Ocean Observations Research Articles

High-Sensitivity Seawater Refraction Index Optical Measurement Sensor Based on a Position-Sensitive Detector.

The refractive index of seawater is one of the essential parameters in ocean observation, so it is necessary to achieve high-precision seawater refractive index measurements. In this paper, we propose a method for measuring the refractive index of seawater, based on a position-sensitive detector (PSD). A theoretical model was established to depict the correlation between laser spot displacement and refractive index change, utilizing a combination of a position-sensitive detector and laser beam deflection principles. Based on this optical measurement method, a seawater refractive index measurement system was established. To effectively enhance the sensitivity of refractive index detection, a focusing lens was incorporated into the optical path of the measuring system, and simulations were conducted to investigate the impact of focal length on refractive index sensitivity. The calibration experiment of the measuring system was performed based on the relationship between the refractive index of seawater and underwater pressure (depth). By measuring laser spot displacement at different depths, changes in displacement, with respect to both refractive index and depth, were determined. The experimental results demonstrate that the system exhibits a sensitivity of 9.93×10-9 RIU (refractive index unit), and the refractive index deviation due to stability is calculated as ±7.54×10-9 RIU. Therefore, the feasibility of this highly sensitive measurement of seawater refractive index is verified. Since the sensitivity of the refractive index measurement of this measurement system is higher than the refractive index change caused by the wake of underwater vehicles, it can also be used in various applications for underwater vehicle wake measurement, as well as seawater refractive index measurement, such as the motion state monitoring of underwater navigation targets such as AUVs and ROVs.

The Inversion of Three-Dimensional Ocean Temperature and Salinity Fields for the Assimilation of Satellite Observations

The lack of dimensionality of ocean observations makes it difficult to utilize multi-scale data assimilation to correct model errors with limited observations. Since satellite observations can provide high-resolution and time-continuous sea surface information, this study utilizes sea surface temperature (SST), sea surface salinity (SSS), and sea surface height (SSH) anomalies to invert the vertical temperature and salinity fields and thus realize multi-scale data assimilation in the three-dimensional space. We propose a temperature and salinity inversion network (TSI-Net) for reconstructing the mapping of the sea surface’s spatial distribution features to vertical structural features to obtain pseudo-observed fields. In this study, measured satellite remote-sensing data and temperature and salinity profiles are used to correct the model errors in the waters around the China Sea. The sensitivity analysis shows that the multi-component inversion can better fit the temperature field relationship, with a correlation coefficient of about 0.87. The results of the assimilation experiments show that the analytical field obtained by assimilating the pseudo-observed field is more consistent with the target field in terms of the spatial distribution characteristics.

Based on Deep Reinforcement Learning to path planning in uncertain ocean currents for Underwater Gliders

This paper studies the application of Deep Reinforcement Learning (DRL) in the field of underwater glider (UG) formation path planning and solves the path planning of glider formation in uncertain ocean currents. Facing the uncertain ocean current environment, based on the observation and collection of ocean current data by the overall ocean observation system, the glider formation needs to have the ability of path planning. The original DRL algorithm network architecture is simple, and the feature extraction ability is limited. According to the characteristics of ocean current and path planning, a Deep Convolutional Neural Network for uncertain ocean current environment (Duo-CNN) network architecture with a dual-branch structure is proposed. Based on the DRL, the glider model and the interaction between the glider and the environment are used to guide the path planning through the implicit prediction of the known ocean current data. A solution for formation construction, obstacle avoidance, and path planning of glider formation in a dynamic uncertain ocean current environment is provided. The simulation verifies the effectiveness of the path planning of the glider formation using the DRL of the dual-branch Duo-CNN architecture. Compared with other architectures with feature extraction ability, Duo-CNN has advantages.

Estimating the mixed layer depth of the global ocean by combining multisource remote sensing and spatiotemporal deep learning

ABSTRACT Estimating the ocean mixed layer depth (MLD) is crucial for studying the atmosphere-ocean interaction and global climate change. Satellite observations can accurately estimate the MLD over large scales, effectively overcoming the limitation of sparse in situ observations and reducing uncertainty caused by estimation based on in situ and reanalysis data. However, combining multisource satellite observations to accurately estimate the global MLD is still extremely challenging. This study proposed a novel Residual Convolutional Gate Recurrent Unit (ResConvGRU) neural networks, to accurately estimate global MLD along with multisource remote sensing data and Argo gridded data. With the inherent spatiotemporal nonlinearity and dependence of the ocean dynamic process, the proposed method is effective in spatiotemporal feature learning by considering temporal dependence and capturing more spatial features of the ocean observation data. The performance metrics show that the proposed ResConvGRU outperforms other well-used machine learning models, with a global determination coefficient (R2) and a global root mean squared error (RMSE) of 0.886 and 17.83 m, respectively. Overall, the new deep learning approach proposed is more robust and advantageous in data-driven spatiotemporal modeling for retrieving ocean MLD at the global scale, and significantly improves the estimation accuracy of MLD from remote sensing observations.

Forecast-based stochastic optimization for a load powered by wave energy

Stand-alone renewable energy systems, which harness and use electricity without connection to a distribution grid, can benefit from using resource forecasts when making real-time decisions on how to manage power. An example of this is an ocean observation system that uses a wave energy converter (WEC) and battery bank to produce and store electricity in order to meet electrical load requirements for oceanographic sensors and instrumentation. By using wave forecasts to optimize the power consumption of this system, it is possible to reduce the size of the WEC and battery bank needed, thereby reducing the overall system’s cost and complexity. In this paper, we assess the benefits of forecasting for a generic wave-powered ocean observation system. We model an observation platform powered by a WEC and battery-storage system that can switch between four power states, full power: 600 W, medium power: 450 W, low power: 45 W, and survival mode: 1 W. To determine which power state to enter over time, we present a stochastic optimization method that interprets wave forecasts and system information to select a power state on an hourly basis and simulate over two months of consecutive decisions. Using this simulation framework, we compare 8 power management strategies across a range of WEC sizes (3–5 m) and battery capacities (2.5–35 kWh). We find that it is possible to maintain full (600 W) power consumption over the entire simulation window with a 5 m diameter WEC and 25 kWh battery bank, which is on the upper end of the ranges evaluated. To employ a smaller WEC or battery, load flexibility is required. We find that forecast-based methods for this power management handle tradeoffs in performance (e.g., cumulative power consumption, reducing intermittencies and meeting scheduled load targets) more effectively than power management strategies that do not use forecasting. Overall, our results indicate that forecasting for our stand-alone renewable energy system is most impactful when some loads must be met on or within a defined schedule.

Best practices recommendations for estimating dissipation rates from shear probes

As a part of the Scientific Committee on Oceanographic Research (SCOR) Working Group #160 “Analyzing ocean turbulence observations to quantify mixing” (ATOMIX), we have developed recommendations on best practices for estimating the rate of dissipation of kinetic energy, ε, from measurements of turbulence shear using shear probes. The recommendations provided here are platform-independent and cover the conceivable range of dissipation rates in the ocean, seas, and other natural waters. They are applicable to commonly deployed platforms that include vertical profilers, fixed and moored instruments, towed profilers, submarines, self-propelled ocean gliders, and other autonomous underwater vehicles. The procedure for preparing the shear data for spectral estimation is discussed in detail, as are the quality control metrics that should accompany each estimate of ε. The methods are illustrated using a high-quality ‘benchmark’ dataset, while potential pitfalls are demonstrated with a second dataset containing common faults.

Deconstructing co-occurring marine heatwave and phytoplankton bloom events in the Arkona Sea in 2018

Between May and August 2018, two separate marine heatwaves (MHWs) occurred in the Arkona Sea in the western Baltic Sea. These heatwaves bookended an extended period of phytoplankton growth in the region. Data from the Ocean and Land Colour Instrument (OLCI) on board the European Sentinel-3 satellite revealed an eddy-like structure containing high chlorophyll a (Chl-a) concentrations (ca. 25 mg.m-3) persisting for several days at the end of May in the Arkona Sea. Combining ocean colour observations, a coupled bio-optical ocean model and a particle tracking model, we examined the three dimensional relationship between these co-occurring MHW and phytoplankton bloom events. We find that the onset of the MHW in May provided the optimal conditions for phytoplankton growth, i.e. sufficient light and nutrients. Wind-driven surface eddy circulation, geostrophic eddy stirring and transient submesoscale dynamics along the edges of the eddy provided a transport path for nutrient fluxes and carbon export, and helped to sustain the phytoplankton bloom. The bloom may have indirectly had an enhancing effect on the MHW, through the impact of water constituent-induced heating rates on air-sea energy fluxes. The subsurface signature of the MHW plays a critical role in de-coupling surface and subsurface dynamics and terminating the phytoplankton bloom. Subsurface temperature anomalies of up to 8°C between 15 and 20 m depth are found to persist up to 15 days after the surface signature of the MHW has disappeared. The study reveals how surface and subsurface dynamics of MHWs and phytoplankton blooms are connected under different environmental conditions. It extends our knowledge on surface layer processes obtained from satellite data.

Neodymium isotope composition and rare earth element distribution of East Antarctic continental shelf and deep waters

Circumpolar Deep Water (CDW) and Antarctic Bottom Water (AABW) play key roles in the Earth's climate system. Both water masses form critical components of the Antarctic Circumpolar Current and Meridional Overturning Circulation and therefore directly influence the large-scale redistribution of heat, nutrients and carbon. Reconstruction of past CDW transport and AABW production and export has been a key target in palaeoceanography. One promising proxy to achieve this has been the neodymium (Nd) isotope composition of seawater. The biogeochemical processes controlling Nd in the ocean, however, remain underconstrained, and modern observations of Nd isotopes in the Southern Ocean are still geographically limited.To overcome this limitation, 61 seawater samples were collected for Nd isotope and rare earth element (REE) analysis at nine stations along the Wilkes Land continental margin and in the Australian-Antarctic Basin (65°S 125°E) near East Antarctica. The results show that the different water masses have the following Nd isotope characteristics: Antarctic Surface Water (AASW), εNd = −9.0 ± 1.0 (2SD; n = 22); Modified CDW (MCDW), εNd = −8.8 ± 0.8 (2SD; n = 22); AABW, εNd = −8.3 ± 0.5 (2SD; n = 17).There is no evidence of continental REE inputs to surface waters on the Wilkes Land margin. Observed zonal variability of Nd isotope composition in AASW can be attributed to seasonal competition between the poleward flow of warm AASW from the AAG and the westward export of cold surface shelf waters by the Antarctic Slope Current.In terms of deep and bottom waters, mixing of upwelled CDW with AASW and AABW exclusively controls the Nd isotope composition of MCDW, with no indication of boundary processes modifying the Nd isotope composition of MCDW as it encroaches the shelf or slope. The regional Nd isotope signature for AABW is intermediate between published data for the Atlantic sector AABW (εNd = −9.1 ± 0.7) and Pacific sector AABW (εNd = −7.4 ± 0.9). In the absence of active convection, this Nd isotope signature can be explained by a mixture of advected AABW from the Adélie Land coast with a more dominant component of local MCDW and CDW.The data presented in this study constitute the first Nd isotope data set in this part of the Southern Ocean and suggest conservative behaviour of Nd in proximity to the old East Antarctic continent.

Underwater small target detection based on dynamic convolution and attention mechanism

In ocean observation missions, unmanned autonomous ocean observation platforms play a crucial role, with precise target detection technology serving as a key support for the autonomous operation of unmanned platforms. Among various underwater sensing devices, side-scan sonar (SSS) has become a primary tool for wide-area underwater detection due to its extensive detection range. However, current research on target detection with SSS primarily focuses on large targets such as sunken ships and aircraft, lacking investigations into small targets. In this study, we collected data on underwater small targets using an unmanned boat equipped with SSS and proposed an enhancement method based on the YOLOv7 model for detecting small targets in SSS images. First, to obtain more accurate initial anchor boxes, we replaced the original k-means algorithm with the k-means++ algorithm. Next, we replaced ordinary convolution blocks in the backbone network with Omni-dimensional Dynamic Convolution (ODConv) to enhance the feature extraction capability for small targets. Subsequently, we inserted a Global Attention Mechanism (GAM) into the neck network to focus on global information and extract target features, effectively addressing the issue of sparse target features in SSS images. Finally, we mitigated the harmful gradients produced by low-quality annotated data by adopting Wise-IoU (WIoU) to improve the detection accuracy of small targets in SSS images. Through validation on the test set, the proposed method showed a significant improvement compared to the original YOLOv7, with increases of 5.05% and 2.51% in mAP@0.5 and mAP@0.5: 0.95 indicators, respectively. The proposed method demonstrated excellent performance in detecting small targets in SSS images and can be applied to the detection of underwater mines and small equipment, providing effective support for underwater small target detection tasks.

Accuracy Evaluation of Ocean Wave Spectra from Sentinel-1 SAR Based on Buoy Observations and ERA5 Data

Doppler mis-registrations in azimuth can lead to ocean waves shorter than a specific wavelength being undetectable by SAR. In order to evaluate the actual ocean wave observation ability, the accuracy of Sentinel-1 SAR ocean wave spectra from January 2016 to December 2021 is evaluated by comparisons to NDBC buoys, ERA5 wave height, and CMEMS buoys. The results compared with NDBC show that the spectral shape of Sentinel-1 SAR ocean wave spectra is accurate, while the spectral values need to be improved. The wave spectra of Sentinel-1 have the best observations in season autumn. The comparison results of total wave height show the RMSE and bias are 0.91 m and −0.52 m for the comparisons to NDBC buoy wave spectra data, 0.93 m and −0.68 m for the comparison to ERA5 wave height data, and 0.9 m and −0.35 m for the comparisons to CMEMS buoy data. The comparison results of wave height in different wind speeds and areas shows that the accuracy of Sentinel-1 wave mode data is relatively good in the open ocean located in middle and low latitude area under the medium wind speed, while those are relatively poor in high latitude areas or the areas with excessively high or low wind speed.

A Fully Self-Powered Ocean Wave Observation System Empowered by Natural Light Modulated by a Friction-Driven Polymer Network Liquid Crystal.

Conventional ocean wave observation instruments are powered by batteries, limiting the continuous observation time. Besides, the waste of batteries brings environmental contaminations. Triboelectric nanogenerators (TENGs) can reveal ocean wave information through their electrical output, taking the triboelectric charge as the information carrier. However, charge amplification is necessary, consuming additional energy. Herein, taking the photons rather than electrons as the information carrier, we developed a fully self-powered natural light-enabled sensing system for ocean wave monitoring by coupling two rotary-freestanding sliding TENGs (RFS-TENGs) and a polymer network liquid crystal (PNLC)-triggered optical system. The natural light is modulated by the PNLC driven by ocean wave-induced friction. With the assistance of a one-way bearing, the rise and fall of the wave will trigger different RFS-TENGs to power the PNLC in different voltage drops, leading to different transmitted natural light intensities. The wave height information can be obtained through the number of pulse signals with the same trough light intensity, while the wave period can be obtained through the duration between the same two sets of pulse signals. The effectiveness of the developed sensing paradigm in practical applications was verified by flume-based experiments, with the highest accuracies of 90.7% in wave height and 99.8% in wave period.

Quantifying potential marine debris sources and potential threats to penguins on the West Antarctic Peninsula

Marine pollution is becoming ubiquitous in the environment. Observations of pollution on beaches, in the coastal ocean, and in organisms in the Antarctic are becoming distressingly common. Increasing human activity, growing tourism, and an expanding krill fishing industry along the West Antarctic Peninsula all represent potential sources of plastic pollution and other debris (collectively referred to as debris) to the region. However, the sources of these pollutants from point (pollutants released from discrete sources) versus non-point (pollutants from a large area rather than a specific source) sources are poorly understood. We used buoyant simulated particles released in a high-resolution physical ocean model to quantify pollutant loads throughout the region. We considered non-point sources of debris from the Antarctic Circumpolar Current, Bellingshausen Sea, Weddell Sea, and point source pollution from human activities including tourism, research, and fishing. We also determined possible origins for observed debris based on data from the Southern Ocean Observing System and Palmer Long-Term Ecological Research program. Our results indicate that point source pollution released in the coastal Antarctic is more likely to serve as a source for observed debris than non-point sources, and that the dominant source of pollution is region-specific. Penguin colonies in the South Shetland and Elephant Islands had the greatest debris load from point sources whereas loads from non-point sources were greatest around the southernmost colonies. Penguin colonies at Cornwallis Island and Fort Point were exposed to the highest theoretical debris loads. While these results do not include physical processes such as windage and Stokes Drift that are known to impact debris distributions and transport in the coastal ocean, these results provide critical insights to building an effective stratified sampling and monitoring effort to better understand debris distributions, concentrations, and origins throughout the West Antarctic Peninsula.

Quantifying Uncertainty in Sustainable Biomass and Production of Biotic Carbon in Enceladus' Notional Methanogenic Biosphere

AbstractBeneath Enceladus' ice crust lies an ocean which might host habitable conditions. Here, the scale and productivity of a notional Enceladean methanogenic biosphere are computed as a function of the core‐to‐ocean flux of hydrogen and the ratio between abiotic and biotic methane in Enceladus' space plume. Habitats with an ocean‐top pH range of 8–9 have up to 40%–60% probability of being energy‐limited. Those at pH > 9 are increasingly uninhabitable, and those <8.5 are increasingly likely to host exponential growth, possibly leading to compositional inconsistencies between the ocean and Cassini gas observations. In those cases, energy‐based habitability models cannot infer an inhabited Enceladus consistent with both Earth life and Cassini measurements without including additional microbial growth limiters such as nutrient limitation, toxicity, or spatial constraints. If methanogens are isolated to a 350 K seafloor habitat and consume 10 mol s−1 of H2, the most probable biomass is 103, 103.7 kg C with ocean‐top pH 8,9, respectively. Biomass production consistent with space plume fluxes is 104–106 kgC yr−1—milligrams of cellular carbon per kilogram of H2O ejected—but requires that >50% of the space plume methane is biotic. Alternative scenarios are presented, and biomass is generally lower when habitat temperature is higher. Ocean biomass density cannot yet be reliably estimated owing to uncertainties in the scale and physicochemical properties of Enceladus' putative habitats. Evaluating abiotic to biotic ratios in plume methane and organic material could help identify false negative results from life detection missions and constrain the scale of an underlying biosphere.

Automated detection of fin whale calls recorded with distributed acoustic sensing

As the global biodiversity crisis intensifies, wildlife monitoring has become a necessity. From an oceanographic perspective, the tracking of baleen whales allows studies of their habitat use and can support call density estimates, which are indicative of ocean health. Distributed Acoustic Sensing (DAS) is a promising observational technique that measures strain rates along a spare optical fiber with a spatial resolution of tens of meters to distances of ∼100 km. Over 4-days in November 2021, a public domain DAS dataset was collected on the submarine cables of the Ocean Observatories Initiative Regional Cabled Array off the coast of central Oregon. The experiment recorded the acoustic signals from tens of thousands of fin whale calls as well as blue whale calls, ship signals and T-phases. We will describe preliminary efforts to develop automated methods to detect and localize fin whale calls using small representative examples of the DAS data. Our longer-term goal is to extend this approach to the full data set to understand the sensitivity of DAS to fin whale calls as function of cable geometry and seafloor characteristics and to study the distribution of calling fin whales in coastal waters off central Oregon. [Work supported by ONR.]

Ocean Bottom Distributed Acoustic Sensing for Oceanic Seismicity Detection and Seismic Ocean Thermometry

AbstractA T‐wave is a seismo‐acoustic wave that can travel a long distance in the ocean with little attenuation, making it valuable for monitoring remote tectonic activity and changes in ocean temperature using seismic ocean thermometry (SOT). However, current high‐quality T‐wave stations are sparsely distributed, limiting the detectability of oceanic seismicity and the spatial resolution of global SOT. The use of ocean bottom distributed acoustic sensing (OBDAS), through the conversion of telecommunication cables into dense seismic arrays, is a cost‐effective and scalable means to complement existing seismic stations. Here, we systematically investigate the performance of OBDAS for oceanic seismicity detection and SOT using a 4‐day Ocean Observatories Initiative community experiment offshore Oregon. We first present T‐wave observations from distant and regional earthquakes and develop a curvelet denoising scheme to enhance T‐wave signals on OBDAS. After denoising, we show that OBDAS can detect and locate more and smaller T‐wave events than regional OBS network. During the 4‐day experiment, we detect 92 oceanic earthquakes, most of which are missing from existing catalogs. Leveraging the sensor density and cable directionality, we demonstrate the feasibility of source azimuth estimation for regional Blanco earthquakes. We also evaluate the SOT performance of OBDAS using pseudo‐repeating earthquake T‐waves. Our results show that OBDAS can utilize repeating earthquakes as small as M3.5 for SOT, outperforming ocean bottom seismometers. However, ocean ambient natural and instrumental noise strongly affects the performance of OBDAS for oceanic seismicity detection and SOT, requiring further investigation.

Exploiting passive acoustic recordings of seismic survey datasets

Marine seismic reflection surveys provide a dense and abundant dataset covering a large region of geological interest in the ocean. These data provide an opportunity for acoustic analysis with varying environmental characteristics, both in the water column and the seabed. In a typical survey, an airgun array is fired tens to hundreds of thousands of times, and hundreds of receiver channels record the acoustic signal reflected from the seabed after each shot. Additionally, the high amplitude signal broadcast from airgun arrays used in these surveys may be measured passively by hydrophones located close to the survey. In this work, we consider the data measured by Ocean Observatories Initiative (OOI) hydrophones adjacent to two seismic surveys, MGL1905, and MGL2104. In each case, a 6600 in3 airgun array is fired ∼every 37.5 m along multiple survey lines extending 10s to 100s of kilometers. Each survey generates multiple terabytes of acoustic data, and many of the shots are also captured by OOI hydrophones. This work aims to combine these two datasets to evaluate the acoustic behavior of airgun shots over a variety of conditions and examine the relationship between various environmental factors and acoustic propagation behavior. [Work supported by ONR.]

Analysis and optimization of a deep-water in-situ power generation system based on novel ductless Archimedes screw hydrokinetic turbines

A growing interest in harnessing ocean current energy for deep ocean observation platforms has been encouraged by the requirement of cost saving and wide-range deployment. A new concept of deep-water in-situ power generation system using ductless Archimedes screw hydrokinetic turbines was proposed in this study. To fill the hydrodynamic knowledge gap of ductless Archimedes screw hydrokinetic turbines, an experimentally validated numerical model was built to perform a parameter-sensitive study. Results demonstrate that interactions with the turbine's cylindrical side enable efficient operation even under yawed conditions. The power coefficients were found to be higher with yaw angles ranging from 310° to 50° and from 130° to 230° compared to the non-yawed state. To further enhance the hydrodynamic performance of the turbine, two optimization procedures were proposed: multi-objective optimization and specific optimization. An in-house Simulink model was developed to assess the effectiveness of the two optimization procedures. The results indicate that the specific optimization is better suited for practical applications when the ocean current data is available. With an optimized turbine radius of 0.15 m, the proposed deep-water in-situ power generation system can provide sufficient energy to power environmental sensors, such as temperature and salinity sensors.

A Scheme for Credibility of Surface Currents Derived From High Frequency Radars

Large amount of real-time ocean currents measured by HF radar have been included into the ocean observation database in many countries and regions, in support of applications for various marine activities such as marine research, Oil spill detection, tsunami warning, search and rescue. However, due to the performance of inversion algorithms and radar antennas, and other reasons, there are differences in the credibility of ocean current results at different times and locations, which brings on difficulties to applications of ocean currents. This letter proposes a method for developing a credibility model of ocean current results, mainly for all-digital multi-input and multi-output (MIMO) HF radar. By comparing the model with field experimental results, it is confirmed that the method is feasible. It will be very beneficial for radar users to select and use ocean current results, greatly reducing the work of verification of newly installed radars, and will be an innovation in the application technology of over-the-horizon marine radars.

Targeted observation for the climatology temperature in the Kuroshio region based on the CNOP approach

To reduce the cost of ocean observations and improve prediction accuracy of the Kuroshio region temperature, this study investigates the related targeted observation by using the conditional nonlinear optimal perturbation (CNOP) approach. Results show that the scheme of vertical-integrated energy is more suitable for the identification of sensitive area in the related targeted observation. By conducting a set of observation system simulation experiments (OSSEs), we discovered that the sensitive areas identified by the CNOP exert substantial influence on temperature predictions within the target area. The dynamic diagnosis further indicated that the pressure gradient and Coriolis force in the momentum equations greatly contribute the development of the prediction biases. These findings implied that the implement of CNOP-based targeted observation represents a cost-effective strategy for enhancing temperature predictions in the Kuroshio region.

Underwater object detection method based on learnable query recall mechanism and lightweight adapter.

With the rapid development of ocean observation technology, underwater object detection has begun to occupy an essential position in the fields of aquaculture, environmental monitoring, marine science, etc. However, due to the problems unique to underwater images such as severe noise, blurred objects, and multi-scale, deep learning-based target detection algorithms lack sufficient capabilities to cope with these challenges. To address these issues, we improve DETR to make it well suited for underwater scenarios. First, a simple and effective learnable query recall mechanism is proposed to mitigate the effect of noise and can significantly improve the detection performance of the object. Second, for underwater small and irregular object detection, a lightweight adapter is designed to provide multi-scale features for the encoding and decoding stages. Third, the regression mechanism of the bounding box is optimized using the combination loss of smooth L1 and CIoU. Finally, we validate the designed network against other state-of-the-art methods on the RUOD dataset. The experimental results show that the proposed method is effective.