Water Column Images Research Articles

Automatic detection and extraction of lost shipping containers based on YOLO and the segment anything model

ABSTRACT The artificial visual method is currently commonly used to decipher multi-beam water column images to obtain the position and state of lost shipping containers. However, the recognition efficiency and accuracy of this method need to be improved. The You Only Look Once (YOLO) series model has strong real-time target detection capability. Meanwhile, the Segment Anything Model (SAM) has strong zero-shot transferability. A detection and extraction method for lost shipping containers, which combines the two models mentioned above, is proposed in this study. First, the YOLO series model is employed to detect lost shipping container targets in a single-frame water column image. On this basis, the output of bounding box positions by the optimal target detection model is used as a prompt for the SAM. Finally, the SAM is used to extract lost shipping container targets in images through zero-shot transferability. Experimental results in the Pearl River estuary show that the combined modelling method of YOLOv5-n and EdgeSAM-3× achieves the best overall performance. The precision and recall for the detection of lost shipping containers by this method is better than 95%. In terms of target extraction, YOLOv5-n and EdgeSAM-3× have the best Intersection over Union, recall, and F1 scores.

Multibeam water column image super-resolution by combining morphological and intensity features

ABSTRACT Multibeam water column images (WCI) play a crucial role in the detection and recognition of underwater targets. However, WCI is not a complete image of underwater space due to the gaps between beams inherent in multibeam systems. In this study, we design the WCI super-resolution network (WCISRN) based on Swin-transformer to adapt to the uneven and sparse distribution of water column data. To enable WCISRN to learn morphological features effectively, we produced simulated datasets for pre-training. Subsequently, we conduct self-supervised training on the measured WCI dataset to fine-tune the network parameters, aiming to better adapt WCISRN to the backscattering intensity features of WCI. Finally, by performing network cross-fusion, the pre-trained network and self-supervised training network are combined to form a new network that achieves a balance between image morphology and intensity features. The proposed method is evaluated through visual analysis and quantitative comparison using simulated and real-world datasets. The experimental results demonstrate that our method exhibits stronger detail restoration ability and clearer image morphology. The average peak signal-to-noise ratio (PSNR) on the test set improved from 23.75–26.14, indicating a significant enhancement in the imaging quality of WCI.

Methane gas flares in the forearc basin of the Andaman-Nicobar subduction zone

Gas hydrates deposits in the Andaman forearc basin are inferred from seismic data and confirmed by drilling/coring during the NGHP-01 expedition. We present new evidence of gas flares in the Andaman forearc basin, detected through water column image (WCI), subbottom profiling, and high-resolution seismic data acquired onboard RV Sindhu Sadhana (SSD-085) in November-December 2021. The gas flares are located over an elongated sedimentary ridge, featuring two prominent mounds (M1 and M2) with distinct geological features. Compressional tectonics induced by the Diligent fault (DLF) formed the ridge with varying slopes and elevations. Gas flares observed above the mound M1 in WCI and sub-bottom profiler data. Seafloor samples reveal carbonate rocks with visible pores, indicating gas/fluid migration or burrows. The regional seismic profile delineates three sedimentary sequences: folded and faulted strata, mass transport deposits, and horizontal-to-sub-horizontal sedimentary layers. Additionally, we observed a bottom simulating reflector (BSR), indicating potential subsurface gas hydrate deposits. Detailed high-resolution seismic data revealed complex fault systems near bathymetry mounds (M1 and M2), which may serve as pathways for vertical fluid/gas migration.

Frequency-Domain Reverse-Time Migration with Analytic Green's Function for the Seismic Imaging of Shallow Water Column Structures in the Arctic Ocean.

Seismic oceanography can provide a two- or three-dimensional view of the water column thermocline structure at a vertical and horizontal resolution from the multi-channel seismic dataset. Several seismic imaging methods and techniques for seismic oceanography have been presented in previous research. In this study, we suggest a new formulation of the frequency-domain reverse-time migration method for seismic oceanography based on the analytic Green's function. For imaging thermocline structures in the water column from the seismic data, our proposed seismic reverse-time migration method uses the analytic Green's function for numerically calculating the forward- and backward-modeled wavefield rather than the wave propagation modeling in the conventional algorithm. The frequency-domain reverse-time migration with analytic Green's function does not require significant computational memory, resources, or a multifrontal direct solver to calculate the migration seismic images as like conventional reverse-time migration. The analytic Green's function in our reverse-time method makes it possible to provide a high-resolution seismic water column image with a meter-scale grid size, consisting of full-band frequency components for a modest cost and in a low-memory environment for computation. Our method was applied to multi-channel seismic data acquired in the Arctic Ocean and successfully constructed water column seismic images containing the oceanographic reflections caused by thermocline structures of the water mass. From the numerical test, we note that the oceanographic reflections of the migrated seismic images reflected the distribution of Arctic waters in a shallow depth and showed good correspondence with the anomalies of measured temperatures and calculated reflection coefficients from each XCDT profile. Our proposed method has been verified for field data application and accuracy of imaging performance.

Automatic segmentation of gas plumes from multibeam water column images using a U-shape network

Automatic segmentation of gas plumes from multibeam water column images using a U-shape network

Gas Plume Target Detection in Multibeam Water Column Image Using Deep Residual Aggregation Structure and Attention Mechanism

A multibeam water column image (WCI) can provide detailed seabed information and is an important means of underwater target detection. However, gas plume targets in an image have no obvious contour information and are susceptible to the influence of underwater environments, equipment noises, and other factors, resulting in varied shapes and sizes. Compared with traditional detection methods, this paper proposes an improved YOLOv7 (You Only Look Once vision 7) network structure for detecting gas plume targets in a WCI. Firstly, Fused-MBConv is used to replace all convolutional blocks in the ELAN (Efficient Layer Aggregation Networks) module to form the ELAN-F (ELAN based on the Fused-MBConv block) module, which accelerates model convergence. Additionally, based on the ELAN-F module, MBConv is used to replace the 3 × 3 convolutional blocks to form the ELAN-M (ELAN based on the MBConv block) module, which reduces the number of model parameters. Both ELAN-F and ELAN-M modules are deep residual aggregation structures used to fuse multilevel features and enhance information expression. Furthermore, the ELAN-F1M3 (ELAN based on one Fused-MBConv block and three MBConv blocks) backbone network structure is designed to fully leverage the efficiency of the ELAN-F and ELAN-M modules. Finally, the SimAM attention block is added into the neck network to guide the network to pay more attention to the feature information related to the gas plume target at different scales and to improve model robustness. Experimental results show that this method can accurately detect gas plume targets in a complex WCI and has greatly improved performance compared to the baseline.

Investigating Eddies From Coincident Seismic and Hydrographic Measurements in the Chukchi Borderlands, the Western Arctic Ocean

AbstractHalocline eddies transport mass and energy across the Arctic Ocean. Seismic oceanography uses multichannel seismic reflection (MCS) data to create high resolution images of the water column, revealing oceanic fine structures. In this paper, we present water column images processed from MCS data acquired during cruise MGL1112 on the Chukchi Borderlands in the western Arctic Ocean. Combined with along‐track images of current velocities measured during MCS acquisition by a hull‐mounted acoustic Doppler current profilers, a total of 23 mesoscale eddies were detected, of which 19 are anticyclonic, and 4 are cyclonic. They correspond to the lentoid and mounded reflections on the seismic images, respectively. These shallow eddies are constrained by the halocline and occur in regions with rugged seafloor. The geometric parameters of these eddies were estimated from the underway data collected during MGL1112. These parameters could be valuable for modeling 3D eddy structures and validating high‐resolution climate projection models. Expendable Bathythermography (XBT) profiles of water temperature and sound speed versus depth were collected during this ruise. Three of the 24 XBT stations sampled eddies opportunely, we found one was warm‐core and two were cold‐core anticyclonic eddies combined with historical conductivity‐temperature‐depth data. The cores of these eddies might be made up of Pacific water. We synthesized the MCS records with coincident and historical hydrographic data which permitted distinctions between the seismic responses of the eddies. Distinctive reflection structures are observed around the eddy core (e.g., chaotic, imbricate, layered, and listric reflections), which could constrain stirring and mixing process of the eddies. These results are useful for better understanding the structure and evolution of the eddies on the Chukchi Borderlands and better understanding regional mass and energy transport processes in the western Arctic Ocean.

An Efficient Method for Detection and Quantitation of Underwater Gas Leakage Based on a 300-kHz Multibeam Sonar

In recent years, multibeam sonar has become the most effective and sensitive tool for the detection and quantitation of underwater gas leakage and its rise through the water column. Motivated by recent research, this paper presents an efficient method for the detection and quantitation of gas leakage based on a 300-kHz multibeam sonar. In the proposed gas leakage detection method based on multibeam sonar water column images, not only the backscattering strength of the gas bubbles but also the size and aspect ratio of a gas plume are used to isolate interference objects. This paper also presents a volume-scattering strength optimization model to estimate the gas flux. The bubble size distribution, volume, and flux of gas leaks are determined by matching the theoretical and measured values of the volume-scattering strength of the gas bubbles. The efficiency and effectiveness of the proposed method have been verified by a case study at the artificial gas leakage site in the northern South China Sea. The results show that the leaking gas flux is approximately between 29.39 L/min and 56.43 L/min under a bubble radius ranging from 1 mm to 12 mm. The estimated results are in good agreement with the recorded data (32–67 L/min) for gas leaks generated by an air compressor. The experimental results demonstrate that the proposed method can achieve effective and accurate detection and quantitation of gas leakages.

Bubble Plume Target Detection Method of Multibeam Water Column Images Based on Bags of Visual Word Features

Bubble plumes, as main manifestations of seabed gas leakage, play an important role in the exploration of natural gas hydrate and other resources. Multibeam water column images have been widely used in detecting bubble plume targets in recent years because they can wholly record water column and seabed backscatter strengths. However, strong noises in multibeam water column images cause many issues in target detection, and traditional target detection methods are mainly used in optical images and are less efficient for noise-affected sonar images. To improve the detection accuracy of bubble plume targets in water column images, this study proposes a target detection method based on the bag of visual words (BOVW) features and support vector machine (SVM) classifier. First, the characteristics of bubble plume targets in water column images are analyzed, with the conclusion that the BOVW features can well express the gray scale, texture, and shape characteristics of bubble plumes. Second, the BOVW features are constructed following steps of point description extraction, description clustering, and feature encoding. Third, the quadratic SVM classifier is used for the recognition of target images. Finally, a procedure of bubble plume target detection in water column images is described. In the experiment using the measured data in the Strait of Georgia, the proposed method achieved 98.6% recognition accuracy of bubble plume targets in validation sets, and 91.7% correct detection rate of the targets in water column images. By comparison with other methods, the experimental results prove the validity and accuracy of the proposed method, and show potential applications of our method in the exploration and research on ocean resources.

Compression of Multibeam Echosounders Bathymetry and Water Column Data

Over the past decade, Multibeam Echosounders (MBES) have become one of the most used techniques in sea exploration. Modern MBES are capable of acquiring both bathymetric information on the seafloor and the reflectivity of the seafloor and water column. Water column imaging MBES surveys acquire significant amounts of data with rates that can exceed several GB/h depending on the ping rate. These large file sizes obtained from recording the full water column backscatter make remote transmission difficult if not prohibitive with current technology and bandwidth limitations. In this paper, we propose an algorithm to decorrelate water column and bathymetry data, focusing on the KMALL format released by Kongsberg Maritime in 2019. The pre-processing stage is integrated into FAPEC, a data compressor originally designed for space missions. Here, we test the algorithm with three different datasets: two of them provided by Kongsberg Maritime and one dataset from the Gulf of Mexico provided by Fugro USA Marine. We show that FAPEC achieves good compression ratios at high speeds using the pre-processing stage proposed in this paper. We also show the advantages of FAPEC over other lossless compressors as well as the quality of the reconstructed water column image after lossy compression at different levels. Lastly, we test the performance of the pre-processing stage, without the constraint of an entropy encoder, by means of the histograms of the original samples and the prediction errors.

Regional High-Resolution Benthic Habitat Data from Planet Dove Imagery for Conservation Decision-Making and Marine Planning

High-resolution benthic habitat data fill an important knowledge gap for many areas of the world and are essential for strategic marine conservation planning and implementing effective resource management. Many countries lack the resources and capacity to create these products, which has hindered the development of accurate ecological baselines for assessing protection needs for coastal and marine habitats and monitoring change to guide adaptive management actions. The PlanetScope (PS) Dove Classic SmallSat constellation delivers high-resolution imagery (4 m) and near-daily global coverage that facilitates the compilation of a cloud-free and optimal water column image composite of the Caribbean’s nearshore environment. These data were used to develop a first-of-its-kind regional thirteen-class benthic habitat map to 30 m water depth using an object-based image analysis (OBIA) approach. A total of 203,676 km2 of shallow benthic habitat across the Insular Caribbean was mapped, representing 5% coral reef, 43% seagrass, 15% hardbottom, and 37% other habitats. Results from a combined major class accuracy assessment yielded an overall accuracy of 80% with a standard error of less than 1% yielding a confidence interval of 78–82%. Of the total area mapped, 15% of these habitats (31,311.7 km2) are within a marine protected or managed area. This information provides a baseline of ecological data for developing and executing more strategic conservation actions, including implementing more effective marine spatial plans, prioritizing and improving marine protected area design, monitoring condition and change for post-storm damage assessments, and providing more accurate habitat data for ecosystem service models.

Subsea pipeline leak inspection by autonomous underwater vehicle

Faced with the difficulty in finding and locating leakage points of submarine pipelines timely as well as the high cost of pipeline routine inspection, a scheme using autonomous underwater vehicle (AUV) equipped with multibeam echo sounder (MBES) and forward looking sonar (FLS) for automatic inspection of submarine pipelines was developed. In the process of inspection, the AUV maintains autonomous navigation along the pre-set pipeline route at a fixed height above the pipeline, while the MBES collects water column images. After extracting the outline characteristics of gas-filled bubbles and making a leakage risk judgment, it rises to the sea surface to issue an alarm to the shore-based command center via satellite. The results of sea trial verify the effectiveness of sub-sea pipeline leak detection and real-time obstacle avoidance of the vehicle proposed in this study. Additionally, a variable buoyancy system (VBS) is adopted, which enhances the navigation efficiency of the vehicle. On the other hand, considering the complex operation environment of the pipeline inspection, the online collision avoidance is indispensable. The traditional image segmentation algorithm is not ideal for the high noise and bottom reverberation in operation sea area. Based on the traditional algorithm, an improved Otsu algorithm is proposed to improve the operation speed and denoise effect. As a result, an improved Otsu algorithm is proposed to accurately identify obstacles. Moreover, Kalman filtering is also introduced to estimate dynamic obstacles.

Widespread occurrence of methane seeps in deep-water regions of Krishna-Godavari basin, Bay of Bengal

Drilling/coring activities during NGHP-Expeditions 01 and 02 discovered significant gas hydrate deposits in the Krishna-Godavari (KG) basin, a proven petroliferous basin located along the eastern continental margin of India. Active and paleo-cold seeps are also reported from deep waters (>1000 m), indicating methane venting episodes in the KG basin. In the present study, we analyzed geophysical data acquired onboard RV Sindhu Sadhana to study the spatial distribution of gas flares in the KG offshore basin. Twenty-two gas flares were observed in water-column images at seafloor depths ranging from 638 m to 1960 m. All flares except one are detected within the gas hydrate stability zone (GHSZ), which occurs beyond 700–720 m water depths. Most gas flares seem to terminate at these depths, indicating that the bubbles might be coated with a thin methane hydrate skin. Seabed sampling of flare regions shows the presence of chemosynthetic communities and shallow gas hydrates within 2–4 mbsf.The spatial distribution of gas flares is not random and tends to align with the toe-thrust zones and shale diapiric mounds. These compressive structures formed due to shale tectonism in the KG basin have provided an environment conducive for the formation of gas hydrate deposits and gas flares/cold seeps. The analysis of high-resolution seismic data across the flares shows the presence of seismic chimneys and faults, which facilitated migration of deep-seated fluid/gas through the GHSZ. Focused fluid flow along the fault zone and the perturbation of methane + seawater phase curve due to salinity increase owing to the continuous formation of methane hydrate are the most likely mechanisms to explain methane gas migration.

Automatic Detection and Segmentation on Gas Plumes from Multibeam Water Column Images

The detection of gas plumes from multibeam water column (MWC) data is the most direct way to discover gas hydrate reservoirs, but current methods often have low reliability, leading to inefficient detections. Therefore, this paper proposes an automatic method for gas plume detection and segmentation by analyzing the characteristics of gas plumes in MWC images. This method is based on the AdaBoost cascade classifier, combining the Haar-like feature and Local Binary Patterns (LBP) feature. After obtaining the detected result from the above algorithm, a target localization algorithm, based on a histogram similarity calculation, is given to exactly localize the detected target boxes, by considering the differences in gas plume and background noise in the backscatter strength. On this basis, a real-time segmentation method is put forward to get the size of the detected gas plumes, by integration of the image intersection and subtraction operation. Through the shallow-water and deep-water experiment verification, the detection accuracy of this method reaches 95.8%, the precision reaches 99.35% and the recall rate reaches 82.7%. Integrated with principles and experiments, the performance of the proposed method is analyzed and discussed, and finally some conclusions are drawn.

Optical Flow-Based Detection of Gas Leaks from Pipelines Using Multibeam Water Column Images

In recent years, most multibeam echo sounders (MBESs) have been able to collect water column image (WCI) data while performing seabed topography measurements, providing effective data sources for gas-leakage detection. However, there can be systematic (e.g., sidelobe interference) or natural disturbances in the images, which may introduce challenges for automatic detection of gas leaks. In this paper, we design two data-processing schemes to estimate motion velocities based on the Farneback optical flow principle according to types of WCIs, including time-angle and depth-across track images. Moreover, by combining the estimated motion velocities with the amplitudes of the image pixels, several decision thresholds are used to eliminate interferences, such as the seabed, non-gas backscatters in the water column, etc. To verify the effectiveness of the proposed method, we simulated the scenarios of pipeline leakage in a pool and the Songhua Lake, Jilin Province, China, and used a HT300 PA MBES (it was developed by Harbin Engineering University and its operating frequency is 300 kHz) to collect acoustic data in static and dynamic conditions. The results show that the proposed method can automatically detect underwater leaking gases, and both data-processing schemes have similar detection performance.

High-Performance Compression of Multibeam Echosounders Water Column Data

Over the last few decades, multibeam echosounders (MBES) have become the dominant technique to efficiently and accurately map the seafloor. They now allow to collect water column acoustic images along with the bathymetry, which is providing a wealth of new possibilities in oceans exploration. However, water column imagery generates vast amounts of data that poses obvious logistic, economic, and technical challenges. Surprisingly, very few studies have addressed this problem by providing efficient lossless or lossy data compression solutions. Currently, the available options are only lossless, providing low compression ratios at low speeds. In this paper, we adapt a data compression algorithm, the Fully Adaptive Prediction Error Coder (FAPEC), which was created to offer outstanding performance under the strong requirements of space data transmission. We have added to this entropy coder a specific pre-processing stage tailored to the Kongsberg Maritime water column file formats. Here, we test it on data acquired with Kongsberg MBES models EM302, EM710, and EM2040. With this bespoke pre-processing, FAPEC provides good lossless compression ratios at high speeds, whereas lossy ratios reach water column file sizes even smaller than bathymetry raw files still with good image quality. We show the advantages over other lossless compression solutions, both in terms of compression ratios and speed. We illustrate the quality of water column images after lossy FAPEC compression, as well as its resilience to datagram errors and its potential for automatic detection of water column targets. We also show the successful integration in ARM microprocessors (like those used by smartphones and also by autonomous underwater vehicles), which provides a real-time solution for MBES water column data compression.

Multibeam water column data research in the Taixinan Basin: Implications for the potential occurrence of natural gas hydrate

A multi beam sonar survey is carried out in the continental slope of the Taixinan Basin to obtain submarine topographic and water column data. The data are processed to obtain water column images. Anomalous water column images, displaying plume characteristics, are found in gas hydrate enriched areas in the Taixinan Basin. This indicates the presence of natural gas resources in the Taixinan Basin. The multibeam sonar system is shown to provide an accurate and effective approach for detecting sub-sea gas hydrate.

A method of sidelobe effect suppression for multibeam water column images based on an adaptive soft threshold

Recording abundant back scatter information in the water, multibeam sonar water column imaging (WCI) has become one of the most effective means for the acoustic remote sensing investigation of water targets and has been applied and developed rapidly for sounding, fishery, oceanography and archaeology applications as well as in other industries in recent years. Due to the signal interference caused by the sidelobes, there is a great deal of noise in non-target areas of WCI, which reduces the accuracy and efficiency of interpretation of WCI and hinders the automatic detection and recognition of underwater objects. In this paper, an adaptive soft threshold denoising algorithm for WCI is proposed. The WCI data are divided into background areas and target-noise mixing areas by analysing the mathematical features of all angle sequences; thus, the target, noise, and sidelobe artefacts are separated using adjustable threshold parameters and by suppressing noise and sidelobe artefacts to obtain a clearer image. Lastly, the measured data are used for verification. The results indicate that the algorithm has a certain reference value for sidelobe suppression of multibeam WCI.

The first record of active methane (cold) seep ecosystem associated with shallow methane hydrate from the Indian EEZ

Here we report the discovery of cold-seep ecosystem and shallow methane hydrates (2–3 mbsf) associated with methane gas flares in the water column from the Indian EEZ for the first time. The seep-sites are located in the Krishna–Godavari (K–G) basin at water depths of 900–1800 m and are characterized by gas flares in the water-column images. The occurrence of methane gas hydrates at very shallow depths (2–3 mbsf) at some of the seep-sites is attributed to high methane flux and conducive P–T conditions, necessary for the stability of methane hydrate. Chemosymbiont bearing Bivalves (Vesicomidae, Mytilidae, Thyasiridae and Solemyidae families); Polychaetes (Siboglinidae family) and Gastropods (Provannidae family) are also identified from seep-sites.

Comprehensive Detection of Gas Plumes from Multibeam Water Column Images with Minimisation of Noise Interferences.

Multibeam echosounder systems (MBES) can record backscatter strengths of gas plumes in the water column (WC) images that may be an indicator of possible occurrence of gas at certain depths. Manual or automatic detection is generally adopted in finding gas plumes, but frequently results in low efficiency and high false detection rates because of WC images that are polluted by noise. To improve the efficiency and reliability of the detection, a comprehensive detection method is proposed in this paper. In the proposed method, the characteristics of WC background noise are first analyzed and given. Then, the mean standard deviation threshold segmentations are respectively used for the denoising of time-angle and depth-angle images, an intersection operation is performed for the two segmented images to further weaken noise in the WC data, and the gas plumes in the WC data are detected from the intersection image by the morphological constraint. The proposed method was tested by conducting shallow-water and deepwater experiments. In these experiments, the detections were conducted automatically and higher correct detection rates than the traditional methods were achieved. The performance of the proposed method is analyzed and discussed.