Multibeam Echosounder Backscatter Data Research Articles

Sunken oil detection and classification using MBES backscatter data

Sunken oil incidents have occurred multiple times in the Bohai Sea over the past ten years. Currently, quick and effective sunken oil detection and classification remains a difficult problem. In this study, sonar detection experiments are conducted to obtain acoustic image samples using a multibeam echosounder (MBES) in a large seawater tank at the bottom of the area where the sunken oil is located. A series of MBES data corrections are constructed to generate backscatter strength images that can reflect the target characteristics directly. Meanwhile, eight-dimensional features are extracted, and a support vector machine (SVM) classification framework is built to classify the sunken oil and other interference targets. The results indicate that the MBES backscatter images provide an alternative approach for detecting and classifying sunken oil. The overall target classification accuracy reaches 88.5% by the SVM algorithm. Thus, this study provides a basis for further investigation of detecting sunken oil.

Toward monitoring and estimating the size of the HFO-contaminated seabed around a shipwreck using MBES backscatter data

Despite a progressive reduction of oil spills caused by the activity of maritime transportation, the latent sources of pollution still exist. Although the harmful impact of heavy fuel oil (HFO) on the marine environment is widely known, many shipwrecks cause contamination of the surrounding areas. In this paper, an approach to monitor the area of the HFO spill around a shipwreck is made using a bottom backscattering strength (BBS) obtained by a multibeam echosounder (MBES). As a case study, the s/s Stuttgart wreck located in the Gulf of Gdansk (Poland) is verified. Two different measurement campaigns have been carried out in shallow waters using low (190 kHz) and high (420 kHz) MBES frequency. The results indicate that the polluted area around s/s Stuttgart was estimated at 49.1 ha, which is around 18.3% more in comparison to the geological surveys made four years earlier.

Measuring Centimeter-Scale Sand Ripples Using Multibeam Echosounder Backscatter Data from the Brown Bank Area of the Dutch Continental Shelf

Backscatter data from multibeam echosounders are commonly used to classify seafloor sediment composition. Previously, it was found that the survey azimuth affects backscatter when small organized seafloor structures, such as sand ripples, are present. These sand ripples are too small to be detected in the multibeam bathymetry. Here, we show that such azimuth effects are time dependent and are useful to examine the orientation of sand ripples in relation to the flow direction of the tide. To this end, multibeam echosounder data at four different frequencies were gathered from the area of the Brown Bank in the North Sea. The acoustic results were compared to video and tide-flow data for validation. The sand ripples affected the backscatter at all frequencies, but for the lowest frequencies the effect was spread over more beam angles. Using the acoustic data made it possible to deduce the orientations of the sand ripples over areas of multiple square kilometers. We found that the top centimeter(s) of the seafloor undergoes a complete transformation every six hours, as the orientation of the sand ripples changes with the changing tide. Our methodology allows for morphology change detection at larger scales and higher resolutions than previously achieved.

Using seabed acoustic imagery to characterize and classify seabed sediment types in the pockmark area of the North Yellow Sea, China

Pockmarks are circular or elliptical depressions in the seabed that are geomorphologically similar but otherwise distinct from volcanic craters. Pockmarks are found at various depths in the ocean including on the continental shelf, the continental slope and the deep-sea plain. This paper presents data covering a large-scale pockmark group in the middle part of the shallow sea sedimentary plain of the North Yellow Sea at a water depth of 51–58 m. The distribution of pockmarks within this group is very dense, and pockmark diameters range from several hundred meters to several kilometers. Based on multibeam echo sounder data, a high-resolution image of the pockmark area is obtained by efficient image processing methods. The textural features of the seabed surface layer are then described and analyzed. Multibeam echo sounder backscatter data are combined with seabed sediment sampling data to develop a statistical model linking seabed backscatter sonar strength and sediment grain-size characteristics. Additionally, an improved BP neural network is used to realize rapid and automatic classification and identification of seabed sediment types. Fine-scale processing analysis of sonar images and acoustic classification of seabed sediments as presented in this paper are useful for determining seabed surface textures and local sediment distribution laws. This study thus provides a basis for further study of the formation and evolution of seabed pockmarks.

Acoustic Seabed Classification Based on Multibeam Echosounder Backscatter Data Using the PSO-BP-AdaBoost Algorithm: A Case Study From Jiaozhou Bay, China

When backpropagation neural network (BPNN) is often applied to supervised classification, problems arise, including a slow convergence rate, local extremum, and difficulty in determining the number of hidden layers and hidden nodes that affect the classification accuracy and efficiency. These problems can be overcome by using smarter network designs. Adaptive boosting (AdaBoost), which combines multiple weak classifiers to create a strong classifier, has a strong classification advantage. In this article, we propose an acoustic seabed classification method that combines AdaBoost with the particle swarm optimization (PSO). The PSO-BP-AdaBoost algorithm uses multibeam echosounder backscatter data to solve the multiclassification problem of diverse seafloor sediment types with small differences between types. We optimize a BPNN using the PSO algorithm to obtain the optimal initial weight and threshold and combine these to form an AdaBoost strong classifier. The input data is obtained from the sonar mosaic from multibeam echosounder backscatter data collected in Jiaozhou Bay using a series of fine processing techniques. These processing techniques result in 34-dimensional (34-D) features using ReliefF analysis. The most advantageous 8-D features are used as input into the AdaBoost algorithm based on one-level decision tree, PSO-BP algorithm, support vector machine (SVM), and PSO-BP-AdaBoost algorithm. The PSO-BP-AdaBoost classification model has better classification accuracy. The overall accuracy is improved by 12.68%, 6.78%, and 3.56%, respectively, which demonstrates that the PSO-BP-AdaBoost algorithm can be effectively applied to acoustic seabed classification and identification and achieves high precision.

Seafloor Characterization Using Multibeam Echosounder Backscatter Data: Methodology and Results in the North Sea

Seafloor characterization using multibeam echosounder (MBES) backscatter data is an active field of research. The observed backscatter curve (OBC) is used in an inversion algorithm with available physics-based models to determine the seafloor geoacoustic parameters. A complication is that the OBC cannot directly be coupled to the modeled backscatter curve (MBC) due to the correction of uncalibrated sonars. Grab samples at reference areas are usually required to estimate the angular calibration curve (ACC) prior to the inversion. We first attempt to estimate the MBES ACC without grab sampling by using the least squares cubic spline approximation method implemented in a differential evolution optimization algorithm. The geoacoustic parameters are then inverted over the entire area using the OBCs corrected for the estimated ACC. The results indicate that a search for at least three geoacoustic parameters is required, which includes the sediment mean grain size, roughness parameter, and volume scattering parameter. The inverted mean grain sizes are in agreement with grab samples, indicating reliability and stability of the proposed method. Furthermore, the interaction between the geoacoustic parameters and Bayesian acoustic classes is investigated. It is observed that higher backscatter values, and thereby higher acoustic classes, should not only be attributed to (slightly) coarser sediment, especially in a homogeneous sedimentary environment such as the Brown Bank, North Sea. Higher acoustic classes should also be attributed to larger seafloor roughness and volume scattering parameters, which are not likely intrinsic to only sediment characteristics but also to other contributing factors.

Seafloor Classification in a Sand Wave Environment on the Dutch Continental Shelf Using Multibeam Echosounder Backscatter Data

High resolution maps of sandy seafloors are valuable to understand seafloor dynamics, plan engineering projects, and create detailed benthic habitat maps. This paper presents multibeam echosounder backscatter classification results of the Brown Bank area of the North Sea. We apply the Bayesian classification method in a megaripple and sand wave area with significant slopes. Prior to the classification, corrections are implemented to account for the slopes. This includes corrections on the backscatter value and its corresponding incident angle. A trade-off in classification resolutions is found. A higher geo-acoustic resolution is obtained at the price of losing spatial resolution, however, the Bayesian classification method remains robust with respect to these trade-off decisions. The classification results are compared to grab sample particle size analysis and classified video footage. In non-distinctive sedimentary environments, the acoustic classes are not attributed to only the mean grain size of the grab samples but to the full spectrum of the grain sizes. Finally, we show the Bayesian classification results can be used to characterize the sedimentary composition of megaripples. Coarser sediments were found in the troughs and on the crests, finer sediments on the stoss slopes and a mixture of sediments on the lee slopes.

Performance of Multibeam Echosounder Backscatter-Based Classification for Monitoring Sediment Distributions Using Multitemporal Large-Scale Ocean Data Sets

Obtaining an overview of the spatial and temporal distribution of seabed sediments is of high interest for multiple research disciplines. Multibeam echosounders allow for the mapping of seabed sediments with high area coverage. In this paper, the repeatability of acoustic classification derived from multibeam echosounder backscatter is addressed. To this end, multibeam echosounder backscatter data acquired on the Cleaver Bank (North Sea) during five different surveys is employed using two different classification methods, i.e., a method based on the principal component analyses and the Bayesian technique. Different vessels were used for the different surveys. The comparison of the classification results between the different surveys indicates good repeatability. This repeatability demonstrates the potential of using backscatter for long-term environmental monitoring. However, the use of different classification methods results in somewhat different classification maps. Monitoring, therefore, requires the consistent use of a single method. Furthermore, it is found that the statistical characteristics of backscatter is such that clustering algorithms are less suited to discern the number of sediment types present in the study area. The Bayesian technique accounting for backscatter statistics is therefore recommended. A strong positive correlation between backscatter and median grain size for finer sediments ( 0.5 mm. Consequently, for the situation considered a unique assignment of sediment type to acoustic class is not possible for these coarser sediments.

Use of multibeam echo sounder backscatter and bathymetry data to reveal new insights into the Campeche Escarpment

Multibeam echo sounder (MBES) bathymetry and backscatter data from the 2013 R/V Falkor survey are used to define the Cretaceous–Paleogene (K-Pg) boundary deposit outcropping on the Campeche Escarpment and to characterize large-scale erosional processes on the Campeche shelf. We delineate the escarpment into four geomorphic provinces based on submarine canyon density and morphology, as well as pronounced geomorphological differences formed in response to erosional processes along strike of the Campeche Escarpment. Large-scale, retrogressive-appearing mass wasting has left high-angle scarps where large slabs of sediment have eroded the escarpment edge, giving the slope a terraced appearance. This erosion reveals higher backscatter intensity material overlain with remnants of lower backscatter intensity sediments within the slide scar. We infer this high-intensity backscatter material to be predominantly carbonate layers on which erosion occurred. The most prominent high-intensity backscatter layer is interpreted to identify the K-Pg boundary deposit (KPBD) that was reported previously based on a steep bathymetric change and constrained by cores from Deep Sea Drilling Project (DSDP) Sites 86 and 94. Given its proximity to the impact crater, the KPBD is likely to be composed of a massive impact breccia. Upslope, high-intensity backscatter layers are inferred to represent postimpact carbonates that progressively infilled the basin flank after the Chicxulub bolide impact. Lower intensity backscatter material is inferred to be fine-grained Cenozoic sediment drape. The stark contrast observed in MBES backscatter data between high-intensity carbonate material and lower-intensity sediments refines and constrains the location of the K-Pg boundary deposit exposed along the Campeche escarpment and provides new insights into the carbonate platform's postbolide impact evolution as observed in the erosional styles of the Campeche escarpment.

The influence of geomorphology and sedimentary processes on benthic habitat distribution and littoral sediment dynamics: Geraldton, Western Australia

Understanding of the processes regulating sediment transport, accumulation and erosion requires an appropriate mapping of coastal geomorphology, seabed sediments and benthic habitat distribution to allow management issues to be identified, understood and addressed. In this study multibeam echo-sounder data were used to map shallow water geomorphological features and the spatial distribution of benthic habitats, with the support of underwater imagery for ground truthing the acoustic data. At Geraldton, sediment analyses have revealed a dominant biogenic nature, with modern carbonate sedimentation linked to the seagrass and macroalgal carbonate factories colonising these shallow (< 30 m) coastal embayments. Whilst seagrasses are common on sheltered hardgrounds blanketed by fine sand, macroalgae were found on high energy limestone reefs. The distribution of sand bar and sheet systems is regulated by wave induced sediment transport with the influence of pre-existing seabed topography. Exposure to wave energy, seabed geomorphology and sediment characteristics is closely related to the distribution of benthic habitats and sand substrates, highlighting the value of an integrated analysis of these parameters. The capability of multibeam echo-sounder backscatter data to discriminate between seagrass meadows, macroalgal communities and sandy substrates was also evaluated and the acoustic response from the seabed was better explained by considering together seafloor geomorphology and biota type, as both these parameters influence backscatter strength. • Coastal geomorphology features are mapped and the processes regulating their formation described • Seagrass meadows and macroalgal communities are active carbonate sediment producers • Temperate water benthic habitat distribution is mapped on the basis of multibeam echo-sounder backscatter data • Seabed geomorphology and sediment characteristics are linked to the distributions of benthic habitats and substrates

Seabed Mixed Sediment Classification with Multi-beam Echo Sounder Backscatter Data in Jiaozhou Bay

The multi-beam echo sounder system can not only obtain high-precision seabed bathymetry data, but also obtain high-resolution seabed backscatter strength data. A number of studies have applied acoustic remote sensing method to classify seabed sediment type with multi-beam backscatter strength data, and obtained better classification results than the traditional sediment sampling method. However, these studies mainly focus on the single type sediment classification or seabed mixed sediment classification using single beam data, not multi-beam echo sounder data. Based on backscatter strength data by a high-frequency (300 kHz) Simrad EM3000 multi-beam echo sounder and seabed sediment sampling data of Jiaozhou Bay in Qingdao, China, we establish the relation model between seabed backscatter strength and sediment type characteristics after data processing and corrections. The purpose of data processing is to diminish or weaken the influence of local bottom slope and near nadir reflection on backscatter strength data. Processed backscatter strength data through corrections and compensations can better characterize the features of the seabed sediments. Applying the back propagation neural network method based on genetic algorithm, we present a fast and accurate seabed classification method in this paper which could identify not just a single type of sediment-like rock, sand but also mixed types of sediment like sand gravel, clayey silt and sand-silt-clay.

Improving seabed classification from Multi-Beam Echo Sounder (MBES) backscatter data with visual data mining

Multi-Beam Echo Sounders are often used for classification of seabed type, as there exists a strong link between sonar backscatter and sediment characteristics of the seabed. Most of the methods for seabed classification from MBES backscatter create a highly-dimensional data set of statistical features and then use a combination of Principal Component Analysis and k-means clustering to derive classes. This procedure can be time consuming for contemporary large MBES data sets with millions of records. This paper examines the complexity of one of most commonly used classification approaches and suggests an alternative where feature data set is optimised in terms of dimensionality using computational and visual data mining. Both the original and the optimised method are tested on an MBES backscatter data set and validated against ground truth. The study found that the optimised method improves accuracy of classification and reduced complexity of processing. This is an encouraging result, which shows that bringing together methods from acoustic classification, visual data mining, spatial analysis and remote sensing can support the unprecedented increases in data volumes collected by contemporary acoustic sensors.

High-frequency multibeam echo-sounder measurements of seafloor backscatter in shallow water: Part 1 – Data acquisition and processing

Abstract Methods for processing multibeam echo-sounder (MBES) bathymetry data are well established, how-ever, there is no universal and established approach to processing and interpretation of MBES backscatter data. The aim of this paper is to (1) detail the different backscatter logging methods implemented in modern high-frequency MBES systems used primarily in shallow water; (2) describe and suggest appropriate methods of data processing; and (3) discuss likely errors and artefacts expected from MBES backscatter processing. These issues are illustrated using data collected with a Reson SeaBat MBES in coastal shelf areas of Australia, although the methods and errors discussed are relevant to other high-frequency shallow-water MBES systems. Production of backscatter mosaics and analysis of backscatter for seafloor classification using MBES data are considered in Part 2 – Mosaic production, analysis and classification (Parnum and Gavrilov, 2011), published alongside this paper.

Riverbed sediment classification using multi-beam echo-sounder backscatter data

A method has recently been developed that employs multi-beam echo-sounder backscatter data to both obtain the number of sediment classes and discriminate between them by applying the Bayes decision rule to multiple hypotheses [Simons and Snellen, Appl. Acoust. 70, 1258-1268 (2009)]. In deep water, the number of scatter pixels within the beam footprint is large enough to ensure Gaussian distributions for the backscatter strengths and to increase the discriminative power between acoustic classes. In very shallow water (<10 m), however, this number is too small. This paper presents an extension of this high-frequency methodology for these environments, together with a demonstration of its performance using backscatter data from the river Waal, The Netherlands. The objective of this work is threefold. (i) Increasing the discriminating power of the classification method: high-resolution bathymetry data allow precise bottom slope corrections for obtaining the true incident angle, and the high-resolution backscatter data reduce the statistical fluctuations via an averaging procedure. (ii) Performing a correlation analysis: the dependence of acoustic backscatter classification on sediment physical properties is verified by observing a significant correlation of 0.75 (and a disattenuated correlation of 0.90) between the classification results and sediment mean grain size. (iii) Enhancing the statistical description of the backscatter intensities: angular evolution of the K-distribution shape parameter indicates that the riverbed is a rough surface, in agreement with the results of the core analysis.

A Bayesian approach to seafloor classification using multi-beam echo-sounder backscatter data

Seafloor classification using acoustic remote sensing techniques is an attractive approach due to its high-coverage capabilities and limited costs. The multi-beam echo-sounder (MBES) system provides high-resolution bathymetry and backscatter information with 100% coverage. In this paper, we present a seafloor classification method that employs the MBES backscatter data. The method uses the averaged backscatter data per beam. It, therefore, is independent on the quality of the MBES calibration. Also, its performance is insensitive to seafloor type variation along the MBES swathe and corrections for the angular dependence of the backscatter are not needed. The method accounts for the ping-to-ping variability of the backscatter intensity. It estimates both the number of seafloor types present in the survey area and the probability density function for the backscatter strength at a certain angle for each of the seafloor types. Application of the method to MBES backscatter data acquired in a well-known test area in the North Sea shows very good agreement with available ground truth. The method’s discriminatory performance for this area is demonstrated to be comparable to that of taking samples of the sediment. All seafloor types known to be present in the area are resolved for. Application of the method to the Stanton bank data set shows clearly separable areas that differ in seafloor composition.

Using the MBES for classification of riverbed sediments

For keeping the Dutch rivers suitable for commercial activities measures are required. For example, the bottom of the river Waal, connecting Rotterdam with German industrial areas, is subsiding. Since the subsidence varies along the river, dangerous shoals occur. Sediment suppletions are planned to counteract the subsidence. Appropriate suppletion material is expected to keep the bottom more stable. To monitor the suppletion effectiveness, multibeam‐echosounder (MBES) measurements are planned, allowing for simultaneous estimation of bathymetry and sediment composition. For the latter, we apply a method employing the MBES backscatter data. It estimates the number of sediment types present in the survey area and discriminates between them by applying the Bayes decision rule for multiple hypotheses, implicitly accounting for the backscatter strength ping‐to‐ping variability. The method's applicability was demonstrated in a well‐surveyed test area (North Sea). In 2007, MBES measurements were acquired at the Waal, accompanied with extensive sediment grabbing. Contrary to the test area, water depths are very shallow and significant bottom slopes exist, requiring corrections. The lower water depths correspond to smaller beam‐footprints, resulting in a higher ping‐to‐ping variability. Consequently the discriminating power between sediments will decrease. The performance of the classification method for this river environment is assessed.