165 research outputs found
NURC/SACLANTCEN milestone experiments toward solving inverse problems in ocean acoustics
This paper reviews milestone experiments conducted by Saclant Undersea Research Centre to support the development and validation of techniques for obtaining and taking into account environmental information in sonar. The experiments brought together underwater acousticians, geophysicists and oceanographers with the aim to collect comprehensive acoustic and environmental ground-truth data. Environmental-adaptive signal processing was first demonstrated in a deep water area (WESTSARDINIA'89&90). From ducted propagation measurements, a model-based matched filter (MBMF) receiver that fully incorporates the physics of wave propagation determined a source range, depth and Doppler. For environmental inversion the broadband extension of matched-field processing was investigated in a shallow water area south of Elba island (YELLOWSHARK'94&95). Geoacoustic properties of the sea bottom were determined by finding the best fit between predicted and observed sound fields at multiple frequencies using genetic search algorithms. The same inversion results were obtained by MBMF processing of broadband linearly-frequency-modulated signals enabling the use of an array of a few hydrophones instead of a dense and large vertical array. This motivated the development of drifting acoustic buoys which were tested successfully over the southern continental shelf off Marettimo island, Sicily (ENVERSE'97&98). A recent experiment demonstrated an integrated concept of Rapid Environmental Assessment (MREA/BP'07) using sparse arrays of hydrophones and pressure/temperature sensors, hand-deployed from small vessels
A model-based acoustic time-reversal mirror for robust variable focusing
A natural extension of the acoustic time-reversal mirror (TRM) is investigated where predicted receive array signals are backpropagated to an arbitrary virtual source location. First, large time-bandwidth product (TB), broadband signals propagating from a real probe source to the array are processed iteratively by a model-based matched filter (MBMF) receiver in searching for the environmental parameters that best correspond to the measured acoustic-impulse responses. The process ends when most of the time-spread energy across the array is recombined coherently (processing gain). Second, the resulting acoustic-channel model is used to synthesize the transmit signals that will focus their energy at the desired range and depth (the virtual source). The procedure overcomes the noise and reverberation limitations inherent to the TRM operation. The at-sea, focusing performances of a model-based TRM were predicted from related MBMF experiments: range-depth localization of a distant towed source in a duct (west sardinia 89\N90) and bottom geoacoustic characterization in shallow water (yellow shark 94\N95) [Hermand et al., IEEE J. Oceanic Eng. 18, 447–465 (1993); 24, 41–66 (1999)]. Simulation results using yellow shark 94, large TB, broadband vertical array data will be compared with trm 96\N97 experimental results [Hodgkiss et al., J. Acoust. Soc. Am. 105, 1597–1604 (1999)] obtained along the same transect
Coastal seabed tomography by inversion of drifting acoustic buoys data
A buoy field, specifically designed for shallow-water acoustic tomography, was deployed in a complex coastal environment on the western Sicilian shelf (enverse 97, Saclant Undersea Research Centre). Eight buoys drifted away from a fixed sound source receiving its repeated, broadband transmissions on their single hydrophone at fixed depths and known positions (DGPS). The 1-day run sampled, over kilometers, the acoustic impulse response (the Green’s function) of the environment as a function of range and azimuth from the source. In this paper, the mapping of sediment properties and bottom types from the ‘‘synthetic horizontal aperture’’ measurements is investigated. Statistical analyses of the frequency-dependent, mode interference patterns associated with the buoy tracks allow to isolate range-azimuth sectors of limited acoustic variability and to define regions of similar bottom conditions. Average geoacoustic parameters of these regions are determined by maximizing the processing gain of a model-based matched filter receiver [J.-P. Hermand, IEEE J. Oceanic Eng. 24, 41–66 (1999)]. The preliminary inversion results, including P-wave speed and thickness of the sediment cover, are congruent with the ground truth of a dense grid of seismic reflection profiles and sediment cores (enverse 98)
On the usefulness of waterborne measurement of particle velocity in geoacoustic inversion
Recent advances in sensor design have led to the development of receiving systems whose elements are vector sensors, i.e., sensors that simultaneously measure the acoustic pressure and fluid motion due to the propagation of acoustic energy at the sensor location. As such, arrays of vector sensors can provide more information about the sound field than arrays made of traditional hydrophones, and thus are attractive for various applications including the inversion for environmental properties. The fundamental question addressed by this paper is: does the use of acoustic vector data versus pressure-only data improve the results of an inversion scheme based on waterborne observations and matched field processing? To our knowledge no experimental data are available yet to allow a detailed comparison of performance between standard (pressure-only) arrays and vector sensor arrays for environmental inversions. For a preliminary study we will simulate the inversion of vector sensor data in the South Elba environment for which pressure-only inversion results and ground truth data are available following the Yellow Shark'94 and Blue Planet'07 experiments. Broadband signals received on fully-populated and sparse arrays will be considered
A Monte Carlo experiment for measuring acoustic properties of macroalgae living tissue
A methodology is developed to measure ex situ ultrasonic velocity of submerged aquatic vegetation tissue, in particular, macroalgae, in a nondestructive and efficient manner. An entire thallus is submerged in artificial seawater-filled tank through which many ultrasonic pulse-echo measurements are recorded while thallus parts are randomly displaced. Average sound speed of tissue is estimated from normal fit to extracted travel times given measured total volume fraction of tissue and travel time in water alone. For species Ecklonia radiata the resulting values for sound speed 1573.4?±?4.8 m s-1 and adiabatic compressibility 3.134?×10-10?±?1.34?×10-11 Pa-1 at 18?°C agree with more laborious and destructive methods
Time- and space-varying interference patterns of broadband acoustic field sampled by drifting buoys during ENVERSE 97 experiments
During the winter of 1997 SACLANTCEN deployed a fixed controlled sound source, a vertical receive array, and drifting hydrophone buoys in a complex coastal environment on the western Sicilian shelf (ENVERSE 97). The acoustic impulse response of the medium was measured in a broad frequency band as a function of range and azimuth from the source, using repeated, large time-bandwidth-product FM transmissions and DGPS positioning. This paper investigates the combined effects of water column and bottom variability upon the space-frequency distribution of the sound-field intensity. The time and space dependence of extracted features such as patterns of field extrema are analyzed and related to the observed environmental conditions. In particular, perturbations of acoustic field invariants are detected and shown to be well correlated with the range-dependent bottom properties and the time-varying ocean sound speed and current fields. Preliminary modeling (C-SNAP) results obtained from concurrent oceanographic and geophysical ground-truth data (enverse 98) are compared to the acoustic measurements to determine their sensitivity to various environmental parameters
Acoustic interferometry for sediment geoacoustic characterization using broadband ship noise in the Yellow Shark environment
Characterization of sediment dynamics in an estuary environment using acoustic techniques
In recent years, acoustic-based methods have been developed to characterize the dynamical behavior of loose sediments and bed deposits in very shallow water environments. In this paper, we present preliminary results on the estimation of the dynamic changes in an estuarine environment using data from dual-frequency echosounding at high resolution and contemporaneous hydrological measurements including suspended matter concentration, density subbottom profiling, and data assimilation based on a sediment transport model. Those measurements are being conducted in the lower estuary of the Scheldt (Belgium) at the Sint Anna site where strong tide and season-dependent phenomena can be observed. This allows us to construct a ground-truthed, time-dependent geoacoustic model of the environment, i.e., a characterization of sound speed, density, and attenuation in function of time and depth. Synthetic acoustic data generated by that model will then be used to test inversion methods for monitoring sediment dynamics in real time
On the use of acoustic particle velocity fields in adjoint-based inversion
Following the recent interest in the use of combined pressure and particle motion sensors in underwater acoustics and signal processing, some general aspects regarding the modeling and multipath phenomenology of acoustic particle velocity fields in shallow water environments have been studied. In this paper we will address a number of issues associated with the incorporation of vector sensor data (pressure and particle velocity) into adjoint-based inversion schemes. Specifically, we will discuss the ability of a semi-automatic adjoint approach to compute the necessary gradient information without the need for an analytic model of the adjoint particle velocity field. Solutions to the forward propagation of acoustic pressure are computed using an implicit finite-difference parabolic equation solver while the particle velocity is calculated locally at each grid point. Some numerical examples of vector sensor inversion results are provided
Arbitrary-order Hilbert spectral analysis for time series possessing scaling statistics: a comparison study with detrended fluctuation analysis and wavelet leaders
In this paper we present an extended version of Hilbert-Huang transform,
namely arbitrary-order Hilbert spectral analysis, to characterize the
scale-invariant properties of a time series directly in an amplitude-frequency
space. We first show numerically that due to a nonlinear distortion,
traditional methods require high-order harmonic components to represent
nonlinear processes, except for the Hilbert-based method. This will lead to an
artificial energy flux from the low-frequency (large scale) to the
high-frequency (small scale) part. Thus the power law, if it exists, is
contaminated. We then compare the Hilbert method with structure functions (SF),
detrended fluctuation analysis (DFA), and wavelet leader (WL) by analyzing
fractional Brownian motion and synthesized multifractal time series. For the
former simulation, we find that all methods provide comparable results. For the
latter simulation, we perform simulations with an intermittent parameter {\mu}
= 0.15. We find that the SF underestimates scaling exponent when q > 3. The
Hilbert method provides a slight underestimation when q > 5. However, both DFA
and WL overestimate the scaling exponents when q > 5. It seems that Hilbert and
DFA methods provide better singularity spectra than SF and WL. We finally apply
all methods to a passive scalar (temperature) data obtained from a jet
experiment with a Taylor's microscale Reynolds number Relambda \simeq 250. Due
to the presence of strong ramp-cliff structures, the SF fails to detect the
power law behavior. For the traditional method, the ramp-cliff structure causes
a serious artificial energy flux from the low-frequency (large scale) to the
high-frequency (small scale) part. Thus DFA and WL underestimate the scaling
exponents. However, the Hilbert method provides scaling exponents
{\xi}{\theta}(q) quite close to the one for longitudinal velocity.Comment: 13 pages, 10 figure
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