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The low-order normal modes with small grazing angles (SGA) often control long-range sound field characteristics in shallow water. The SGA reflection loss from a half-space low-velocity bottom (LVB) is independent of the sound attenuation, except around the angle of complete transmission; the SGA bottom reflection loss (BRL) from a seafloor with a top low-velocity layer is very insensitive to the LVB attenuation also, except around a few selected frequencies. Thus, the "seafloor velocity-attenuation coupling" problem will be more fatal for LVB geo-acoustic inversions. The dispersion equation of the normal modes in the LVB layer is coincidentally the same as the singularity expression of the SGA reflection coefficient in the water column, resulting in a sound siphon effect that causes the abnormally high SGA BRL and transmission loss in the water at the siphon frequencies. The siphon effect is very sensitive to seafloor acoustic parameters, might offer a physical base for geo-acoustic inversion, and show a dim light in a "gray area" for inverting the LVB sound attenuation at low to mid frequencies. As an example, the acoustic siphon effect and related seafloor geophysical parameters forming it in the Yellow Sea are reported in this paper. [ABSTRACT FROM AUTHOR]

Attenuation is the most difficult seafloor acoustic property to get, particularly at low to mid frequencies. For low velocity bottoms (LVB), it becomes even more challenging, due to its small attenuation and lower velocity (relative to the velocity of the adjacent water). The latter one causes a fatal "seafloor velocity-attenuation couplings" in geo-acoustic inversions. Thus, attenuation inversions for the LVB require an accurate seafloor velocity profile, especially the velocity in the LVB layer. The propagation of explosive sound in the Yellow Sea with a strong thermocline and a top LVB layer exhibits many prominent characteristics: modal dispersion (the ground wave, water wave, Airy phase), two groups of water waves at high frequencies, and the siphon effect which causes abnormally large sound transmission loss at selected frequencies, etc. These observations are used to precisely measure the critical frequency, the Airy frequency, Airy wave velocity, 1st mode group velocity, and to derive the velocities in the LVB layer and in the basement. Using inverted seafloor parameters, the source level-normalized transmission loss and the first mode decay rate in ranges up to 27.66 km, the sound attenuations in the LVB are derived for a frequency range of 13–5000 Hz. [ABSTRACT FROM AUTHOR]

The study of transient acoustic wave propagation across the Arctic Ocean ice layer provides theoretical guidance for the design of trans-ice acoustic communication systems. In this study, the Arctic Ocean was modeled as an ice–water composite structure, where the ice and water are regarded as an elastic solid and liquid, respectively. An analytical transient solution for acoustic wave propagation in this structure was derived using the eigenfunction expansion method. Further, the numerical procedures were presented and used to analyze the acoustic wave propagation characteristics across the ice layer. The results show that waveforms corresponding to the radial displacements are more severely distorted than the axial displacements. The amplitudes of the radial and axial displacements decreased rapidly with increasing propagation distance. The ice thickness had a greater impact on the radial displacement than axial displacement; the thicker the ice, the greater the distortion for both radial and axial displacements. [ABSTRACT FROM AUTHOR]

A feature matching method based on the convolutional neural network (named FM-CNN), inspired from matched-field processing (MFP), is proposed to estimate source depth in shallow water. The FM-CNN, trained on the acoustic field replicas of a single source generated by an acoustic propagation model in a range-independent environment, is used to estimate single and multiple source depths in range-independent and mildly range-dependent environments. The performance of the FM-CNN is compared to the conventional MFP method. Sensitivity analysis for the two methods is performed to study the impact of different environmental mismatches (i.e., bottom parameters, water column sound speed profile, and topography) on depth estimation performance in the East China Sea environment. Simulation results demonstrate that the FM-CNN is more robust to the environmental mismatch in both single and multiple source depth estimation than the conventional MFP. The proposed FM-CNN is validated by real data collected from four tracks in the East China Sea experiment. Experimental results demonstrate that the FM-CNN is capable of reliably estimating single and multiple source depths in complex environments, while MFP has a large failure probability due to the presence of strong sidelobes and wide mainlobes. [ABSTRACT FROM AUTHOR]

Passive synthetic aperture (PSA) extension for a moving array has the ability to enhance the accuracy of direction-of-arrival (DOA) estimation by constructing a larger virtual aperture. The array element overlap in array continuous measurements is required for the traditional extended towed array measurement (ETAM) methods. Otherwise, the phase factor estimation is biased, and the aperture extension fails when multiple sources exist. To solve this problem, passive aperture extension with sparse Bayesian learning (SBL) is proposed. In this method, SBL is used to simultaneously estimate the phase correction factors of different targets, followed by phase compensation applied to the extended aperture manifold vectors for DOA estimation. Simulation and experimental data results demonstrate that this proposed method successfully extends the aperture and provides higher azimuth resolution and accuracy compared to conventional beamforming (CBF) and SBL without extension. Compared with the traditional ETAM methods, the proposed method still performs well even when the array elements are not overlapped during the motion. [ABSTRACT FROM AUTHOR]

Matched autoproduct processing (MAP) refers to a matched field processing (MFP) style array signal processing technique for passive source localization, which interrogates frequency-difference autoproduct instead of genuine acoustic pressure. Due to frequency downshifting, MAP is less sensitive to environmental mismatch, but it suffers from low spatial resolution and a low peak-to-sidelobe ratio of ambiguity surface. These source localization metrics are herein improved with coherent approaches. Specifically, the coherent normalized MFP is extended to coherent matched autoproduct processing (CMAP), a difference frequency coherent algorithm that exploits correlations among the autoproducts at various difference frequencies and eliminates the phase factor of the source spectrum for passive source localization. Phase-only coherent matched autoproduct processing is a CMAP derivation technique that only uses phase information. Through simulations in a Munk sound-speed profile environment, sensitivity analysis in the South China Sea environment, and high signal-to-noise ratio experimental measurements, these two algorithms are validated as compared to the conventional MFP and incoherent MAP. Simulation investigations demonstrate that difference frequency coherent algorithms can suppress sidelobes while simultaneously enhancing the localization resolution and robustness. The experimental results generally support the findings of the simulations. [ABSTRACT FROM AUTHOR]

Device fabrication based on piezoelectric materials requires prior characterization of full matrix constants. For this, the Institute of Electrical and Electronics Engineers standard on piezoelectricity suggests the use of ultrasonic pulse-echo and electric resonance methods. However, these techniques tend to provide inconsistent characterization, because they require multiple samples with drastically different sizes. Resonant ultrasound spectroscopy (RUS) is a promising alternative, because it uses only a single sample for characterization, thus ensuring self-consistent results. The inverse problem of finding material constants from resonant frequencies is often solved using the nonlinear least squares method despite its being a time-consuming algorithm. Herein, deep learning (DL) is introduced in the inversion procedure of RUS. After the DL network is trained, the material constants are determined with high efficiency. The practicability and reliability of the combination of DL and RUS are demonstrated by characterizing the full tensor constants of LiNbO3 single crystals. [ABSTRACT FROM AUTHOR]

A multi-range vertical array data processing (MRP) method based on a convolutional neural network (CNN) is proposed to estimate geoacoustic parameters in shallow water. The network input is the normalized sample covariance matrices of the broadband multi-range data received by a vertical line array. Since the geoacoustic parameters (e.g., bottom sound speed, density, and attenuation) have different scales, the multi-task learning is used to estimate these parameters simultaneously. To reduce the influence of the uncertainty of the source position, the training and validation data are composed of the simulation data of different source depths. Simulation results demonstrate that compared with the conventional matched-field inversion (MFI), the CNN with MRP alleviates the coupling between the geoacoustic parameters and is more robust to different source depths in the shallow water environment. Based on the inversion results, better localization performance is achieved when the range-dependent environment is assumed to be a range-independent model. Real data from the East China Sea experiment are used to validate the MRP method. The results show that, compared with the MFI and the CNN with single-range vertical array data processing, the use of geoacoustic parameters from MRP achieves better localization performance. [ABSTRACT FROM AUTHOR]

This paper proposes the use of gated feedback gated recurrent unit network (GFGRU), a learning-based sparse estimation algorithm, for multiple source localization in the direct arrival zone of the deep ocean. The GFGRU, trained on sound field replicas of a single source generated by an acoustic propagation model, is used to estimate the ranges and depths of multiple sources without knowing the number of sources. The performance of GFGRU is compared to the Bartlett processor, feedforward neural network (FNN), and sparse Bayesian Learning (SBL) algorithm. Simulations indicate that GFGRU behaves similarly to SBL and offers modest localization performance improvement over the Bartlett and FNN in the presence of array tilt mismatch. The results of real data from the South China Sea also verify the robustness of the proposed GFGRU using a 105 m-aperture vertical array in the deep ocean. [ABSTRACT FROM AUTHOR]

An approach of broadband mode separation in shallow water is proposed using phase speed extracted from one hydrophone and solved with sparse Bayesian learning (SBL). The approximate modal dispersion relation, connecting the horizontal wavenumbers (phase velocities) for multiple frequencies, is used to build the dictionary matrix for SBL. Given a multi-frequency pressure vector on one hydrophone, SBL estimates a set of sparse coefficients for a large number of atoms in the dictionary. With the estimated coefficients and corresponding atoms, the separated normal modes are retrieved. The presented method can be used for impulsive or known-form signals in a shallow-water environment while no bottom information is required. The simulation results demonstrate that the proposed approach is adapted to the environment where both the reflected and refracted modes coexist, whereas the performance of the time warping transformation degrades significantly in this scenario. [ABSTRACT FROM AUTHOR]

A multi-task learning (MTL) method with adaptively weighted losses applied to a convolutional neural network (CNN) is proposed to estimate the range and depth of an acoustic source in deep ocean. The network input is the normalized sample covariance matrices of the broadband data received by a vertical line array. To handle the environmental uncertainty, both the training and validation data are generated by an acoustic propagation model based on multiple possible sets of environmental parameters. The sensitivity analysis is investigated to examine the effect of mismatched environmental parameters on the localization performance in the South China Sea environment. Among the environmental parameters, the array tilt is found to be the most important factor on the localization. Simulation results demonstrate that, compared with the conventional matched field processing (MFP), the CNN with MTL performs better and is more robust to array tilt in the deep-ocean environment. Tests on real data from the South China Sea also validate the method. In the specific ranges where the MFP fails, the method reliably estimates the ranges and depths of the underwater acoustic source. [ABSTRACT FROM AUTHOR]

The horizontal wavenumbers and modal depth functions are estimated by block sparse Bayesian learning (BSBL) for broadband signals received by a vertical line array in shallow-water waveguides. The dictionary matrix consists of multi-frequency modal depth functions derived from shooting methods given a large set of hypothetical horizontal wavenumbers. The dispersion relation for multi-frequency horizontal wavenumbers is also taken into account to generate the dictionary. In this dictionary, only a few of the entries are used to describe the pressure field. These entries represent the modal depth functions and associated wavenumbers. With the constraint of block sparsity, the BSBL approach is shown to retrieve the horizontal wavenumbers and corresponding modal depth functions with high precision, while a priori knowledge of sea bottom, moving source, and source locations is not needed. The performance is demonstrated by simulations and experimental data. [ABSTRACT FROM AUTHOR]

Measurements along two ship tracks were obtained in an experiment to investigate the properties of acoustic propagation over the continental slope in the South China Sea. The measured data show a notable difference in transmission loss about 35 dB as sound crosses different geodesic paths. Numerical simulations indicate that the range and azimuth-dependent geological properties control the level of the transmission loss and lead to this large transmission loss fluctuation. In addition, the model also suggests some small-scale features of horizontal refraction effect caused by irregular topography, but they are not observed in the measured data. [ABSTRACT FROM AUTHOR]

WO3 nanowire films were prepared by thermal evaporation. The electrochromic properties and relative near infrared absorption mechanism of the films in detail are investigated by electrochemistry, ultraviolet–visible–near infrared spectroscopy, x-ray diffraction spectroscopy, and some theoretical approaches. The studies indicate that WO3 nanowire films show excellent electrochromic properties, including a rapid response time (∼3 s) and a large contrast ratio between the bleached and the colored states. These improvements are due to the loose structure of the films and the small diameter of a single nanowire. It is also shown that a lithium tungsten bronze structure appears when the film is colored, causing two broad absorption bands centering around 920 and 1800 nm. A calculation including the Urbach effect, cation-polaron absorption and large polaron absorption was carried out to interpret the absorbance. A brief discussion of the discrepancy of the transmittance spectrum in the region λ>800 nm between the nanowire film and other traditional films is presented in the end. [ABSTRACT FROM AUTHOR]

A deep learning approach based on big data is proposed to locate broadband acoustic sources using a single hydrophone in ocean waveguides with uncertain bottom parameters. Several 50-layer residual neural networks, trained on a huge number of sound field replicas generated by an acoustic propagation model, are used to handle the bottom uncertainty in source localization. A two-step training strategy is presented to improve the training of the deep models. First, the range is discretized in a coarse (5 km) grid. Subsequently, the source range within the selected interval and source depth are discretized on a finer (0.1 km and 2 m) grid. The deep learning methods were demonstrated for simulated magnitude-only multi-frequency data in uncertain environments. Experimental data from the China Yellow Sea also validated the approach. [ABSTRACT FROM AUTHOR]

This paper reports on the long-term statistics of ambient noise (50–2000 Hz) in the northeast South China Sea. The data were collected from July 2016 to March 2018. The long-term statistics, seasonal, and diel variations of ambient noise are analyzed. There are significant seasonal variations from 500 to 2000 Hz. The ambient noise level at 1000 Hz is 6–10 dB higher in winter than in summer. There is only a small difference between the day and night comparison (less than 1.6 dB). The results are significant to evaluate and improve the performance of underwater acoustic systems in this area. [ABSTRACT FROM AUTHOR]

A correction to the article "Electrochromic properties of WO3 nanowire films and mechanism responsible for the near infrared absorption," published online in the August 7, 2008 issue, is presented.

Signals propagating in waveguides can be decomposed into normal modes that exhibit dispersive characteristics. Based on the dispersion analysis, the warping transformation can be used to improve the modal separability. Different from the warping transformation defined using an ideal waveguide model, an improved warping operator is presented in this paper based on the beam-displacement ray-mode (BDRM) theory, which can be adapted to low-frequency signals in a general shallow water waveguide. For the sake of obtaining the warping operators for the general waveguides, the dispersion formula is first derived. The approximate dispersion relation can be achieved with adequate degree of accuracy for the waveguides with depth-dependent sound speed profiles (SSPs) and acoustic bottoms. Performance and accuracy of the derived formulas for the dispersion curves are evaluated by comparing with the numerical results. The derived warping operators are applied to simulations, which show that the non-linear dispersion structures can be well compensated by the proposed warping operators.