Noise interferometry is the process by which approximations to acoustic Green's functions, which describe sound propagation between two locations, are estimated by cross-correlating time series of ambient noise measured at those locations. Noise-interferometry-based approximations to Green's functions can be used as the basis for a variety of inversion algorithms, thereby providing a purely passive alternative to active-source ocean acoustic remote sensing. In this paper we give an overview of results from noise interferometry experiments conducted in the Florida Straits at 100 m depth in December 2012, and at 600 m depth in September/October 2013. Under good conditions for noise interferometry, estimates of cross-correlation functions are shown to allow one to perform advanced phase-coherent signal processing techniques to: perform waveform inversions; estimate currents by exploiting nonreciprocity; perform time-reversal/back-propagation calculations; and investigate modal dispersion using time-warping techniques. Conditions which are favorable for noise interferometry are identified and discussed.

Adult American eels (Anguilla rostrata) are vulnerable to hydropower turbine mortality during outmigration from growth habitat in inland waters to the ocean where they spawn. Imaging sonar is a reliable and proven technology for monitoring of fish passage and migration; however, there is no efficient automated method for eel detection. We designed a deep learning model for automated detection of adult American eels from sonar data. The method employs convolution neural network (CNN) to distinguish between 14 images of eels and non-eel objects. Prior to image classification with CNN, background subtraction and wavelet denoising were applied to enhance sonar images. The CNN model was first trained and tested on data obtained from a laboratory experiment, which yielded overall accuracies of >98% for image-based classification. Then, the model was trained and tested on field data that were obtained near the Iroquois Dam located on the St. Lawrence River; the accuracy achieved was commensurate with that of human experts.

This paper investigates a novel approach to processing records of ambient noise in the ocean that are measured concurrently in spatially separated locations. The approach is a synthesis of two well-known phase-coherent signal processing techniques. At the first stage of processing, an approximation to the transient acoustic Green function is found by the method of noise interferometry. At the second stage, the approximate Green function is time reversed and back propagated from the location of one of the receivers, thereby producing a focus in the vicinity of the other receiver. Unlike the earlier work, measurements at just two points (rather than vertical array measurements) are used when the sound-propagation range is large compared to the ocean depth. The requirement for optimal focusing of the back-propagated field is shown to lead to extraction of estimates of the unknown physical parameters of the waveguide and, hence, to passive acoustic remote sensing of the ocean.

Theoretical studies have shown that cross-correlation functions (CFs) of time series of ambient noise measured at two locations yield approximations to the Green's functions (GFs) that describe propagation between those locations. Specifically, CFs are estimates of weighted GFs. In this paper, it is demonstrated that measured CFs in the 20–70 Hz band can be accurately modeled as weighted GFs using ambient noise data collected in the Florida Straits at ∼100 m depth with horizontal separations of 5 and 10 km. Two weighting functions are employed. These account for (1) the dipole radiation pattern produced by a near-surface source, and (2) coherence loss of surface-reflecting energy in time-averaged CFs resulting from tidal fluctuations. After describing the relationship between CFs and GFs, the inverse problem is considered and is shown to result in an environmental model for which agreement between computed and simulated CFs is good.

Acoustic emissions from current energy converters remain an environmental concern for regulators because of their potential effects on marine life and uncertainties about their effects stemming from a lack of sufficient observational data. Several recent opportunities to characterize tidal turbine sound emissions have begun to fill knowledge gaps and provide a context for future device deployments. In July 2021, a commercial-off-the-shelf hydrophone was deployed in a free-drifting configuration to measure underwater acoustic emissions and characterize a 25 kW-rated tidal turbine at the University of New Hampshire’s Living Bridge Project in Portsmouth, New Hampshire. Sampling methods and analysis were performed in alignment with the recently published IEC 62600-40 Technical Specification for acoustic characterization of marine energy converters. Results from this study indicate acoustic emissions from the turbine were below ambient sound levels and therefore did not have a significant impact on the underwater noise levels of the project site. As a component of Pacific Northwest National Laboratory’s Triton Field Trials (TFiT) described in this Special Issue, this effort provides a valuable use case for the IEC 62600-40 Technical Specification framework and further recommendations for cost-effective technologies and methods for measuring underwater noise at future current energy converter project sites.