Underwater sound propagation is primarily driven by a nonlinear forward model relating variability of the ocean sound speed profile (SSP) to the acoustic observations (e.g., eigenray arrival times). Ocean acoustic tomography (OAT) methods aim at reconstructing SSP variations (with respect to a reference environment) from changes of the acoustic measurements between multiple source-receiver pairs. This article investigates the performance of three different OAT methods: (1) model-based methods (i.e., classical ray-based OAT using a linearized forward model), (2) data-driven methods (such as deep learning) to directly learn the inverse model, and (3) a hybrid solution [i.e., the neural adjoint (NA) method], which combines deep learning of the forward model with a standard recursive optimization to estimate SSPs. Additionally, synthetic SSPs were generated to augment the variability of the training set. These methods were tested with modeled ray arrivals calculated for a downward refracting environment with mild fluctuations of the thermocline. Idealized towed and fixed source configurations are considered. Results indicate that merging data-driven and model-based methods can benefit OAT predictions depending on the selected sensing configurations and actual ray coverage of the water column. But ultimately, the robustness of OAT predictions depends on the dynamics of the SSP variations.

A library of broadband (100–1000 Hz) channel impulse responses (CIRs) estimated between a short bottom-mounted vertical line array (VLA) in the Santa Barbara channel and selected locations along the tracks of 27 isolated transiting ships, cumulated over nine days, is constructed using the ray-based blind deconvolution algorithm. Treating this CIR library either as data-derived replica for broadband matched-field processing (MFP) or training data for machine learning yields comparable ranging accuracy (∼50 m) for nearby vessels up to 3.2 km for both methods. Using model-based replica of the direct path only computed for an average sound-speed profile comparatively yields∼110 m ranging accuracy.

This paper investigates the performance of sequential bottom parameter estimation based on ray-based blind deconvolution (RBD) [Sabra et al., JASA EL42-7 (2010)] of sources of opportunity using the 2016 Santa Barbara Channel (SBC) experimental recordings of shipping noise. The RBD algorithm relies on estimating the unknown phase of the source of opportunity through wideband beamforming along a well-resolved ray path to approximate the environment’s channel impulse responses (CIR) between the source and the VLA elements. The corrected power ratio of the direct and bottom-bounced arrivals is processed to infer the bottom reflection loss and is utilized to invert for the bottom parameters. Sequential parameter estimation uses a state space model for predicting and correcting the bottom parameters as the estimated bottom reflection loss values become available. Inversions results for the SBC experiment were also performed with conventional active sources to validate the inversion obtained with RBD of sources of opportunity.

A general blind deconvolution algorithmic framework is developed for sources of opportunity (e.g., ships at known locations) in an ocean waveguide. Here, both channel impulse responses (CIRs) and unknown source signals need to be simultaneously estimated from only the recorded signals on a receiver array using blind deconvolution, which is generally an ill-posed problem without any a priori information or additional assumptions about the underlying structure of the CIRs. By exploiting the typical ray-like arrival-time structure of the CIRs between a surface source and the elements of a vertical line array (VLA) in ocean waveguides, a principle component analysis technique is applied to build a bilinear parametric model linking the amplitudes and arrival-times of the CIRs across all channels for a variety of admissible ocean environments. The bilinear channel representation further reduces the dimension of the channel parametric model compared to linear models. A truncated power interaction deconvolution algorithm is then developed by applying the bilinear channel model to the traditional subspace deconvolution method. Numerical and experimental results demonstrate the robustness of this blind deconvolution methodology.

This paper summarizes the ongoing investigations surrounding the use of a ray-based blind deconvolution algorithm to recover arrival time information from sources of opportunity, such as shipping vessels, recorded on vertical line arrays (VLAs) in ocean waveguides. The deconvolution is primarily performed by using an estimate of the unknown source phase, obtained through wideband beamforming, to essentially match filter the VLA recordings and recover the channel impulse response (CIR). This paper will focus on results from an experiment performed in 2016 in the Santa Barbara shipping channel (water depth ~550 m). Four VLAs, with both short (~15 m) and long (~56 m) apertures, were deployed between the north and south bound shipping lanes and continuously collected acoustic data during one week. With the ultimate goal of passive acoustic tomography in mind, this paper aims to discuss (1) the robustness of the algorithm to extract differential arrival times along VLA elements using ships as sources of opportunity, (2) the achievable accuracy of blind arrival time measurements in comparison to the time-of-flight precision required for tomographic inversions, and (3) the ideal parameters (e.g., frequency bandwidth, snapshot duration, beamforming methodology...) for which to perform this ray-based blind deconvolution method in SBC-like ocean environments.