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Through the advancement of observation systems, our vision has far extended its reach into the world of fishes, and how they interact with fishing gears—breaking through physical boundaries and visually adapting to challenging conditions in marine environments. As marine sciences step into the era of artificial intelligence (AI), deep learning models now provide tools for researchers to process a large amount of imagery data (i.e., image sequence, video) on fish behavior in a more time-efficient and cost-effective manner. The latest AI models to detect fish and categorize species are now reaching human-like accuracy. Nevertheless, robust tools to track fish movements in situ are under development and primarily focused on tropical species. Data to accurately interpret fish interactions with fishing gears is still lacking, especially for temperate fishes. At the same time, this is an essential step for selectivity studies to advance and integrate AI methods in assessing the effectiveness of modified gears. We here conduct a bibliometric analysis to review the recent advances and applications of AI in automated tools for fish tracking, classification, and behavior recognition, highlighting how they may ultimately help improve gear selectivity. We further show how transforming external stimuli that influence fish behavior, such as sensory cues and gears as background, into interpretable features that models learn to distinguish remains challenging. By presenting the recent advances in AI on fish behavior applied to fishing gear improvements (e.g., Long Short-Term Memory (LSTM), Generative Adversarial Network (GAN), coupled networks), we discuss the advances, potential and limits of AI to help meet the demands of fishing policies and sustainable goals, as scientists and developers continue to collaborate in building the database needed to train deep learning models.

Acoustic waves are a carrier of information mainly in environments where the use of other types of waves, for example electromagnetic waves, is limited. The term acoustical imaging is widely used in the ultrasonic engineering to imaging areas in which the acoustic waves propagate. In particular, ultrasound is widely used in the visualization of human organs-ultrasonography (Nowicki, 2010). Expanding the concept, acoustical imaging can also be used to presentation (monitoring) the current state of sound intensity distribution leading to characterization of sources in observed underwater region. This can be represented in the form of an acoustic characteristic of the area, for example as a spectrogram. Knowledge of the underwater world which is built by analogy to the perception of the space on the Earth's surface is to be systematize in the form of images. Those images arise as a result of graphical representation of processed acoustic signals. In this paper, it is explained why acoustic waves are used in underwater imaging. Furthermore, the passive and active systems for underwater observation are presented. The paper is illustrated by acoustic images, most of them originated from our own investigation.

This article reviews remotely operated underwater vehicle (ROUV) and its different types focusing on the control systems. This study offers a brief introduction of unmanned underwater vehicle (UUV) together with ROUV. Underwater robots are designed to work as an alternative to humans because of a difficult and hazardous underwater environment. The applications and demand of marine robots are increasing with the passage of time. There are several research articles and publications available on these topics but, a complete review of old and recent research about this technology is still hard to find. This article also assesses some recently published research papers on underwater systems. It presents the comparison of different control systems and designs of underwater vehicles. There have been major developments in marine technology depending on the needs, applications and cost of different missions. Scientists design many remotely operated vehicles based on the educational or industrial purposes. This article is presented in order to help and assist the future researchers as a massive review of the field of remotely operated underwater vehicles and their possible future developments are presented.

GNSS-A (combination of Global Navigation Satellite System and Acoustic ranging) observations have provided important geophysical results, typically based on static GNSS-Acoustic positioning methods. Recently, continuous GNSS-Acoustic observations using a moored buoy have been attempted. Precise kinematic GNSS-Acoustic positioning is essential for these approaches. In this study, we developed a new kinematic GNSS-A positioning method using the extended Kalman filter (EKF). As for the observation model, parameters expressing underwater sound speed structure [nadir total delay (NTD) and underwater delay gradients] are defined in a similar manner to the satellite geodetic positioning. We then investigated the performance of the new method using both the synthetic and observational data. We also investigated the utility of a GNSS-Acoustic array geometry composed of multi-angled transponders for detection of vertical displacements. The synthetic tests successfully demonstrated that (1) the EKF-based GNSS-Acoustic positioning method can resolve the GNSS-Acoustic array displacements, as well as NTDs and underwater delay gradients, more precisely than those estimated by the conventional kinematic positioning methods and (2) precise detection of vertical displacements can be achieved using multi-angled transponders and EKF-based GNSS-Acoustic positioning. Analyses of the observational data also demonstrated superior performance of the EKF-based GNSS-Acoustic positioning method, when assuming a laterally stratified sound speed structure. Further, we found three superior aspects to the EKF-based array positioning method when using observational data: (1) robustness of the solutions when some transponders fail to respond, (2) precise detection for an abrupt vertical displacement, and (3) applicability to real-time positioning when sampling interval of the acoustic ranging is shorter than 30 min. The precision of the detection of abrupt steps, such as those caused by coseismic slips, is ~ 5 cm (1σ) using this method, an improvement on the precision of ~ 10 cm of conventional methods. Using the observational data, the underwater delay gradients and the horizontal array displacements could not be accurately solved even using the new method. This suggests that short-wavelength spatial heterogeneity exists in the actual ocean sound speed structure, which cannot be approximated using a simple horizontally graded sound speed structure.

In-situ visual observations of marine organisms is crucial to developing behavioural understandings and their relations to their surrounding ecosystem. Typically, these observations are collected via divers, tags, and remotely-operated or human-piloted vehicles. Recently, however, autonomous underwater vehicles equipped with cameras and embedded computers with GPU capabilities are being developed for a variety of applications, and in particular, can be used to supplement these existing data collection mechanisms where human operation or tags are more difficult. Existing approaches have focused on using fully-supervised tracking methods, but labelled data for many underwater species are severely lacking. Semi-supervised trackers may offer alternative tracking solutions because they require less data than fully-supervised counterparts. However, because there are not existing realistic underwater tracking datasets, the performance of semi-supervised tracking algorithms in the marine domain is not well understood. To better evaluate their performance and utility, in this paper we provide (1) a novel dataset specific to marine animals located at http://warp.whoi.edu/vmat/, (2) an evaluation of state-of-the-art semi-supervised algorithms in the context of underwater animal tracking, and (3) an evaluation of real-world performance through demonstrations using a semi-supervised algorithm on-board an autonomous underwater vehicle to track marine animals in the wild.

To address the problem of unreliable single-link underwater acoustic communication caused by large signal delays and strong multipath effects in shallow water environments, this paper proposes a distributed underwater acoustic diversity communication system (DUA-DCS). DUA-DCS employs a maneuverable distributed cross-medium buoy network to form multiple distributed, non-coherent, and parallel communication links. In the uplink, a receiving diversity processing mechanism of joint decision feedback equalizer embedded phase-locked loop and maximum signal-to-interference ratio combining (DFE-PLL-MSIRC) is proposed to achieve waveform-level diversity combining of underwater signals. A phase-locked loop module is embedded in each branch of the decision feedback equalizer to eliminate the residual frequency and phase errors after Doppler compensation. Meanwhile, the combining coefficients are determined based on the maximum signal-to-interference ratio criterion, taking into account the residual inter-symbol interference after equalization, resulting in efficient and accurate computation. Additionally, the combined decision values are fed back to the feedback filters in each branch to ensure more accurate feedback output. Simulation and lake experiment results demonstrate that, compared to the single-link communication system, DFE-PLL-MSIRC can achieve a diversity gain of more than 5.2 dB and obtain about 3 dB more diversity gain than the comparison algorithm. And the BER of DFE-PLL-MSIRC can be reduced by at least one order of magnitude, which is lower by at least 0.6 order of magnitude compared to the comparison algorithm. In the downlink, a transmitting diversity processing mechanism of complex orthogonal space-time block coding (COSTBC) is proposed. By utilizing a newly designed generalized complex orthogonal transmission matrix, complete transmission diversity can be achieved at the coding rate of 3/4. Compared to the single-link communication system, the system can achieve a diversity gain of more than 6 dB.

The depth-average velocity is routinely calculated using data from underwater gliders. The calculation is a dead reckoning, where the difference between the glider’s velocity over ground and its velocity through water yields the water velocity averaged over the glider’s dive path. Given the accuracy of global positioning system navigation and the typical 3–6-h dive cycle, the accuracy of the depth-average velocity is overwhelmingly dependent on the accurate estimation of the glider’s velocity through water. The calculation of glider velocity through water for the Spray underwater glider is described. The accuracy of this calculation is addressed using a method similar to that used with shipboard acoustic Doppler current profilers, where water velocity is compared before and after turns to determine a gain to apply to glider velocity through water. Differences of this gain from an ideal value of one are used to evaluate accuracy. Sustained glider observations of several years off California and Palau consisted of missions involving repeated straight sections, producing hundreds of turns. The root-mean-square accuracy of depth-average velocity is estimated to be in the range of 0.01–0.02 m s −1 , consistent with inferences from the early days of underwater glider design.

Wind speed measurements are needed to understand ocean–atmosphere coupling processes and their effects on climate. Satellite observations provide sufficient spatial and temporal coverage but are lacking adequate calibration, while ship- and mooring-based observations are spatially limited and have technical shortcomings. However, wind-generated underwater noise can be used to measure wind speed, a method known as Weather Observations Through Ambient Noise (WOTAN). Here, we adapt the WOTAN technique for application to ocean gliders, enabling calibrated wind speed measurements to be combined with contemporaneous oceanographic profiles over extended spatial and temporal scales. We demonstrate the methodology in three glider surveys in the Mediterranean Sea during winter 2012/13. Wind speeds ranged from 2 to 21.5 m s −1 , and the relationship to underwater ambient noise measured from the glider was quantified. A two-regime linear model is proposed, which validates a previous linear model for light winds (below 12 m s −1 ) and identifies a regime change in the noise generation mechanism at higher wind speeds. This proposed model improves on previous work by extending the validated model range to strong winds of up to 21.5 m s −1 . The acquisition, data processing, and calibration steps are described. Future applications for glider-based wind speed observations and the development of a global wind speed estimation model are discussed.

The exploration of underwater environments poses significant challenges due to the optical properties of water, leading to color distortion, reduced contrast and blurring in images. This work aims to enhance the clarity and fidelity of underwater images and videos in near real-time. The SeaThru physics-based color correction method was suitably adapted for obtaining target images across a diverse collection of underwater datasets considered. Based on these target images, the MIMO-UNet model is used to address the processing speed limitations of the physics-based correction methods, enabling near real-time image and video processing without explicit depth information. The proposed method has been integrated into autonomous underwater observation systems and remotely operated vehicle (ROV) cameras, offering enhanced visibility. Additionally, we build a MIMO-UNet network for generating realistic synthetic underwater images, valuable for training and simulation. This research advances underwater imaging enhancement and restoration, significantly improving visual data quality and vision-dependent tasks in submerged environments. The public release of the dataset aims to facilitate further research and development in this field.

Understanding the recent history of Thwaites Glacier, and the processes controlling its ongoing retreat, is key to projecting Antarctic contributions to future sea-level rise. Of particular concern is how the glacier grounding zone might evolve over coming decades where it is stabilized by sea-floor bathymetric highs. Here we use geophysical data from an autonomous underwater vehicle deployed at the Thwaites Glacier ice front, to document the ocean-floor imprint of past retreat from a sea-bed promontory. We show patterns of back-stepping sedimentary ridges formed daily by a mechanism of tidal lifting and settling at the grounding line at a time when Thwaites Glacier was more advanced than it is today. Over a duration of 5.5 months, Thwaites grounding zone retreated at a rate of >2.1 km per year—twice the rate observed by satellite at the fastest retreating part of the grounding zone between 2011 and 2019. Our results suggest that sustained pulses of rapid retreat have occurred at Thwaites Glacier in the past two centuries. Similar rapid retreat pulses are likely to occur in the near future when the grounding zone migrates back off stabilizing high points on the sea floor. The Thwaites Glacier grounding zone has experienced sustained pulses of rapid retreat over the past two centuries, according to sea floor observations obtained by an autonomous underwater vehicle.