Multiple mechanized ocean vessels, including both surface ships and submerged vehicles, can be simultaneously monitored over instantaneous continental-shelf scale regions >10,000 km ${}^2$ via passive ocean acoustic waveguide remote sensing. A large-aperture densely-sampled coherent hydrophone array system is employed in the Norwegian Sea in Spring 2014 to provide directional sensing in 360 degree horizontal azimuth and to significantly enhance the signal-to-noise ratio (SNR) of ship-radiated underwater sound, which improves ship detection ranges by roughly two orders of magnitude over that of a single hydrophone. Here, 30 mechanized ocean vessels spanning ranges from nearby to over 150 km from the coherent hydrophone array, are detected, localized and classified. The vessels are comprised of 20 identified commercial ships and 10 unidentified vehicles present in 8 h/day of Passive Ocean Acoustic Waveguide Remote Sensing (POAWRS) observation for two days. The underwater sounds from each of these ocean vessels received by the coherent hydrophone array are dominated by narrowband signals that are either constant frequency tonals or have frequencies that waver or oscillate slightly in time. The estimated bearing-time trajectory of a sequence of detections obtained from coherent beamforming are employed to determine the horizontal location of each vessel using the Moving Array Triangulation (MAT) technique. For commercial ships present in the region, the estimated horizontal positions obtained from passive acoustic sensing are verified by Global Positioning System (GPS) measurements of the ship locations found in a historical Automatic Identification System (AIS) database. We provide time-frequency characterizations of the underwater sounds radiated from the commercial ships and the unidentified vessels. The time-frequency features along with the bearing-time trajectory of the detected signals are applied to simultaneously track and distinguish these vessels.

The vocalization source level distributions and pulse compression gains are estimated for four distinct baleen whale species in the Gulf of Maine: fin, sei, minke and an unidentified baleen whale species. The vocalizations were received on a large-aperture densely-sampled coherent hydrophone array system useful for monitoring marine mammals over instantaneous wide areas via the passive ocean acoustic waveguide remote sensing technique. For each baleen whale species, between 125 and over 1400 measured vocalizations with significantly high Signal-to-Noise Ratios (SNR > 10 dB) after coherent beamforming and localized with high accuracies (<10% localization errors) over ranges spanning roughly 1 km–30 km are included in the analysis. The whale vocalization received pressure levels are corrected for broadband transmission losses modeled using a calibrated parabolic equation-based acoustic propagation model for a random range-dependent ocean waveguide. The whale vocalization source level distributions are characterized by the following means and standard deviations, in units of dB re 1 $\mathsf{\mu }$ Pa at 1 m: 181.9 ± 5.2 for fin whale 20-Hz pulses, 173.5 ± 3.2 for sei whale downsweep chirps, 177.7 ± 5.4 for minke whale pulse trains and 169.6 ± 3.5 for the unidentified baleen whale species downsweep calls. The broadband vocalization equivalent pulse-compression gains are found to be 2.5 ± 1.1 for fin whale 20-Hz pulses, 24 ± 10 for the unidentified baleen whale species downsweep calls and 69 ± 23 for sei whale downsweep chirps. These pulse compression gains are found to be roughly proportional to the inter-pulse intervals of the vocalizations, which are 11 ± 5 s for fin whale 20-Hz pulses, 29 ± 18 for the unidentified baleen whale species downsweep calls and 52 ± 33 for sei whale downsweep chirps. The source level distributions and pulse compression gains are essential for determining signal-to-noise ratios and hence detection regions for baleen whale vocalizations received passively on underwater acoustic sensing systems, as well as for assessing communication ranges in baleen whales.

A large variety of sound sources in the ocean, including biological, geophysical, and man-made, can be simultaneously monitored over instantaneous continental-shelf scale regions via the passive ocean acoustic waveguide remote sensing (POAWRS) technique by employing a large-aperture densely-populated coherent hydrophone array system. Millions of acoustic signals received on the POAWRS system per day can make it challenging to identify individual sound sources. An automated classification system is necessary to enable sound sources to be recognized. Here, the objectives are to (i) gather a large training and test data set of fin whale vocalization and other acoustic signal detections; (ii) build multiple fin whale vocalization classifiers, including a logistic regression, support vector machine (SVM), decision tree, convolutional neural network (CNN), and long short-term memory (LSTM) network; (iii) evaluate and compare performance of these classifiers using multiple metrics including accuracy, precision, recall and F1-score; and (iv) integrate one of the classifiers into the existing POAWRS array and signal processing software. The findings presented here will (1) provide an automatic classifier for near real-time fin whale vocalization detection and recognition, useful in marine mammal monitoring applications; and (2) lay the foundation for building an automatic classifier applied for near real-time detection and recognition of a wide variety of biological, geophysical, and man-made sound sources typically detected by the POAWRS system in the ocean.

The passive ocean acoustic waveguide remote sensing (POAWRS) technique is employed to detect and characterize the underwater sound radiated from three scientific research and fishing vessels received at long ranges on a large-aperture densely-sampled horizontal coherent hydrophone array. The sounds radiated from the research vessel (RV) Delaware II in the Gulf of Maine, and the RV Johan Hjort and the fishing vessel (FV) Artus in the Norwegian Sea are found to be dominated by distinct narrowband tonals and cyclostationary signals in the 150 Hz to 2000 Hz frequency range. The source levels of these signals are estimated by correcting the received pressure levels for transmission losses modeled using a calibrated parabolic equation-based acoustic propagation model for random range-dependent ocean waveguides. The probability of the detection region for the most prominent signal radiated by each ship is estimated and shown to extend over areas spanning roughly 200 km in diameter when employing a coherent hydrophone array. The current standard procedure for quantifying ship-radiated sound source levels via one-third octave bandwidth intensity averaging smoothes over the prominent tonals radiated by a ship that can stand 10 to 30 dB above the local broadband level, which may lead to inaccurate or incorrect assessments of the impact of ship-radiated sound.

The vocalization behavior of humpback whales was monitored over vast areas of the Gulf of Maine using the passive ocean acoustic waveguide remote sensing technique (POAWRS) over multiple diel cycles in Fall 2006. The humpback vocalizations comprised of both song and non-song are analyzed. The song vocalizations, composed of highly structured and repeatable set of phrases, are characterized by inter-pulse intervals of 3.5 ± 1.8 s. Songs were detected throughout the diel cycle, occuring roughly 40% during the day and 60% during the night. The humpback non-song vocalizations, dominated by shorter duration (≤3 s) downsweep and bow-shaped moans, as well as a small fraction of longer duration (∼5 s) cries, have significantly larger mean and more variable inter-pulse intervals of 14.2 ± 11 s. The non-song vocalizations were detected at night with negligible detections during the day, implying they probably function as nighttime communication signals. The humpback song and non-song vocalizations are separately localized using the moving array triangulation and array invariant techniques. The humpback song and non-song moan calls are both consistently localized to a dense area on northeastern Georges Bank and a less dense region extended from Franklin Basin to the Great South Channel. Humpback cries occur exclusively on northeastern Georges Bank and during nights with coincident dense Atlantic herring shoaling populations, implying the cries are feeding-related.

The ability to monitor surface ships and other ocean vehicles continuously over instantaneous wide areas is essential for a wide range of applications including defense and ocean environmental assessment. Here, we employ a large-aperture coherent hydrophone array system to detect, localize, and classify several surface ships and other ocean vehicles from their sounds radiated underwater using the passive ocean acoustic waveguide remote sensing (POAWRS) technique. The approach is calibrated for four distinct research and fisheries survey vessels with accurately known locations obtained from global positioning systems (GPS). Acoustic signals passively recorded on the coherent hydrophone array are first beamformed for their azimuthal dependencies. The sounds radiated by ocean vehicles are automatically detected using a threshold detector from the beamformed spectrograms. The bearing versus time trajectories of sequences of detections are used to localize the ocean vehicles by employing the moving array triangulation technique. The sounds radiated by the ships are dominated by distinct tonals and cyclostationary signals in the 50 to 2000 Hz frequency range. The temporal-spectral characteristics of these signal can be used to classify each ship. Our analysis indicates the ocean vehicles can be instantaneously monitored and tracked over wide areas spanning more than 300 km in diameter.