Multibeam Water Column Research Articles

Acoustic Imaging Learning-Based Approaches for Marine Litter Detection and Classification

This paper introduces an advanced acoustic imaging system leveraging multibeam water column data at various frequencies to detect and classify marine litter. This study encompasses (i) the acquisition of test tank data for diverse types of marine litter at multiple acoustic frequencies; (ii) the creation of a comprehensive acoustic image dataset with meticulous labelling and formatting; (iii) the implementation of sophisticated classification algorithms, namely support vector machine (SVM) and convolutional neural network (CNN), alongside cutting-edge detection algorithms based on transfer learning, including single-shot multibox detector (SSD) and You Only Look once (YOLO), specifically YOLOv8. The findings reveal discrimination between different classes of marine litter across the implemented algorithms for both detection and classification. Furthermore, cross-frequency studies were conducted to assess model generalisation, evaluating the performance of models trained on one acoustic frequency when tested with acoustic images based on different frequencies. This approach underscores the potential of multibeam data in the detection and classification of marine litter in the water column, paving the way for developing novel research methods in real-life environments.

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.

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

Echo grid integration: A novel method for preprocessing multibeam water column data to quantify underwater gas bubble emissions

AbstractWater column imaging multibeam echo sounder systems (MBESs) are a promising technology for quantitative estimates of the gas bubble volume flow within large gas seepage areas. Considerable progress has been made in recent years toward applicable calibration methods for MBESs as well as developing inversion models to convert acoustically measured backscattering cross sections to gas bubble volume flow. However, MBESs are still not commonly used for quantitative gas flow assessments. A reason for this is the absence of published processing methods that demonstrate how MBES data can be processed to quantitatively represent bubble streams. Here, we present a novel method (echo grid integration) that allows for assessing the aggregated backscattering cross section of targets within horizontal water layers. This derived value enables quantifying bubble stream gas flow rates using existing acoustic inversion methods. The presented method is based on averaging geo‐referenced volume backscattering coefficients onto a high‐resolution 3D voxel‐grid. The results are multiplied with the voxel volume to represent measurements of the total backscattering cross‐section within each voxel cell. Individual gridded values cannot be trusted because the beam pattern effects cause the values of individual targets to “smear” over multiple grid‐cells. The true aggregated backscattering cross‐section is thus estimated as the integral over the grid‐cells affected by this smearing. Numerical simulation of MBES data acquisition over known targets assesses the method's validity and quantify it's uncertainty for different, realistic scenarios. The found low measurement bias (< 1%), and dispersion (< 5%) are promising for application in gas flow quantification methods.

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.

Mapping warming reefs—An application of multibeam acoustic water column analysis to define threatened abalone habitat

Robust definition of the spatial extent of seafloor habitats and how they may be changing through time is a holy grail for ecosystem management, particularly if an ecosystem is approaching a tipping point beyond which irreversible changes may occur. Here we generate and explore a new data set for the management of warming reefs in eastern Tasmania, Australia that will significantly improve the baseline maps required for fine-scaled spatial modelling and management that is, both robust at regional scales and is highly resolved within the water column. This procedure enabled the relative density of kelp vegetation to be identified in a region that is being overwhelmed by the range extension of a destructive grazer, the Longspined Sea Urchin, Centrostephanus rodgersii. We present a new online tool to visualize multibeam water column acoustic data as surfaces of kelp density at high resolution (50 cm) scale over seafloor terrain maps (spanning a total straight-line distance of 594 km and a total area of 29.14 km2) to reveal the types of reef structure on the East Coast of Tasmania where abalone habitat is threatened by kelp loss.

Use of multibeam bathymetry and backscatter to improve seabed geochemical surveys — Part 1: Historical review, technical description, and best practices

Oil and gas seeps have been a key tool in hydrocarbon exploration since ancient times. Basin-wide reconnaissance exploration, focused on basic geology and identification of hydrocarbon seepage, has been typical of onshore basin analysis since the beginning of the petroleum industry. Since the discovery of marine chemosynthetic “cold seep” communities in the mid-1980s, and their association with offshore oil and gas seepage, the energy industry has been mapping seeps to target them for exploration and avoid them in development. For exploration, the successful sampling of oil or gas at the seafloor reduces exploration risk by demonstrating generative source rock, maturation, migration, and charge — all key data about the subsurface petroleum systems. In the marine environment, seep communities and associated diagenetic precipitates can modify the bathymetry and/or the backscatter and can be imaged by multibeam echo sounder (MBES). MBES can provide detailed bathymetry of the seafloor; multibeam backscatter can provide not only potential targets for seep sampling but also information on the seafloor characteristics at or just below the seafloor, and multibeam water column data can image gas plumes rising from the seafloor. Multibeam was introduced outside of military applications in the 1970s with the application of multibeam to seep science in the oil and gas industry, and the use of ultrashort baseline-positioned cores in a real-time geographical information system (GIS) to target seeps, began in the late 1990s, with the first proprietary survey in 2000. We review the history of multibeam, the history of seep science, and “lessons learned” over decades to best practices in seep hunting, from vessel specification to dry dock to presurvey to survey operations to target selection.

Extraction of Submarine Gas Plume Based on Multibeam Water Column Point Cloud Model

The gas plume is a direct manifestation of sea cold seep and one of the most significant symbol indicators of the presence of gas hydrate reservoirs. The multibeam water column (MWC) data can be used to extract and identify the gas plume efficiently and accurately. The current research methods mostly start from the perspective of image theory, which cannot identify the three-dimensional (3D) spatial structure features of gas plumes, reducing the efficiency and accuracy of detection. Therefore, this paper proposes a method for identifying and extracting the gas plume based on an MWC point cloud model, which calculates the spatially resolved homing of MWC data and constructs a 3D point cloud model of MWC containing acoustic reflection intensity information. It first performs noise suppression of the 3D point cloud of the MWC based on the symmetric subtraction and Otsu algorithm by leveraging the noise distribution of the MWC and the reflection intensity characteristics of the gas plume. Then, it extracts the point cloud clusters containing the gas plume based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) according to the density difference between the gas plume point cloud and the background MWC point cloud and next identifies the point cloud clusters by feature matching based on fast point feature histograms (FPFHs). Finally, it extracts the gas plume point cloud set in the MWC. As evidenced by the MWC data collected from gas hydrate enrichment zones in the Gulf of Mexico, the location of gas plume extracted by this method is highly consistent with that of gas leakage points measured during the cruise. Using this method, we obtained the point cloud data set of gas plume for the first time and accurately characterized the 3D spatial morphology of the subsea gas plume, providing technical support for gas hydrate exploration, subsea gas seepage area delineation, and subsea seepage gas flux estimation.

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.

A new approach to processing and imaging multibeam water column echosounder data: Application to a complex methane seep on the southern Cascadia margin

Acoustic echosounding systems are increasingly used to image water-column backscatter in addition to mapping the seafloor. We have imaged an acoustic flare generated by methane bubbles emanating from a vent sourced at 1840 m water depth offshore northern California using a shipboard Kongsberg EM122. Data include five transits over the flare and approximately 11 h of continuous observation when the ship held station. Shipboard observations showed a strong flare splitting into multiple smaller, intermittent flares at a water depth of 800–1200 m and pronounced temporal variability. We introduce a new approach to processing the data in which we correct the backscatter data for ship motion and bin the data into voxels with dimensions of 20 m in X and Y and 40 m in Z for a transit over the flare and into vertical slices with dimensions of 15 m in X and Z and 4 min in time when the ship was stationary. The processed data indicate that the signal is dominated by bubbles emanating from a source region with a diameter of approximately 40 m located on the southern edge of what is likely a ring of sources with a diameter of approximately 600 m. When the ship was stationary, we were able to track an individual pulse rising at a rate of 8–10 m/min. Our results illustrate the limitations of monitoring temporal variation in gas flux using multibeam echosounders because of the trade-off between imaging the entire flare by averaging over tens of minutes to hours and observing a slice through the flare to capture short-lived pulses of gas expulsion. Nevertheless, because echosounders are widely available, they can continue to provide valuable data on the spatial and temporal distribution of gas emissions on continental margins that can be used to frame hypotheses and plan more comprehensive follow-up experiments.

Remote sensing and the UN Ocean Decade: high expectations, big opportunities

Remote sensing and the UN Ocean Decade: high expectations, big opportunities

Controls on Gas Emission Distribution on the Continental Slope of the Western Black Sea

The continental slopes of the Black Sea show abundant manifestations of gas seepage in water depth of <720 m, but underlying controls are still not fully understood. Here, we investigate gas seepage along the Bulgarian and Romanian Black Sea margin using acoustic multibeam water column, bathymetry, backscatter, and sub-bottom profiler data to determine linkages between sub-seafloor structures, seafloor gas seeps, and gas discharge into the water column. More than 10,000 seepage sites over an area of ∼3,000 km2were identified. The maximum water depth of gas seepage is controlled by the onset of the structure I gas hydrate stability zone in ∼720 m depth. However, gas seepage is not randomly distributed elsewhere. We classify three factors controlling on gas seepage locations into depositional, erosional, and tectonic factors. Depositional factors are associated with regionally occurring sediment waves forming focusing effects and mass-transport deposits (MTDs) with limited sediment drape. Elongated seafloor depressions linked to faulting and gas seepage develop at the base between adjacent sediment waves. The elongated depressions become progressively wider and deeper toward shallow water depths and culminate in some locations into clusters of pockmarks. MTDs cover larger regions and level out paleo-topography. Their surface morphology results in fault-like deformation patterns of the sediment drape on top of the MTDs that is locally utilized for gas migration. Erosional factors are seen along channels and canyons as well as slope failures, where gas discharge occurs along head-scarps and ridges. Sediment that was removed by slope failures cover larger regions down-slope. Those regions are devoid of gas seepage either by forming impermeable barriers to gas migration or by removal of the formerly gas-rich sediments. Deep-rooted tectonic control on gas migration is seen in the eastern study region with wide-spread normal faulting promoting gas migration. Overall, gas seepage is widespread along the margin. Gas migration appears more vigorous in shallow waters below ∼160 m water depth, but the number of flare sites is not necessarily an indicator of the total volume of gas released.

Distribution and development of submarine mud volcanoes on the Makran Continental Margin, offshore Pakistan

Abstract In 2018, China Geological Survey and National Institute of Oceanography of Pakistan conducted a comprehensive geological survey on the Makran Continental Margin offshore Pakistan using the R/V Haiyangdizhi10. Seismic profiles, multibeam bathymetric and water column data, and seafloor observations were obtained during the expedition. Twelve mud volcanoes and three pockmarks were discovered in the surveyed area on the Makran Continental Margin. The heights of the mud volcanoes and the depths of the pockmarks are 12–121 m and −108 to −26 m, respectively, with diameter of 500–1400 m. The mud volcanoes are distributed on top of the W-E trending ridges at 1200–2800 m water depth. Six mud volcanoes and one pockmark were active during the investigation as indicated by the gas flares mapped in the multibeam water column data. Acoustic blanking zones, faults and gas chimneys are distributed ubiquitously beneath the mud volcanoes on the seismic profiles. Their spatial correlation with mud volcanoes indicates that the faults and gas chimneys provide pathways for fluid migration. In addition, the Bottom Simulating Reflectors (BSR), interface of gas hydrate and free gas, were also discovered in multiple seismic profiles. Based on the BSR discontinuity and piercing of the gas chimneys, it is inferred that mud volcano activities might cause gas hydrate dissociation, leading to the upward movement of the BSR. Tectonic uplifting, seismic-induced shock and fluid overpressure induced by rapid sedimentation are the major driving forces for the mud volcano formation and development. Gases from deep strata and gas hydrate decomposition are suspected to provide additional forces for fluid migration. Pressure decrease in the deep subsurface and gas hydrate sealing of the faults and fracture are speculated to be important in controlling the intermittent mud volcano activities and potential pockmark formation. The new model of mud volcano activities depicted in this study might be applicable to other mud volcanoes elsewhere.

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.

Acoustic characteristics of cold-seep methane bubble behavior in the water column and its potential environmental impact

The amount of methane leaked from deep sea cold seeps is enormous and potentially affects the global warming, ocean acidification and global carbon cycle. It is of great significance to study the methane bubble movement and dissolution process in the water column and its output to the atmosphere. Methane bubbles produce strong acoustic impedance in water bodies, and bubble strings released from deep sea cold seeps are called “gas flares” which expressed as flame-like strong backscatter in the water column. We characterized the morphology and movement of methane bubbles released into the water using multibeam water column data at two cold seeps. The result shows that methane at site I reached 920 m water depth without passing through the top of the gas hydrate stability zone (GHSZ, 850 m), while methane bubbles at site II passed through the top of the GHSZ (597 m) and entered the non-GHSZ (above 550 m). By applying two methods on the multibeam data, the bubble rising velocity in the water column at sites I and II were estimated to be 9.6 cm/s and 24 cm/s, respectively. Bubble velocity is positively associated with water depth which is inferred to be resulted from decrease of bubble size during methane ascending in the water. Combined with numerical simulation, we concluded that formation of gas hydrate shells plays an important role in helping methane bubbles entering the upper water bodies, while other factors, including water depth, bubble velocity, initial kinetic energy and bubble size, also influence the bubble residence time in the water and the possibility of methane entering the atmosphere. We estimate that methane gas flux at these two sites is 0.4×106−87.6×106 mol/a which is extremely small compared to the total amount of methane in the ocean body, however, methane leakage might exert significant impact on the ocean acidification considering the widespread distributed cold seeps. In addition, although methane entering the atmosphere is not observed, further research is still needed to understand its potential impact on increasing methane concentration in the surface seawater and gas-water interface methane exchange rate, which consequently increase the greenhouse effect.

Automated Filtering of Multibeam Water-Column Data to Detect Relative Abundance of Giant Kelp (Macrocystis pyrifera)

Modern multibeam echosounders can record backscatter data returned from the water above the seafloor. These water-column data can potentially be used to detect and map aquatic vegetation such as kelp, and thus contribute to improving marine habitat mapping. However, the strong sidelobe interference noise that typically contaminates water-column data is a major obstacle to the detection of targets lying close to the seabed, such as aquatic vegetation. This article presents an algorithm to filter the noise and artefacts due to interference from the sidelobes of the receive array by normalizing the slant-range signal in each ping. To evaluate the potential of the filtered data for the detection of aquatic vegetation, we acquired a comprehensive water-column dataset over a controlled experimental site. The experimental site was a transplanted patch of giant kelp (Macrocystis pyrifera) forest of known biomass and spatial configuration, obtained by harvesting several individuals from a nearby forest, measuring and weighing them, and arranging them manually on an area of seafloor previously bare. The water-column dataset was acquired with a Kongsberg EM 2040 C multibeam echosounder at several frequencies (200, 300, and 400 kHz) and pulse lengths (25, 50, and 100 μs). The data acquisition process was repeated after removing half of the plants, to simulate a thinner forest. The giant kelp plants produced evident echoes in the water-column data at all settings. The slant-range signal normalization filter greatly improved the visual quality of the data, but the filtered data may under-represent the true amount of acoustic energy in the water column. Nonetheless, the overall acoustic backscatter measured after filtering was significantly lower, by 2 to 4 dB on average, for data acquired over the thinned forest compared to the original experiment. We discuss the implications of these results for the potential use of multibeam echosounder water-column data in marine habitat mapping.

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.

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.