf of SEL using the sonar equation and statistical relations linking the pdfs of ship traffic density, source levels, and transmission losses to their products and sums.

Shallow water acoustic tomography from angle measurements instead of travel-time measurements

For shallow-water waveguides and mid-frequency broadband acoustic signals, ocean acoustic tomography (OAT) is based on the multi-path aspect of wave propagation. Using arrays in emission and reception and advanced array processing, every acoustic arrival can be isolated and matched to an eigenray that is defined not only by its travel time but also by its launch and reception angles. Classically, OAT uses travel-time variations to retrieve sound-speed perturbations; this assumes very accurate source-to-receiver clock synchronization. This letter uses numerical simulations to demonstrate that launch-and-reception-angle tomography gives similar results to travel-time tomography without the same requirement for high-precision synchronization.

Groundtruthed probabilistic shipping noise modeling and mapping: Application to blue whale habitat in the Gulf of St. Lawrence

The Estuary and the Gulf of St. Lawrence constitute a large marginal sea of Northwest Atlantic of ∼260,000 km2, which is crossed by the main shipping route to the Great Lakes and where ∼125 ships are cruising every time. This inland sea is also part of the habitat of the endangered Northwest Atlantic blue whale population, which frequent these waters throughout the year as indicated by a recent passive acoustic study. The present study aims at estimating the degree of blue whale exposure to shipping noise, in space and time, and the risks of auditory damages, behavioral responses and communication masking in this region. First, shipping noise radiated from the AIS-monitored traffic is modeled, mapped with a high time-space resolution, and validated using in situ measurements from a dedicated ANSI-compliant acoustic observatory along the seaway. The latter is also used to estimate the source levels of the ships. Shipping noise statistics over the region are then computed and risk metrics of blue whale expo...

Perspective of tomography inversion using direction-of-arrival and direction-of-departure

In the ocean, local sound speed variations induce acoustic path changes. Travel-time (TT) variations of acoustic paths are classically used to perform ocean tomography inversion. Initially introduced to cope with multi-arrival interferences and to separate eigenray paths, source-receiver arrays combined with array processing techniques now give access to new observables that could be used for tomography such as direction-of-arrivals (DOAs) and direction of departure (DOD). The cumulative use of TT, DOA, and DOD in the inversion process first requires to study the forward problem which links sound speed variations to these observables measured through array processing from two source-receiver arrays. The so-called sensitivity kernels are established using (1) the first order Born approximation that relates the sound speed variation to the amplitude and phase change of the perturbed received signal and (2) a first order Taylor development which links the received signal perturbations to the relevant observables. In the present work, theoretical TT, DOA, and DOD sensitivity kernels are compared with parabolic equation simulations and tank experiment estimations.

Soundscape cube: A holistic approach to explore and compare acoustic environments

Soundscape patterns result from sounds radiated by several sources governed by diverse processes acting at different scales. Acoustic measurements are sampling this multi-scale variability pattern at particular locations and times. To facilitate soundscape analysis, the identification and separation of the different contributors, and soundscape comparisons, an approach, called soundscape cube, is introduced. For any acoustic measurement time-series, a probability of occurrence is estimated for all time-frequency samples of sound pressure levels (SPL) from the cumulative density functions (cdfs) of the sound spectra computed for consecutive time-windows. These spectral cdfs are then stacked along the time axis to generate a 3D block that piles up the time-frequency surfaces of the spectral SPL quantiles. This soundscape cube can then be explored by various mathematical operators to characterize and separate intra-soundscape SPL patterns emerging across the cube. The soundscape cube can also be split into i...

Time-angle ocean acoustic tomography using sensitivity kernels: Numerical and experimental inversion results

In shallow water acoustic tomography, broadband mid-frequency acoustic waves (1 to 5 kHz) follow multiple ray-like paths to travel through the ocean. Travel-time (TT) variations associated to these raypaths are classically used to estimate sound speed perturbations of the water column using the ray theory. In this shallow water environment, source and receiver arrays, combined with adapted array processing, provide the measurement of directions-of-arrival (DOA) and directions-of-departure (DOD) of each acoustic path as new additional observables to perform ocean acoustic tomography. To this aim, the double-beamforming technique is used to extract the TT, DOA and DOD variations from the array-to-array acoustic records. Besides, based on the first order Born approximation, we introduce the time-angle sensitivity kernels to link sound speed perturbations to the three observable variations. This forward problem is then inverted with the maximum a posteriori method using both the extracted-observable variations and the proposed sensitivity kernels. Inversion results obtained on numerical data, simulated with a parabolic equation code, are presented. The inversion algorithm is performed with the three observables separately, namely TT, DOA and DOD. The three observables are then used jointly in the inversion process. The results are discussed in the context on ocean acoustic tomography.