There is increasing interest in deploying swarms of underwater vehicles for marine surveys. One of the main challenges when designing these systems is coming up with an appropriate way to localize each vehicle in relation to one another. This paper considers the self-localization of a deforming swarm of subsurface floating vehicles using impulsive sources of opportunity, such as the sounds of snapping shrimp that are present in warm coastal waters. Impulsive sound sources provide high intensity, broadband signals that facilitate accurate arrival time detections across each vehicle. This makes them useful references for a self-localization solution. However, the similarity between different signals presents a significant correspondence problem, which must be solved to provide accurate estimates of the changing geometry of the swarm. A geometric solution to this correspondence problem is shown and an optimization procedure is proposed to track the geometry of a swarm as it changes. The method is verified using a swarm of 17 self-ballasting subsurface floats that independently drifted with currents off of the coast of San Diego, California. The changing geometry of the floats was estimated using both an acoustic localization system and the proposed approach. The two estimates show good agreement, validating our method. We believe that this new localization strategy is useful for high endurance, low power, and multi-vehicle surveys.

This paper presents a demonstration of ambient acoustic noise processing on a set of free floating oceanic receivers whose relative positions vary with time. It is shown that it is possible to retrieve information that is relevant to the travel time between the receivers. With thousands of short time cross-correlations (10 s) of varying distance, it is shown that on average, the decrease in amplitude of the noise correlation function with increased separation follows a power law. This suggests that there may be amplitude information that is embedded in the noise correlation function. An incoherent beamformer is developed, which shows that it is possible to determine a source direction using an array with moving elements and large element separation. This incoherent beamformer is used to verify cases when the distribution of noise sources in the ocean allows one to recover travel time information between pairs of mobile receivers.

In 1980, Holliday and Pieper stated: "Most sound scattering in the ocean volume can be traced to a biotic origin." However, most of the bioacoustics research in the past three decades has focused on only a few groups of organisms. Targets such as small gelatinous organisms, marine snow, and phytoplankton, e.g. have been generally to be considered relatively transparent to acoustic waves due to their sizes and relatively low sound speed and density contrasts relative to seawater. However, using a broadband system (ZOOPS-O 2) we found that these targets contributed significantly to acoustic returns in the 1.5-2.5 MHz frequency range. Given that phytoplankton and marine snow layers are ubiquitous features of coastal regions; this works suggests that they should be considered as potential sources of backscatter in biological acoustic surveys.

Here we describe the configuration, calibration, and initial results from the combination of two recently developed underwater instruments that measure acoustic reflectivity and, simultaneously, the location, pose and size of millimeter-sized plankton relative to the sonar beam. The acoustic system, ZOOPS (ZOOPlankton Sonar), uses a broadband chirp signal that operates with a single monostatically configured transducer in the 1.5-2.5 MHz frequency range. We demonstrate that the system can record, with adequate signal-to-noise levels, identifiable reflections from single copepods with lengths as small as 360 μm. To simultaneously identify taxa and measure orientation, a pair of "O-Cam" microscopes were stereoscopically calibrated and geometrically co-registered with the orientation and range-resolved acoustic transmissions of the sonar beam. The system's capability is demonstrated via the in situ measurement of acoustic reflectivity as a function of orientation for 224 individual pelagic copepods comprising three orders of free-living taxa. Comparison with a well-known model, the Distorted Wave Born Approximation (DWBA), using a spheroidal formulation, yields both differences and similarities between the in situ field data and the model's predictions.

Groups of coordinated underwater vehicles or sensors are powerful tools for monitoring the ocean. A requirement of many coordinated surveys is to determine a spatial reference between each node in a swarm. This work considers the self-localization of a swarm of independently moving vehicles using acoustic noise from a dominating incoherent source recorded by a single hydrophone onboard each vehicle. This method provides an inexpensive and infrastructure-free spatial reference between vehicles. Movement between the vehicles changes the swarm geometry and a self-localization estimate must be generated from data collected on short time scales. This challenges past self-localization approaches for acoustic arrays. To overcome this challenge, the proposed self-localization algorithm jointly estimates the vehicle geometry and the directionality of the ambient noise field, without prior knowledge of either estimate. To demonstrate this method, experimental results are provided when a boat is the main dominating source. The results demonstrate the ability to both estimate the direction of arrival of the boat and the relative positions of the vehicles in the swarm. The approach in this paper is not limited to moving vessels. Simulations are provided to examine three different factors that affect the proposed solution: inter-vehicle motion, vehicle geometry, and the azimuthal variance of the noise field.

Modern in situ digital imaging systems collect vast numbers of images of marine organisms and suspended particles. Automated methods to classify objects in these images – largely supervised machine learning techniques – are now used to deal with this onslaught of biological data. Though such techniques can minimize the human cost of analyzing the data, they also have important limitations. In training automated classifiers, we implicitly program them with an inflexible understanding of the environment they are observing. When the relationship between the classifier and the population changes, the computer's performance degrades, potentially decreasing the accuracy of the estimate of community composition. This limitation of automated classifiers is known as “dataset shift.” Here, we describe techniques for addressing dataset shift. We then apply them to the output of a binary deep neural network searching for diatom chains in data generated by the Scripps Plankton Camera System (SPCS) on the Scripps Pier. In particular, we describe a supervised quantification approach to adjust a classifier's output using a small number of human corrected images to estimate the system error in a time frame of interest. This method yielded an 80% improvement in mean absolute error over the raw classifier output on a set of 41 independent samples from the SPCS. The technique can be extended to adjust the output of multi-category classifiers and other in situ observing systems.

Measuring the ever-changing 3-dimensional (3D) motions of the ocean requires simultaneous sampling at multiple locations. In particular, sampling the complex, nonlinear dynamics associated with submesoscales (<1-10 km) requires new technologies and approaches. Here we introduce the Mini-Autonomous Underwater Explorer (M-AUE), deployed as a swarm of 16 independent vehicles whose 3D trajectories are measured near-continuously, underwater. As the vehicles drift with the ambient flow or execute preprogrammed vertical behaviours, the simultaneous measurements at multiple, known locations resolve the details of the flow within the swarm. We describe the design, construction, control and underwater navigation of the M-AUE. A field programme in the coastal ocean using a swarm of these robots programmed with a depth-holding behaviour provides a unique test of a physical-biological interaction leading to plankton patch formation in internal waves. The performance of the M-AUE vehicles illustrates their novel capability for measuring submesoscale dynamics.

Cross-shore velocities in the coastal ocean typically vary with depth. The direction and magnitude of transport experienced by meroplanktonic larvae will therefore be influenced by their vertical position. To quantify how swimming behavior and vertical position in internal waves influence larval cross-shore transport in the shallow (~ 20 m), stratified coastal waters off Southern California, we deployed swarms of novel, subsurface larval mimics, the Mini-Autonomous Underwater Explorers (M-AUEs). The M-AUEs were programmed to maintain a specified depth, and were deployed near a mooring. Transport of the M-AUEs was predominantly onshore, with average velocities up to 14 cm s−1. To put the M-AUE deployments into a broader context, we simulated > 500 individual high-frequency internal waves observed at the mooring over a 14-d deployment; in each internal wave, we released both depth-keeping and passive virtual larvae every meter in the vertical. After the waves' passage, depth-keeping virtual larvae were usually found closer to shore than passive larvae released at the same depth. Near the top of the water column (3–5-m depth), ~ 20% of internal waves enhanced onshore transport of depth-keeping virtual larvae by ≥ 50 m, whereas only 1% of waves gave similar enhancements to passive larvae. Our observations and simulations showed that depth-keeping behavior in high-frequency internal waves resulted in enhanced onshore transport at the top of the water column, and reduced offshore dispersal at the bottom, compared to being passive. Thus, even weak depth-keeping may allow larvae to reach nearshore adult habitats more reliably than drifting passively.

Microscopic-scale processes significantly influence benthic marine ecosystems such as coral reefs and kelp forests. Due to the ocean's complex and dynamic nature, it is most informative to study these processes in the natural environment yet it is inherently difficult. Here we present a system capable of non-invasively imaging seafloor environments and organisms in situ at nearly micrometre resolution. We overcome the challenges of underwater microscopy through the use of a long working distance microscopic objective, an electrically tunable lens and focused reflectance illumination. The diver-deployed instrument permits studies of both spatial and temporal processes such as the algal colonization and overgrowth of bleaching corals, as well as coral polyp behaviour and interspecific competition. By enabling in situ observations at previously unattainable scales, this instrument can provide important new insights into micro-scale processes in benthic ecosystems that shape observed patterns at much larger scales.