Search Results (17)

Offshore wind farms are rapidly expanding worldwide, and the submerged structures supporting wind turbines have the potential to function as artificial reefs for marine organisms. Quantitative visualization of fish aggregations around these foundations can provide valuable information for promoting collaboration between fisheries and offshore wind energy development. This study explored the use of multibeam sonar to detect spatial distributions and estimate the biomass of pelagic fish aggregations around the foundations of offshore wind power facilities. Fish distribution was extracted from multibeam water column image data using an automated sequence of filtering steps, ending with a spatial filter designed to remove common noise artifacts in multibeam sonar data. The resulting fish aggregations were visualized in three dimensions, revealing a tendency to cluster leeward of turbine and observation tower foundations, and fish biomass was successfully estimated from beam backscatter strength. The developed method can be applied to other offshore wind farms to demonstrate the role of turbine foundations as artificial reefs for fish. Full article

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. Full article

Multibeam echosounders, as the most commonly used bathymetric equipment, have been widely applied in acquiring seabed topography and underwater sonar images. However, when interference occurs in the water column, traditional bottom detection methods may fail, resulting in discontinuities in the bathymetry and distortion in the sonar images. To solve this problem, we propose an anti-interference bottom detection method based on deep learning models. First, the variation differences of backscatter strengths at different incidence angles and the failure conditions of traditional methods were analyzed. Second, the details of our deep learning models are explained. And these models were trained using samples in the specular reflection, scatter reflection, and high-incidence angle regions, respectively. Third, the bottom detection procedures of the along-track and across-track water column data using the trained models are provided. In the experiments, multibeam data with strong interferences in the water column were selected. The bottom detection results of the along-track water column data at incidence angles of 0°, 35°, and 60° and the across-track ping data validated the effectiveness of our method. By comparison, our method acquired the correct bottom position when the traditional methods had inaccurate or even no detection results. Our method can be used to supplement existing methods and effectively improve bathymetry robustness under interference conditions. Full article

With rising interest in marine renewable energy (MRE) associated with offshore wind, waves, and tidal flows, the effects of device placement on changes in animal behaviour require proper assessment to minimise environmental impacts and inform decision making. High-frequency multibeam echosounders, or imaging sonars, can be used to observe and record the underwater movement and behaviour of animals at a fine scale (tens of metres). However, robust target detection and tracking of closely spaced animals are required for assessing animal–device and predator–prey interactions. Dual-frequency multibeam echosounders combine longer detection ranges (low frequency) with greater detail (high frequency) while maintaining a wide field of view and a full water column range compared to acoustic or optical cameras. This study evaluates the performance of the Tritech Gemini 1200ik imaging sonar at 720 kHz (low frequency) and 1200 kHz (high frequency) for small target detection with increasing range and the ability of the two frequency modes to discriminate between two closely spaced targets using a 38.1 mm tungsten carbide acoustic calibration sphere under controlled conditions. The quality of target detection decreases for both modes with increasing range, with a 25 m limit of detection at high frequency and a low-frequency mode able to detect the target up to 30 m under test conditions in shallow water. We quantified the enhanced performance of the high-frequency mode in discriminating targets at short ranges and improved target detection and discrimination at high ranges in the low-frequency mode. Full article

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. Full article

Marine acoustic sources are widely used for geophysical imaging, oceanographic sensing, and communicating with and tracking objects or robotic vehicles in the water column. Under the U.S. Marine Mammal Protection Act and similar regulations in several other countries, the impact of controlled acoustic sources is assessed based on whether the sound levels received by marine mammals meet the criteria for harassment that causes certain behavioral responses. This study describes quantitative factors beyond received sound levels that could be used to assess how marine species are affected by many commonly deployed marine acoustic sources, including airguns, high-resolution geophysical sources (e.g., multibeam echosounders, sidescan sonars, subbottom profilers, boomers, and sparkers), oceanographic instrumentation (e.g., acoustic doppler current profilers, split-beam fisheries sonars), and communication/tracking sources (e.g., acoustic releases and locators, navigational transponders). Using physical criteria about the sources, such as source level, transmission frequency, directionality, beamwidth, and pulse repetition rate, we divide marine acoustic sources into four tiers that could inform regulatory evaluation. Tier 1 refers to high-energy airgun surveys with a total volume larger than 1500 in³ (24.5 L) or arrays with more than 12 airguns, while Tier 2 covers the remaining low/intermediate energy airgun surveys. Tier 4 includes most high-resolution geophysical, oceanographic, and communication/tracking sources, which are considered unlikely to result in incidental take of marine mammals and therefore termed de minimis. Tier 3 covers most non-airgun seismic sources, which either have characteristics that do not meet the de minimis category (e.g., some sparkers) or could not be fully evaluated here (e.g., bubble guns, some boomers). We also consider the simultaneous use of multiple acoustic sources, discuss marine mammal field observations that are consistent with the de minimis designation for some acoustic sources, and suggest how to evaluate acoustic sources that are not explicitly considered here. Full article

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. Full article

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. Full article

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. Full article

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. Full article

Multibeam echosounders are widely used for 3D bathymetric mapping, and increasingly for water column studies. However, they rapidly collect huge volumes of data, which poses a challenge for water column data processing that is often still manual and time-consuming, or affected by low efficiency and high false detection rates if automated. This research describes a comprehensive and reproducible workflow that improves efficiency and reliability of target detection and classification, by calculating metrics for target cross-sections using a commercial software before feeding into a feature-based semi-supervised machine learning framework. The method is tested with data collected from an uncalibrated multibeam echosounder around an offshore gas platform in the Adriatic Sea. It resulted in more-efficient target detection, and, although uncertainties regarding user labelled training data need to be underlined, an accuracy of 98% in target classification was reached by using a final pre-trained stacking ensemble model. Full article

Bathymetry is considered an important component in marine applications as several coastal erosion monitoring and engineering projects are carried out in this field. It is traditionally acquired via shipboard echo sounding, but nowadays, multispectral satellite imagery is also commonly applied using different remote sensing-based algorithms. Satellite-Derived Bathymetry (SDB) relates the surface reflectance of shallow coastal waters to the depth of the water column. The present study shows the results of the application of Stumpf and Lyzenga algorithms to derive the bathymetry for a small area using an Unmanned Aerial Vehicle (UAV), also known as a drone, equipped with a multispectral camera acquiring images in the same WorldView-2 satellite sensor spectral bands. A hydrographic Multibeam Echosounder survey was performed in the same period in order to validate the method’s results and accuracy. The study area was approximately 0.5 km² and located in Tuscany (Italy). Because of the high percentage of water in the images, a new methodology was also implemented for producing a georeferenced orthophoto mosaic. UAV multispectral images were processed to retrieve bathymetric data for testing different band combinations and evaluating the accuracy as a function of the density and quantity of sea bottom control points. Our results indicate that UAV-Derived Bathymetry (UDB) permits an accuracy of about 20 cm to be obtained in bathymetric mapping in shallow waters, minimizing operative expenses and giving the possibility to program a coastal monitoring surveying activity. The full sea bottom coverage obtained using this methodology permits detailed Digital Elevation Models (DEMs) comparable to a Multibeam Echosounder survey, and can also be applied in very shallow waters, where the traditional hydrographic approach requires hard fieldwork and presents operational limits. Full article

Black corals (order Antipatharia) are important components of mesophotic and deep-water marine communities, but due to their inaccessibility, there is limited knowledge about the basic aspects of their distribution and ecology. The aim of this study was to test methodologies to map and study colonies of a branched antipatharian species, Antipathella wollastoni, in the Canary Islands (Spain). Acoustic tools, side-scan sonar (SSS), and a multibeam echosounder (MBES), coupled with ground-truthing video surveys, were used to determine the habitat characteristics of Antipathella wollastoni. Below 40 m depth, colonies of increasing height (up to 1.3 m) and abundance (up to 10 colonies/m²) were observed, particularly on steep and current-facing slopes on rocky substrates. However, coral presence was not directly imaged on backscatter mosaics and bathymetric data. To improve this situation, promising initial attempts of detecting Antipathella wollastoni by utilizing the MBES water column scatter in an interval for 0.75 m to 1 m above the seafloor are reported. Full article

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. Full article

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. Full article