Oceanographic Research Articles

The Strait and the Sea Unsettling Scores

In this inquiry I build on my previous work on contemporary aesthetics to engage the media practices and bio-acoustic mappings of cetacean scientists, as well as fishers’ video diaries and sound cartographies of interactions with cetaceans. To these, I also juxtapose a contemporary composer, sound engineer, and media artist’s sonographic records of encounters with these decidedly intriguing marine mammals alas too often reduced to the moniker “charismatic”. Concretely, I zoom in on three media practices: the sound compositions of Ariel Guzik, founder of the Nature Expression and Resonance Lab, who designs and engineers instruments to communicate with grey whales, sperm whales, and bottle nose dolphins in the Sea of Cortez; the hydrophone recordings, spectrograms, drone footage, and photo-identifications by a team of cetacean ethologists and communication scientists who run a research vessel and a marine ecology lab in the Tyrrhenian Sea; and the multimedia online platforms of fishers who produce video diaries of potentially fatal interactions with orcas in the Strait of Gibraltar. These carefully selected and juxtaposed audiovisual practices are the graphic, acoustic, and musical fragments that compose the de-territorialized cartography I am calling The Strait and The Sea.

Improving deep learning based bluespotted ribbontail ray (Taeniura Lymma) recognition

This paper presents the novel task of bluespotted ribbontail (BR) ray (Taeniura lymma) recognition using deep learning based computer vision methods to enable the identification of specific individuals of this species. Mapping the specific individuals in relation to location and time will allow marine researchers to understand their movement patterns, habitat choice, life span, size of the population and more – data which could allow monitoring and establishing a tailor-made conservation plan for this species.Our work is pioneer on this recognition problem. We give a detailed description of the three basic steps of detection, feature extraction and recognition in this vision problem and perform experiments to explore the system configuration and what improves the performance. A feature extraction enhancement as well as a crucial effect of a split into different main poses are demonstrated. Though the precision results achieved in this paper are still moderate and should be further improved, they are nevertheless promising and reasonable for practical use if the six best matches are chosen. For this scenario, almost 85% precision for upper-pose model, and almost 80% precision for left- and right-pose models, are achieved demonstrating the feasibility of the pipeline suggested as well as opportunities for improvement.

Jellyfish-Inspired Visual and Sensory Bubbling Robots with Automatic 3D Morphable Films for Underwater Environmental Interactions.

Coelenterates, such as Atolla jellyfish, are capable of integrating color, communication, and motion in a sophisticated manner, thereby enabling them to function as intelligent biological systems that can adapt to the challenges of the underwater environment. Extensive efforts have been dedicated to exploiting underwater visual, sensory, actuating, or combined systems. However, current biomimetic soft systems are still limited by the lack of comprehensive, integrated functional skins that can automatically deform, dynamically sense, and further send color signals when diving into underwater conditions. Here, we propose the synthetic soft skins composed of assembled entangled carbon nanotube networks and fluorescent unit-embedded elastomers which can be applied in a suspended form to allow autonomic 3D deformation, real-time perception, and dynamic fluorescence color transformation. The capabilities of the sensory and color display thresholds were controlled through the entanglement density of carbon nanotubes and the suspended area. As a demonstration, the soft thin skin was integrated into an artificial jellyfish robot, enabling the realization of a closed-loop feedback system for dynamic sensory processing, signal processing, and further 3D morphing-induced fluorescent color change, demonstrating significant potentials in underwater visual display, danger warning, and environmental exploration.

Research horizons for invasive marine species detection with eDNA/eRNA

The global marine ecosystem is changing rapidly as the result of biogeochemical cycles and ecosystem structure being altered by industrial civilization. Invasive marine species (IMS) are one of the most damaging regional consequences of human activity, and one of the most easily attributable to specific processes. This makes IMS introduction one of most tractable threats for management by appropriate policies. Once established, a different set of policies are required either to restrict IMS spread, or to attempt local eradication. The key ecosystem management tool for IMS damage mitigation is rapid, widely deployable IMS detection. Environmental Nucleic Acids (eNA), combining environmental DNA (eDNA) and environmental RNA (eRNA) analyses, have emerged as valuable tools for sensitive, cost-effective and readily deployable detection of IMS. Methods for IMS detection by eNA are still being developed through a widespread and active research community, so identifying the limitations of current processes will help prioritise eNA-based IMS detection research. We analysed and synthesised the opinions of expert marine ecosystem managers and researchers in Australia and New Zealand about the knowledge gaps and research needs for eNA-based IMS detection. This synthesis was placed in context with current research literature on what eNA technologies are currently providing as an IMS management tool; what problems exist with the current technology; and what could be done to improve this general approach. Our analyses produced a list of priorities that chart a path towards the best possible systems for IMS detection by eNA.

Overview of underwater optical wireless communication positioning based on signal processing

Underwater Optical Wireless Communication (UWOC) stands as a pivotal technology in the realm of data transmission in challenging aquatic environments. Despite its importance, UWOC systems grapple with a plethora of interference issues that have the potential to severely compromise their performance. This abstract delves into the realm of anti-interference techniques in the domain of UWOC, with a particular focus on the utilization of signal processing methods. The narrative commences by underscoring the significance of UWOC systems and the vast array of applications they support, spanning diverse fields such as oceanography, environmental monitoring, and underwater exploration. This underscores the pressing need for robust solutions to mitigate the various forms of interference that can hinder effective data transmission in underwater scenarios. The article proceeds by introducing the various types of UWOC and delves into their current research status, with a specific emphasis on the ongoing evolution and emerging trends in the UWOC field. It then provides detailed insights into several prevalent UWOC positioning methods, outlining their distinct characteristics and the specific application contexts where they excel. Additionally, the article highlights the existing challenges and prospects for the future development of UWOC positioning technology, intending to offer valuable guidance to prospective researchers in this dynamic and critical area of underwater communication.

Clouddrift: a Python package to accelerate the use of Lagrangian data for atmospheric, oceanic, and climate sciences

Clouddrift: a Python package to accelerate the use of Lagrangian data for atmospheric, oceanic, and climate sciences

Inductive Wireless Power Transfer for Autonomous Underwater Vehicles using Split Core Transformer and Resonant LLC Circuit

Unmanned Underwater Vehicles (UUVs) are pivotal for underwater exploration and maintenance. Autonomous Underwater Vehicles (AUVs), with their potential to reduce operational time and environmental impact, are gaining increased interest. However, they face important technological challenges, particularly in power supply. This study focuses into the application of Inductive Wireless Power Transfer (IWPT) for continuous AUV operation, employing tightly coupled split core transformers (SCT) designed for near-field power transfer. Robust isolation and alignment mechanisms are proposed to overcome the effects of seawater environment. An IWPT device with an SCT and resonant LLC circuit is simulated and experimentally tested. Finite Element Method studies highlight the advantage of isolating the device from the seawater environment, especially at high frequencies. LLC simulation and experimental results demonstrate an efficiency in power transfer of 93.2 % and 87.1 %, respectively, transferring up to 312 W. However, global efficiency of the experimental device drops to 76.4 %, highlighting the need for optimizations in circuit design.

A survey on node localization technologies in UWSNs: Potential solutions, recent advancements, and future directions

SummaryLocation‐based underwater communication applications such as strategic surveillance, disaster prevention, marine research, and mine detection have given the field of underwater wireless sensor networks (UWSN) a head start. Node localization is a prerequisite for accurate data collection, target monitoring, and network management in UWSNs. However, the unique characteristics of the underwater environment, such as signal attenuation, multipath propagation, and variable acoustic properties, pose a major challenge to effective node localization. Accurate sensor node location data is essential for successful underwater data collection, but difficult to achieve as the GPS system cannot be used in an underwater environment. In this paper, existing node localization techniques such as ALS, SLUM, MASL, SLMP, UDB, USP, etc., and recent advances such as the fusion of range‐based and range‐free techniques, the fusion of RSSI and AoA to improve localization accuracy by using directional information in addition to signal strength, and the use of optimization techniques such as PSO, COA, and WOA algorithms to improve the accuracy of the applied node localization algorithm, e.g., TP‐TSFLA, and challenges related to UWSN are discussed. Also, different localization algorithms that affect the accuracy of UWSN localization techniques have been evaluated and compared with NS2 in terms of localization error, localization coverage, energy consumption, and average communication cost metrics. In addition, this paper also provides an up‐to‐date investigation of localization techniques. Finally, the tools available for simulation are presented, followed by open research questions that need to be addressed in the localization of nodes.

Underwater Mapping and Optimization Based on Multibeam Echo Sounders

Multibeam echo sounders (MBESs) enable extensive underwater environment exploration. However, due to weak correlation between adjacent multibeam sonar data and difficulties in inter-frame feature matching, the resulting underwater mapping accuracy frequently falls short of the desired level. To address this issue, this study presents the development of a multibeam data processing system, which includes functionalities for sonar parameter configuration, data storage, and point cloud conversion. Subsequently, an Iterative Extended Kalman Filter (iEKF) algorithm is employed for odometry estimation, facilitating the initial construction of the point cloud map. To further enhance mapping accuracy, we utilize the Generalized Iterative Closest Point (GICP) algorithm for point cloud registration, effectively merging point cloud data collected at different times from the same location. Finally, real-world lake experiments demonstrate that our method achieves an Absolute Trajectory Error (ATE) of 15.10 m and an average local point cloud registration error of 0.97 m. Furthermore, we conduct measurements on various types of artificial targets. The experimental results indicate that the average location error of the targets calculated by our method is 4.62 m, which meets the accuracy requirements for underwater target exploration.

NUAM-Net: A Novel Underwater Image Enhancement Attention Mechanism Network

Vision-based underwater exploration is crucial for marine research. However, the degradation of underwater images due to light attenuation and scattering poses a significant challenge. This results in the poor visual quality of underwater images and impedes the development of vision-based underwater exploration systems. Recent popular learning-based Underwater Image Enhancement (UIE) methods address this challenge by training enhancement networks with annotated image pairs, where the label image is manually selected from the reference images of existing UIE methods since the groundtruth of underwater images do not exist. Nevertheless, these methods encounter uncertainty issues stemming from ambiguous multiple-candidate references. Moreover, they often suffer from local perception and color perception limitations, which hinder the effective mitigation of wide-range underwater degradation. This paper proposes a novel NUAM-Net (Novel Underwater Image Enhancement Attention Mechanism Network) that addresses these limitations. NUAM-Net leverages a probabilistic training framework, measuring enhancement uncertainty to learn the UIE mapping from a set of ambiguous reference images. By extracting features from both the RGB and LAB color spaces, our method fully exploits the fine-grained color degradation clues of underwater images. Additionally, we enhance underwater feature extraction by incorporating a novel Adaptive Underwater Image Enhancement Module (AUEM) that incorporates both local and long-range receptive fields. Experimental results on the well-known UIEBD benchmark demonstrate that our method significantly outperforms popular UIE methods in terms of PSNR while maintaining a favorable Mean Opinion Score. The ablation study also validates the effectiveness of our proposed method.

Break Out of the Sea Surface: Genetic Algorithm based Search Path Planning Model for Locating and Searching Submersible Lost in Deep Sea

Deep-sea tourism and exploration, which require regulatory approval due to safety concerns, are burgeoning entertainment activities. Our study introduces a probabilistic model for locating missing submersibles to facilitate these approvals. The model predicts the submersible's location based on Newton's second law, analyzing three potential malfunction scenarios. It incorporates dynamics to account for variations in water flow and terrain, using a normal distribution to handle random location fluctuations. To address the complexities of deep-sea search and rescue, we propose a method that integrates detailed equipment selection, suited to the extreme conditions. Furthermore, we develop a discrete grid-based model for search path planning. This model simplifies path planning into a sorting optimization problem by connecting grid centers and calculating the probability integral for each grid. Our research also discusses the scalability of our positioning and search models, crucial for adapting to various deep-sea environments. We demonstrate the efficacy of our model through simulations and calculations, proving its potential to enhance safety and efficiency in deep-sea tourism projects. This comprehensive approach ensures that our probabilistic model not only predicts the submersible's location but also optimizes the search process, addressing both regulatory and practical challenges in the field of underwater exploration.

Environmental Care: How Marine Scientists Relate to Environmental Changes

AbstractMarine scientists have reported drastic environmental changes in marine and polar regions as a result of climate change. The changes range from species compositions in coastal regions and the deep-sea floor, the degradation of water and ice quality to the ever-growing plastic pollution affecting marine habitats. Marine scientists study these changes in their fieldwork, and communicate their findings in scientific publications. Some also rally in protests for the necessity of political programs to tackle changes. Based on ethnographic visits and interviews with marine scientists, this study examines how marine scientists experience and act on environmental changes as individuals and as collectives. In order to analyze their experiences and actions, I use the notion of care and portray care in different times and spaces, from work to protest. I demonstrate how care needs to be situated in different times and spaces, how care is embedded in a complex relationship of institutional requirements and structural demands that researchers experience, but also how care receives institutionalization and has an impact on research interests and agendas. In doing so, I show the social and epistemic consequences of care, opening up a view of individual and collective care in the marine sciences.

Feasibility study of underwater drilling robot based on razor clam

Razor acis lives in the low tidal area of the inner bay and thrives in the area with mild infiltration of fresh water. It uses its rhythmic contraction and foot burp and its unique anatomy to make holes. The extraordinary ability of razor clams to effectively penetrate the sediment has inspired innovation in the design of underwater drilling robots. Underwater drilling robots are currently widely used in the industrial world, such as underwater geological exploration, underwater shipwreck exploration, etc. However, the existing models of these robots mainly have a rigid structural design, which limits the flexibility of their drilling components, while their rough appearance also hinders their drilling efficiency. In response to these limitations, this paper proposes a robot with a soft, flexible body, and other enhanced functions designed to simulate a soft razor clam, replicate its peristaltic motion, minimize surface friction, and thus achieve superior drilling capability. This paper focuses on the development of software, segmented structure design, and studies designed to reduce surface friction and explore the mechanism of biomimetic motion.

Footprints and Contributions of “Ten-O-Maru”: The First Marine Research Ship in Japan

Footprints and Contributions of “Ten-O-Maru”: The First Marine Research Ship in Japan

Sea Grant’s Community Engaged Internship: expanding participation and cultivating belonging in coastal and ocean sciences

Sea Grant’s Community Engaged Internship: expanding participation and cultivating belonging in coastal and ocean sciences

CORAL-Catamaran for Underwater Exploration: Development of a Multipurpose Unmanned Surface Vessel for Environmental Studies.

CORAL (Catamaran fOr UndeRwAter expLoration) is a compact, unmanned catamaran-type vehicle designed and developed to assist the scientific community in exploring marine areas such as inshore regions that are not easily accessible by traditional vessels. This vehicle can operate in different modalities: completely autonomous, semi-autonomous, or remotely assisted by the operator, thus accommodating various investigative scenarios. CORAL is characterized by compact dimensions, a very low draft and a total electric propulsion system. The vehicle is equipped with a single echo-sounder, a 450 kHz Side Scan Sonar, an Inertial Navigation System assisted by a GPS receiver and a pair of high-definition cameras for recording both above and below the water surface. Here, we present results from two investigations: the first conducted in the tourist harbour in Pozzuoli Gulf and the second in the Riomaggiore-Manarola marine area within the Cinque Terre territory (Italy). Both surveys yielded promising results regarding the potentiality of CORAL to collect fine-scale submarine elements such as anthropic objects, sedimentary features, and seagrass meadow spots. These capabilities characterize the CORAL system as a highly efficient investigation tool for depicting shallow bedforms, reconstructing coastal dynamics and erosion processes and monitoring the evolution of biological habitats.

The State of Marine Social Science: Yesterday, Today, and into the Future.

Rapidly changing ocean conditions are resulting in changes in marine species and across entire ecosystems that, in turn, affect communities and individuals who rely on these resources for their livelihoods, culture, and sustenance. Marine social science, an emerging field that embraces diverse methods to understand human-ocean relationships, is increasingly called on to contribute to transdisciplinary ocean science that can inform the evidence-based policy and management needed to address these changes. Here, we review the state of marine social science as a growing field of study. First, we outline the history of marine social science, including the emergence of the field and the social science disciplines and community it encompasses. We then discuss current marine social science research themes as a framework to understand key ocean issues, which is followed by a commentary on the future of marine social science research.

Underwater Side-Scan Sonar Target Detection: YOLOv7 Model Combined with Attention Mechanism and Scaling Factor

Side-scan sonar plays a crucial role in underwater exploration, and the autonomous detection of side-scan sonar images is vital for detecting unknown underwater environments. However, due to the complexity of the underwater environment, the presence of a few highlighted areas on the targets, blurred feature details, and difficulty in collecting data from side-scan sonar, achieving high-precision autonomous target recognition in side-scan sonar images is challenging. This article addresses this problem by improving the You Only Look Once v7 (YOLOv7) model to achieve high-precision object detection in side-scan sonar images. Firstly, given that side-scan sonar images contain large areas of irrelevant information, this paper introduces the Swin-Transformer for dynamic attention and global modeling, which enhances the model’s focus on the target regions. Secondly, the Convolutional Block Attention Module (CBAM) is utilized to further improve feature representation and enhance the neural network model’s accuracy. Lastly, to address the uncertainty of geometric features in side-scan sonar target features, this paper innovatively incorporates a feature scaling factor into the YOLOv7 model. The experiment initially verified the necessity of attention mechanisms in the public dataset. Subsequently, experiments on our side-scan sonar (SSS) image dataset show that the improved YOLOv7 model has 87.9% and 49.23% in its average accuracy (mAP0.5) and (mAP0.5:0.95), respectively. These results are 9.28% and 8.41% higher than the YOLOv7 model. The improved YOLOv7 algorithm proposed in this paper has great potential for object detection and the recognition of side-scan sonar images.

Discovering DIY oceanography: building floats to track deep ocean currents

Below the surface of the ocean, currents, eddies, and other physical processes redistribute energy and biologically important elements. Measuring this transport can be challenging, and scientists must address a wide spectrum of oceanographic and engineering concepts when designing instruments to observe these processes. In this activity, high-school aged students are asked to consider some of these challenges by using easily-available material to design, assemble, and test their own simple deep-water floats, mimicking a tool used by oceanographers to measure transport below the ocean’s surface. Students are asked to build floats that are neutrally buoyant at an interface between oil and water, teaching them key oceanographic concepts such as buoyancy and ocean stratification. Additionally, students manage a budget by selecting cost-effective materials to construct their floats. This activity aims to increase students’ understanding of how ocean currents affect our planet’s climate and ecosystems, how we observe these phenomena, and some of the practical challenges that oceanographers face. The project supports key educational standards and aims to deepen students’ appreciation for marine science.

Marine information-seeking behaviours and AI chatbot impact on information discovery

Purpose This research investigates students’ information-seeking behaviours at the Indian Maritime University (IMU) and assesses the impact of AI chatbots on their marine science knowledge and awareness. The study aims to provide insights into the role of AI-driven solutions in enhancing knowledge sharing and the challenges faced in using AI tools for marine information retrieval. Design/methodology/approach The study used a stratified random sampling method, encompassing 152 respondents from IMU’s B.Sc. in Nautical Science and B. Tech in Marine Engineering programs. Data collection involved a structured electronic survey questionnaire. The analysis encompassed descriptive statistics using SPSS. Findings Information needs were met through diverse channels, with 57.9% of respondents using AI-driven chatbots for marine information retrieval. AI significantly recommended research papers (61.8%). The chatbot positively impacted marine science awareness and knowledge, with a mean satisfaction rating of approximately 3.3. Challenges included insufficient access to AI tools, data privacy concerns and accuracy issues. Originality/value This study contributes original insights into the information-seeking behaviours of marine students at IMU and the impact of AI chatbots on their knowledge and awareness. It highlights the multifaceted nature of marine information retrieval, the effectiveness of AI-driven solutions in enhancing knowledge sharing and the challenges that need to be addressed for the broader adoption of AI tools in this context.