Multibeam Sonar Research Articles

Seabed Sediment Classification using Multifrequency Backscatter

Seabed sediment serves as an important metric for assessing marine ecosystems. Different sectors, including the development and utilization of marine resources, marine engineering, environmental research, marine fisheries, and maritime navigation safety, all depend on the classification and identification of different forms of seabed sediment. Hydrographic surveys employing Multibeam Echosounders provide information that can be used to categorize the types of seabed sediment. The data acquired during the hydrographic surveys using Multibeam Echosounders include bathymetric data and backscatter data. The backscatter data is obtained from the use of Multibeam Echosounder equipment for bathymetric data acquisition. The backscatter data from Multibeam Echosounders can be utilized for determining the classification of seabed sediment as well as for identifying seabed features. This backscatter data represents the types of sediment on the seafloor and can be used for seafloor profiling and acoustic backscatter analysis to determine the several kinds of seafloor sediment. This study uses multi-frequency mosaic backscatter data and an Artificial Neural Network (ANN) approach to classify the different types of seabed sediment in the area of Benoa Port in Bali Province. The research aims to produce a distribution map of sediment types in the study area. A training accuracy of 55% and a testing accuracy of 66% were obtained from the DNN modelling. Three out of six sediment types were identified based on these accuracy results: silty sand, gravelly sand, and sandy silt with gravel corals. According to their distribution, gravely sand had the smallest distribution (5%), while silty sand had the biggest (77%).

Seabed Sediment Classification using Multi-Frequency MBES Bathymetric and Its Features

Principally, for a few decades, hydrographical surveys have been only to determine the depth of seawater. Sonar measurement tehcnology is the most widely used to conduct hydrographical surveys employing a singlebeam echosounder (SBES) and multibeam echosounder (MBES). In addition to depth information, seafloor sediment distribution maps are essential for port planning and management. In general, the distribution of seafloor sediments is predicted using backscatter data from SBES and MBES at single and multiple frequencies. The bathymetric data generated by the echosounder can be derived into several bathymetric features such as slope, ruggedness, roughness, aspect, bathymetric position index (BPI), and curvature. This study examines the possibility of using bathymetric measurement and bathymetric derivation of multi-frequency MBES to predict the distribution of seafloor sediments, especially in the harbor pond area. The study used a deep neural network (DDN) to classify the distribution of seabed sediments with bathymetric and bathymetric features input, validated with 74 in situ sediment samples (clayey sand, silt, sandy silt, and silty sand). Up to 75% of data sample sediments are used for training and 25% for validation. The classification results by DNN showed 42.6% clayey sand, 7.4% sandy silt, 46.7% silt, and 3.35% silty sand. The overall accuracy (AO) and Kappa classification of seabed sediments with DDN were 59.5% and 0.54 (moderate), respectively.

Deep Neural Network to Classify Seabed Sediment Using MBES Multifrequency

This study investigates the classification of sediment distribution and types in inland waters using Artificial Neural Network (ANN) models based on multibeam echosounder data. Multibeam Echosounder Technology (MBES) is an acoustic instrument that emits high-speed sound pulses to map the bottom of water bodies. Valuable information about sediment characteristics can be obtained by analyzing the backscattering phenomenon, which measures the acoustic intensity reflected from the water bottom. The angular response curve (ARC) method, developed by Fonseca and Calder, is employed to analyze the relationship between backscatter intensity and the angle of reflection. The ANN models in this study are trained and tested using intensity values and angular response curves obtained from multibeam echo sounders. The ANN model architecture consists of an input layer, three hidden layers, and an output layer. The sigmoid activation function is utilized in the output layer, while the ReLU activation function is applied to the input and hidden layers. Performance evaluation demonstrates that the ANN model using uncorrected data achieves remarkable accuracy, with 100% accuracy obtained in both the training and testing stages. The model exhibits a training loss value of 0.059443 and a testing loss value of 0.110030, indicating its effectiveness in capturing patterns and reducing errors. Visualizing the sediment distribution in Waduk Selorejo reveals a relatively balanced presence of Clavey sand and Sandy silt sediment types. However, noticeable differences arise between the corrected and uncorrected data, which can be attributed to the alterations made during the data correction process. The best-performing ANN model achieves a testing accuracy of 0.75, indicating that it does not perfectly predict the given data. Acknowledging the potential for prediction errors when using corrected data is important.

Volume Backscattering Strength Estimation of Plankton from Water Column Multibeam Echosounder Data at Alor Strait, East Nusa Tenggara, Indonesia

Plankton support marine lives in significant roles as the main food source and ecological functions. However, marine information regarding plankton abundance is scanty and not distributed equally along Indonesian waters. In general, research in plankton abundance were conducted using conventional methods which unable to identify extensively. Hence, the main objective of this study was to estimate the qualitative volume backscattering strength (A ν ) of plankton abundance in the Alor Strait by utilizing multibeam echosounder water column data. Data acquisition was obtained during Jala Citra 3 – “Flores” 2023 expedition held by the Indonesian Navy Hydrographic and Oceanographic Center. The instrument used in this study is the Kongsberg EM302 multibeam echosounder operating at a frequency of 30kHz which acquired both bathymetry and water column data simultaneously. The results show the average value of the volume backscattering strength that varies in the distribution of sample pings at the research site. The largest average Av value is -120.071 dB at sample point 287 which placed in shallow water by the nearest island. Additionally, the smallest value is -142.783 dB at sample point 272 with around 1800 meters depth. It expects the correlation between depth and volume backscatter strength value which can be further investigated.

A Partially Buried Shipwreck Site: Implication from the Laoniu Reef Shipwreck No. 2

Underwater cultural relics are considered to be a scarce and valuable cultural resource. The significance of partially buried underwater cultural relics, which represents a distinct category of underwater cultural relics, has been largely overlooked in previous research. The Laoniu reef shipwreck No. 2, as an example of a partially buried shipwreck, was discovered through the utilization of side-scan sonar, multibeam echosounder and diver verification. In the investigation, multiangle surveys provided evidence to effectively discover this unique site. Beam-focusing technology and high-frequency side-scan sonar with proper distance and angular parameters can help to verify this site further. Given the potential significance of this particular site in relation to environmental transformations and human interventions, it is imperative to consider the insights derived from this case when reevaluating the methodology employed in underwater cultural relic surveys conducted in prospective regions. Consequently, this investigation holds the potential to unveil novel underwater cultural relic sites, thereby presenting a promising avenue for future discoveries.

The geomorphology of Ireland’s continental shelf

ABSTRACT The Irish Shelf Seabed Geomorphological Map (ISSGM) (v2023) presented here, is the first high-resolution geomorphological map of the entire Irish continental shelf. This large-scale mapping exercise took advantage of the vast INFOMAR multibeam echosounder dataset, and used a protocol of semi-automated mapping techniques to accurately and rapidly extract seabed features. All previous mapping efforts and existing literature on the Irish shallow shelf geomorphology have also been collated and integrated in the map, critically evaluating the previous interpretations. An internationally standardised classification scheme was adopted, aligning the ISSGM (v2023) to other international geomorphological work. At a national level, this detailed geomorphological digital map is intended primarily as a resource to better inform multiple offshore activities and management of the marine environment. The map also acts as a baseline for future studies in marine geomorphology, as it identifies gaps in the knowledge and highlights areas of contentious interpretation that require further work. The map is available online on the Irish Marine Atlas (https://atlas.marine.ie – Geology Theme).

A fully‐automatic side‐scan sonar simultaneous localization and mapping framework

AbstractSide‐scan sonar is a lightweight acoustic sensor that is frequently deployed on autonomous underwater vehicles (AUVs) to provide high‐resolution seafloor images. However, using side‐scan images to perform simultaneous localization and mapping (SLAM) remains a challenge when there is a lack of 3D bathymetric information and discriminant features in the side‐scan images. To tackle this, the authors propose a feature‐based SLAM framework using side‐scan sonar, which is able to automatically detect and robustly match keypoints between paired side‐scan images. The authors then use the detected correspondences as constraints to optimise the AUV pose trajectory. The proposed method is evaluated on real data collected by a Hugin AUV, using as a ground truth reference both manually‐annotated keypoints and a 3D bathymetry mesh from multibeam echosounder (MBES). Experimental results demonstrate that this approach is able to reduce drifts from the dead‐reckoning system. The framework is made publicly available for the benefit of the community.

ВИЗНАЧЕННЯ ТА ВРАХУВАННЯ СИСТЕМАТИЧНИХ ПОХИБОК ПРИ ВИМІРЮВАННІ НАВІГАЦІЙНИХ ГЛИБИН

The scientific study considers the issue of studying the features and determining the inherent values of systematic errors that occur when measuring navigation depths with multibeam echosounders, the elimination of these errors from the measurement results, which requires a special organization and methodology for performing multibeam echosounders tests. Systematic errors of navigation data can affect as on the magnitude of the depth itself, as well as on its planned position, and their influence should be evaluated after studying the angular measurements by echo sounder beams and calibration. The calibration of multi-beam systems, as well as the horizontal and vertical movements of the vessel, is much more significant and complicated compared to the calibration of single-beam systems. It is necessary to perform system accuracy control tests to confirm the reliability of multibeam echosounder data. These tests should preferably be carried out on board before taking depth measurements. For this, it is necessary to capture data, process and edit them in real time. Periodic performance of accurate calibration and testing is necessary to confirm that the multibeam survey meets the accuracy requirements. Multibeam echosounders calibration tests are performed with the aim of minimizing errors, taking into account the delay time of data acquisition, sea waves and changes in the ship's course. Keywords: multibeam echosounder, systematic errors, sensor calibration, hydrographic surveys, relief of the seabed, marine navigation chart, measured tacks, survey software.

Usability of multibeam echosounder for wreck investigations using backscatter and water column data in shallow waters

With the increasing accuracy of survey equipment and the strict survey standards in modern times, the use of sophisticated equipment for hydrographic surveys has become standard practice. Multibeam echosounders (MBES) are an essential part of the inventory. This paper presents an analysis of two data sets collected using MBES and examines their applicability for wreck investigation. While the topic of MBES and wreck investigation is vast, this study is limited to two types of MBES in shallow waters. The analysis of the data sets indicates that MBES can effectively be used for wreck investigation in shallower waters when utilizing water column and backscatter data, adhering to the rigorous survey standards of today. This approach offers valuable insights for enhancing wreck investigation capabilities and meeting the demands of modern hydrographic surveys.

Automated Detection and Tracking of Marine Mammals in the Vicinity of Tidal Turbines Using Multibeam Sonar

Understanding how marine animals behave around tidal turbines is essential if we are to quantify how individuals and populations may be affected by the installation of these devices in the coming decades. Our particular interest is in collision risk, and how this may be affected by the fine-scale behaviour of seals and small cetacean species around devices. We report on a study in which multibeam sonar data were collected close to an operational tidal turbine in Scotland continuously over a twelve-month period. The sonars provide high-resolution (a few cm) data over a 120° angle out to a range of 55 m at a rate of 10 frames per second. We describe a system which uses automatic computer algorithms to detect potential targets of interest, verified by human analysts using a sophisticated computer user interface to confirm detections and assign target species. To date, we have identified 359 tracks of marine mammals in the data, as well as several thousand tracks from fish and diving birds. These are currently being parameterised to study how these species react to the moving turbine rotors, and the data are now being used to explore the development of improved automated detection and classification algorithms.

Comparative vertical quality analysis using different methods for MBES data

Vertical quality evaluation in hydrographic surveys, particularly utilizing Multibeam Echosounders (MBES), presents challenges due to limited bathymetric information in obtaining homologous points and the absence of robust statistical criteria in depth estimation. This study addresses these challenges by proposing and validating the Point-to-Point (P2P) method, which prevents geostatistical interpolation while enhancing accuracy using a limited distance to search for the nearest neighbor and to find a probable homologous point. Three distinct methods were applied to compare depths of sounding lines and check line, namely Surface-to-Surface, Surface-to-Point, and Point-to-Point. The efficacy of the P2P method was established through a comprehensive evaluation of RMSE and ϕRobust, besides the discrepancies. This research underscores the significance of the P2P method, showcasing its superiority over conventional approaches and its potential to rectify the absence of statistical rigor in vertical quality assessment.

Evaluating the Performance of a Dual-Frequency Multibeam Echosounder for Small Target Detection

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.

Groundtruther: A QGIS plug-in for seafloor characterization

This work focuses on developing a software system for concurrently analyzing co-located multibeam echo sounder (MBES) datasets and seafloor imagery, fulfilling the need for a unified seafloor data exploration and analysis platform. The system comprises a graphical user interface where image browsing and geospatial data are linked and several toolboxes for extracting backscatter distribution, its angular response, and bathymetric derivatives to ultimately build detailed quantitative reports. The overall objective is to provide an efficient means of understanding the relationships between morphology, backscatter, and the observed biota and, thus, understanding the relationship between the physical and ecological elements of the seafloor. In addition, Groundtruther provides new ways to interpret remotely sensed information derived from MBES and aid the development of spatial distribution models. Furthermore, it could lead to the enrichment of ground-truth databases used to develop formal geophysical models that link acoustic backscatter observations to intrinsic properties of the seafloor.

PCD reconstruction, object classification and pose estimation for underwater vehicles using orthogonal multibeam forward looking sonar fusion

Orthogonal Multibeam Sonar Fusion (OMSF) is a recent fusion based method capable of producing accurate underwater 3D Point Cloud Data (PCD) from Multibeam Forward Looking Sonars (MFLS), enabling accurate seabed mapping and object scanning. This article provides methodical testing of OMSF reconstruction for MFLS accounting for operating frequency effects and object shapes. The article then proposes novel perception based applications for OMSF, consisting of classification technique integrating OMSF PCDs with a PCD based Convolutional Neural Network (CNN), and pose estimation method combining Orthogonal Feature Matching (OFM) bounding box regression with a pose regression CNN. Reconstruction test results show that OMSF is more accurate and efficient using higher frequency MFLS and achieves up to 53% higher accuracy on solid surfaces compared to hollow frames. Application tests based on an underwater garage dock show classification using OMSF PCDs can achieve 25% and 37% higher success rate and confidence while being more efficient, compared to using raw 3D sonar data. OFM bounding box regression produces 4.28% higher mean Intersection over Union (IoU), and 10% increase in ¿25% IoU metric compared to methods based on more traditional MFLS filtering. Similarly, end-to-end pose estimation achieves 6.25% higher success rate with OFM bounding box samples compared to ones obtained using traditional MFLS filtering.

The Potential of Multibeam Sonars as 3D Turbidity and SPM Monitoring Tool in the North Sea

Monitoring turbidity is essential for sustainable coastal management because an increase in turbidity leading to diminishing water clarity has a detrimental ecological impact. Turbidity in coastal waters is strongly dependent on the concentration and physical properties of particles in the water column. In the Belgian part of the North Sea, turbidity and suspended particulate matter (SPM) concentrations have been monitored for decades by satellite remote sensing, but this technique only focuses on the surface layer of the water column. Within the water column, turbidity and SPM concentrations are measured in stations or transects with a suite of optical and acoustic sensors. However, the dynamic nature of SPM variability in coastal areas and the recent construction of offshore windmill parks and dredging and dumping activities justifies the need to monitor natural and human-induced SPM variability in 3D instead. A possible solution lies in modern multibeam echosounders (MBES), which, in addition to seafloor bathymetry data, are also able to deliver acoustic backscatter data from the water column. This study investigates the potential of MBES as a 3D turbidity and SPM monitoring tool. For this purpose, a novel empirical approach is developed, in which 3D MBES water column and in-situ optical sensor datasets were collected during ship transects to yield an empirical relation using linear regression modeling. This relationship was then used to predict SPM volume concentrations from the 3D acoustic measurements, which were further converted to SPM mass concentrations using calculated densities. Our results show that these converted mean mass concentrations at the Kwinte and Westdiep swale areas are within the limits of the reported yearly averages. Moreover, they are in the same order of magnitude as the measured mass concentrations from Niskin water samples during each campaign. While there is still need for further improvement of acquisition and processing workflows, this study presents a promising approach for converting MBES water column data to turbidity and SPM measurements. This opens possibilities for improving future monitoring tools, both in scientific and industrial sectors.

Combining Two Classification Methods for Predicting Jakarta Bay Seabed Type Using Multibeam Echosounder Data

Classification of seabed types from multibeam echosounder data using machine learning techniques has been widely used in recent decades, such as Random Forest (RF), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Nearest Neighbor (NN). This study combines the two most frequently used machine learning techniques to classify and map the seabed sediment types from multibeam echosounder data. The classification model developed in this study is a combination of two machine learning classification techniques, namely Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN). This classification technique is called SV-KNN. Simply, SV-KNN adopts these two techniques to carry out the classification process. The SV-KNN technique begins with determining test data by specifying support vectors and hyperplanes, as was done on the SVM method, and executes the classification process using the K-NN. Clay, fine silt, medium silt, coarse silt, and fine sand are the five main classes produced by SVKNN. The SV-KNN method has an overall accuracy value of 87.38% and a Kappa coefficient of 0.3093.

Multibeam echosounder backscatter strength analysis for seafloor sediment identification in Damar Besar Island Waters, Thousand Islands

The condition of the Indonesian territory surrounded by the ocean requires technology to obtain seabed information to support the development of social, economic, and security activities. One of the technologies that can be used is the multibeam echosounder (MBES) hydroacoustic technology. MBES can scan seabed in a wide range, resulting in bathymetric data and backscatter values. The value of backscatter can be used to identify seafloor sediments. This study compared backscatter intensity values obtained from the MBES survey to the previous study. After identifying the sediment, the data obtained are mapped to produce a sediment distribution map. The types of sediment identified in the Damar Besar Island Waters are fine silt, medium silt, coarsed silt, silty sand, muddy sand, clayed sand, very fine sand, and fine sand with a range of backscatter values from -34,92 to -19,04 dB. Fine sand sediments are nearby the northern island while muddy sands and silty sands are scattered away from the island.

Multibeam sonar water column data simulation for improved and automated detection of gas seeps

Water column data (WCD) collected by multibeam echosounders (MBES) provide valuable datasets for the detection of gas bubbles. However, the presence of noise patterns, primarily caused by sidelobe interference from seafloor reverberation, limits the usability of WCD. We present a method for simulating characteristic noise patterns in WCD, particularly in Mills Cross configurations, by simulating predicted seafloor reverberation and transducer artefacts. The simulation is based on a reference pattern created from a subset of data and then applied as a correction based on the depth and seafloor backscatter of each ping within the dataset. Noise patterns can be removed by subtracting the simulated data from the original data, allowing for improved analysis of near-benthic water column features such as gas seeps. This method requires no prior knowledge of specific transducer characteristics, making it applicable to a wide range of MBES systems and acoustic targets. This method was applied to two MBES datasets collected simultaneously over a seep field in the Bay of Plenty, New Zealand, as part of the Quantitative Ocean-Column Imaging (QUOI) voyage in July 2018. We demonstrate the utility of applying a reference pattern for detecting the spatial extent of gas seep bases mapped using two different frequencies.

Fish distribution in three dimensions around the Block Island Wind Farm as observed with conventional and volumetric echosounders

AbstractObjectiveOffshore wind development is expected to expand rapidly along the East Coast of the United States within the next 10 years and will impact the biology and ecology of the flora and fauna as well as human activities, such as commercial and recreational fishing. The Block Island Wind Farm is a five‐turbine, 30‐MW wind array located about 6 km off the coast of Rhode Island and has been in operation since 2016.MethodsWe conducted a 4‐day acoustical and biological survey of the area during daylight hours to gain insight on the spatial distribution of fish species in and around the turbines. We utilized a hull‐mounted, downward‐looking Simrad 38‐/200‐kHz ES70 and a pole‐mounted iXblue SeapiX steerable Mills Cross, 150‐kHz, 1.6° resolution multibeam echosounder oriented downward to map the two‐ and three‐dimensional distributions using spiral and straight‐line transect patterns. We collected fish by using hook and line to verify the sources of acoustic backscatter and to measure length, sex, and diet.ResultBlack Sea Bass Centropristis striata were the most commonly caught species and appeared to be the primary constituents of the fish aggregations that were mapped by the acoustic systems. We found increased levels of acoustic backscatter within 200 m of the turbine structures, suggesting that they were attractive structures.ConclusionThese levels were not greater than backscatter levels in the surrounding area, suggesting that the proximate effect of the wind array was spatially limited.

A study on the prediction model of water column sound absorption coefficients considering the characteristics of the East Sea of South Korea

East Sea of Korea has the deepest water depth among Korean seas, and the water column changes are distinct in space and time, resulting in a wide range of water column sound absorption coefficients. In this study, we calculate and analysis the spatiotemporal variation of water column sound absorption coefficient in the East Sea of Korea, which has unique oceanographic characteristics. Using extensive CTD data from the Korea Oceanographic Data Centre (KODC) from 1991 to 2021, we calculate the sound absorption coefficient using the widely applied Francois–Garrison model and reveal the unique absorption characteristics of the East Sea. The derived coefficients are then used to calibrate and improve past multibeam echosounder system (MBES) data, which suffered from incomplete water column information and arbitrary mean values. To facilitate the calculation and development of sound absorption coefficient, a MATLAB-based graphical user interface (GUI) was developed. The results of this study contribute to the calculation and inference of sound absorption coefficients along spatial and temporal dimensions of the water column in the East Sea and to the extraction of more accurate seafloor information from MBES data. This study developed the way for future research focusing on algorithmic advances and new methodologies for the calculation of sound absorption coefficients in the West and South Seas of Korea.