Remote estimation of surficial seafloor properties through the application Angular Range Analysis to multibeam sonar data

The variation of the backscatter strength with the angle of incidence is an intrinsic property of the seafloor, which can be used in methods for acoustic seafloor characterization. Although multibeam sonars acquire backscatter over a wide range of incidence angles, the angular information is normally neglected during standard backscatter processing and mosaicking. An approach called Angular Range Analysis has been developed to preserve the backscatter angular information, and use it for remote estimation of seafloor properties. Angular Range Analysis starts with the beam-by-beam time-series of acoustic backscatter provided by the multibeam sonar and then corrects the backscatter for seafloor slope, beam pattern, time varying and angle varying gains, and area of insonification. Subsequently a series of parameters are calculated from the stacking of consecutive time series over a spatial scale that approximates half of the swath width. Based on these calculated parameters and the inversion of an acoustic backscatter model, we estimate the acoustic impedance and the roughness of the insonified area on the seafloor. In the process of this inversion, the behavior of the model parameters is constrained by established inter-property relationships. The approach has been tested using a 300 kHz Simrad EM3000 multibeam sonar in Little Bay, NH. Impedance estimates are compared to in situ measurements of sound speed. The comparison shows a very good correlation, indicating the potential of this approach for robust seafloor characterization.     

多波束反向散射强度数据处理研究

在探讨多波束测深系统反向散射强度与海底底质类型的关系基础上,研究影响反向散射强度的各种因素,主要分析了海底地形起伏、中央波束区反射信号对反向散射强度的影响,并给出了消除这些影响的方法;将处理后的“纯”反向散射强度数据镶嵌生成海底声像图,为海底底质类型划分以及地貌解译提供了基础数据和辅助判读依据.     

基于Kongsberg EM多波束的Snippet数据处理方法

多波束声呐记录的海底后向散射片段(Snippet)数据处理成角度响应曲线和地理编码(Mosaic)图像可以 帮助识别海底底质类型和反映地貌形态,这一过程包括辐射校正、角度响应改正(AVG)和几何地理编码,但不同的多波束系统硬件在辐射校正和角度响应改正方法上存在差异且传统处理方法忽略了声呐系统本身的指向性模型随时间变化的事实。以声呐方程为基础,针对Kongsberg EM 多波束系统提出了一套完整的Snippet数据处理流程,并分析了各步骤中存在的可变性,给出了每一步的处理建议,最后将此方法应用于EM2040浅水多波束实测数据,并验证了该方法的有效性和实用性。     

Processing Multibeam Backscatter Data

A new highly precise source of data has recently become available using multibeam sonar systems in hydrography. Multibeam sonar systems can provide hydrographic quality depth data as well as high-resolution seafloor sonar images. We utilize the seafloor backscatter strength data of each beam from multibeam sonar and the automatic classification technology so that we can get the seafloor type identification maps. In this article, analyzing all kinds of error effects in backscatter strength, data are based on the relationship between backscatter strength and seafloor types. We emphasize particularly analyzing the influences of local bottom slope and near nadir reflection in backscatter strength data. We also give the correction algorithms and results of these two influent factors. After processing the raw backscatter strength data and correcting error effects, we can get processed backscatter strength data which reflect the features of seafloor types only. Applying the processed backscatter strength data and mosaicked seafloor sonar images, we engage in seafloor classification and geomorphy interpretation in future research.     

Processing Multibeam Backscatter Data

A new highly precise source of data has recently become available using multibeam sonar systems in hydrography. Multibeam sonar systems can provide hydrographic quality depth data as well as high-resolution seafloor sonar images. We utilize the seafloor backscatter strength data of each beam from multibeam sonar and the automatic classification technology so that we can get the seafloor type identification maps. In this article, analyzing all kinds of error effects in backscatter strength, data are based on the relationship between backscatter strength and seafloor types. We emphasize particularly analyzing the influences of local bottom slope and near nadir reflection in backscatter strength data. We also give the correction algorithms and results of these two influent factors. After processing the raw backscatter strength data and correcting error effects, we can get processed backscatter strength data which reflect the features of seafloor types only. Applying the processed backscatter strength data and mosaicked seafloor sonar images, we engage in seafloor classification and geomorphy interpretation in future research.     

Validation of automated supervised segmentation of multibeam backscatter data from the Chatham Rise,New Zealand

Using automated supervised segmentation of multibeam backscatter data to delineate seafloor substrates is a relatively novel technique. Low-frequency multibeam echosounders (MBES), such as the 12-kHz EM120, present particular difficulties since the signal can penetrate several metres into the seafloor, depending on substrate type. We present a case study illustrating how a non-targeted dataset may be used to derive information from multibeam backscatter data regarding distribution of substrate types. The results allow us to assess limitations associated with low frequency MBES where sub-bottom layering is present, and test the accuracy of automated supervised segmentation performed using SonarScope® software. This is done through comparison of predicted and observed substrate from backscatter facies-derived classes and substrate data, reinforced using quantitative statistical analysis based on a confusion matrix. We use sediment samples, video transects and sub-bottom profiles acquired on the Chatham Rise, east of New Zealand. Inferences on the substrate types are made using the Generic Seafloor Acoustic Backscatter (GSAB) model, and the extents of the backscatter classes are delineated by automated supervised segmentation. Correlating substrate data to backscatter classes revealed that backscatter amplitude may correspond to lithologies up to 4 m below the seafloor. Our results emphasise several issues related to substrate characterisation using backscatter classification, primarily because the GSAB model does not only relate to grain size and roughness properties of substrate, but also accounts for other parameters that influence backscatter. Better understanding these limitations allows us to derive first-order interpretations of sediment properties from automated supervised segmentation.     

Backscatter calibration of high-frequency multibeam echosounder using a reference single-beam system,on natural seafloor

The calibration of multibeam echosounders for backscatter measurements can be conducted efficiently and accurately using data from surveys over a reference natural area, implying appropriate measurements of the local absolute values of backscatter. Such a shallow area (20-m mean depth) has been defined and qualified in the Bay of Brest (France), and chosen as a reference area for multibeam systems operating at 200 and 300 kHz. The absolute reflectivity over the area was measured using a calibrated single-beam fishery echosounder (Simrad EK60) tilted at incidence angles varying between 0° and 60° with a step of 3°. This reference backscatter level is then compared to the average backscatter values obtained by a multibeam echosounder (here a Kongsberg EM 2040-D) at a close frequency and measured as a function of angle; the difference gives the angular bias applicable to the multibeam system for recorded level calibration. The method is validated by checking the single- and multibeam data obtained on other areas with sediment types different from the reference area.     

Multibeam sonar backscatter data processing

Multibeam sonar systems now routinely record seafloor backscatter data, which are processed into backscatter mosaics and angular responses, both of which can assist in identifying seafloor types and morphology. Those data products are obtained from the multibeam sonar raw data files through a sequence of data processing stages that follows a basic plan, but the implementation of which varies greatly between sonar systems and software. In this article, we provide a comprehensive review of this backscatter data processing chain, with a focus on the variability in the possible implementation of each processing stage. Our objective for undertaking this task is twofold: (1) to provide an overview of backscatter data processing for the consideration of the general user and (2) to provide suggestions to multibeam sonar manufacturers, software providers and the operators of these systems and software for eventually reducing the lack of control, uncertainty and variability associated with current data processing implementations and the resulting backscatter data products. One such suggestion is the adoption of a nomenclature for increasingly refined levels of processing, akin to the nomenclature adopted for satellite remote-sensing data deliverables.     

Toward remote seafloor classification using the angular response ofacoustic backscattering: a case study from multiple overlapping GLORIAdata

While the average seafloor backscatter strength within a narrow range of grazing angles can be used as a first-order classification tool, this technique often fails to distinguish seafloors of known differing geological character. In order to resolve such ambiguities, it is necessary to examine the variation in backscatter strength as a function of grazing angle. For this purpose, a series of multiply overlapping GLORIA sidescan sonar images (6.5 kHz) have been obtained in water depths ranging from 1000 to 2500 m. To constrain the placement of acoustic backscatter measurements and to measure the true impinging angle of the incident wave, the corresponding seafloor was simultaneously surveyed using the Seabeam multibeam system. As a result of the multiple overlap, the angular response of seafloor backscatter strength may be derived for regions much smaller than the swath width. By using the derived angular response of seafloor backscatter strength in regions for which sediment samples exist, an empirical seafloor classification scheme is proposed based on the shape, variance, and magnitude of the angular response. Because of the observed variability in the shape of the angular response with differing seafloor types, routine normalization of single-pass swath data to an equivalent single grazing angle image cannot be achieved. As a result, for the case of single-pass surveys, confident seafloor classification may only be possible for regions approaching the scale of the swath width     

Multi-angle backscatter classification and sub-bottom profiling for improved seafloor characterization

This study applies three classification methods exploiting the angular dependence of acoustic seafloor backscatter along with high resolution sub-bottom profiling for seafloor sediment characterization in the Eckernförde Bay, Baltic Sea Germany. This area is well suited for acoustic backscatter studies due to its shallowness, its smooth bathymetry and the presence of a wide range of sediment types. Backscatter data were acquired using a Seabeam1180 (180 kHz) multibeam echosounder and sub-bottom profiler data were recorded using a SES-2000 parametric sonar transmitting 6 and 12 kHz. The high density of seafloor soundings allowed extracting backscatter layers for five beam angles over a large part of the surveyed area. A Bayesian probability method was employed for sediment classification based on the backscatter variability at a single incidence angle, whereas Maximum Likelihood Classification (MLC) and Principal Components Analysis (PCA) were applied to the multi-angle layers. The Bayesian approach was used for identifying the optimum number of acoustic classes because cluster validation is carried out prior to class assignment and class outputs are ordinal categorical values. The method is based on the principle that backscatter values from a single incidence angle express a normal distribution for a particular sediment type. The resulting Bayesian classes were well correlated to median grain sizes and the percentage of coarse material. The MLC method uses angular response information from five layers of training areas extracted from the Bayesian classification map. The subsequent PCA analysis is based on the transformation of these five layers into two principal components that comprise most of the data variability. These principal components were clustered in five classes after running an external cluster validation test. In general both methods MLC and PCA, separated the various sediment types effectively, showing good agreement (kappa >0.7) with the Bayesian approach which also correlates well with ground truth data (r²?>?0.7). In addition, sub-bottom data were used in conjunction with the Bayesian classification results to characterize acoustic classes with respect to their geological and stratigraphic interpretation. The joined interpretation of seafloor and sub-seafloor data sets proved to be an efficient approach for a better understanding of seafloor backscatter patchiness and to discriminate acoustically similar classes in different geological/bathymetric settings.     

Seafloor sediment classification from single beam echo sounder data using LVQ network

Seafloor sediment classification based on echo characteristics obtained from single-beam echosounder is very useful in remote and instant sediment classification. Results of different classification techniques using such data provide robust results when the acoustic beam has a normal incidence with the seabottom. This may not always be true and show poor classification, with the data acquired during rough sea periods corresponding to both oblique and normal incidence of the acoustic pulse, due to roll and pitch motion of the ship. In the present study, an attempt is made to exploit the artificial neural network (ANN) techniques for better classification with such data. Learning Vector Quantisation (LVQ) is a supervised learning algorithm of ANN that is found to be an effective tool and show good performance. The input data to the network include the roughness index (E1) and hardness index (E2) derived from echo characteristics. The network utilizes the competitive learning, a distance function in the first layer and a linear function in the second layer. The network was tried with a different size of hidden neurons and training data size to see the influence on classification. It is found that with ten neurons in the first layer and four neurons in the second layer good performance in classification for the data was achieved.     

Characterizing Indian Ocean manganese nodule-bearing seafloor using multi-beam angular backscatter

Application of quantitative angular backscatter modelling to manganese nodule-bearing areas of the Central Indian Ocean Basin (CIOB) has been initiated at NIO during the year 1998. Studies were aimed to establish the suitability of seafloor backscattering in delineating seafloor parameters characteristic of nodule-rich sediments. In this paper, processed Hydrosweep multi-beam backscatter data from 45 spot locations in the CIOB (where nodule samples are available) were analysed to estimate seafloor and sediment volume roughness parameters. The application of a composite roughness model to a nodule-bearing region (6,600 km²) of the CIOB, to determine seafloor interface roughness parameters from a multi-beam backscatter dataset, shows only four power law sets. The results attest 80% of the nodule-bearing seafloor to be smooth in terms of interface roughness parameters at micro-topographic level. The sediment volume roughness parameters are dominant only in 29% of the smooth interface roughness sites. This indicates that 51% of the seafloor area possesses negligible (interface and volume) roughness. A critical analysis using pseudo-side-scan records from 12 selected locations in the study area affirms the combined importance of the seafloor interface and sediment volume roughness parameters for precise determination of manganese nodule abundance.     

Operational Parameters,Data Density and Benthic Ecology: Considerations for Image-Based Classification of Multibeam Backscatter

Efforts to develop a procedurally robust method for automated classification of multibeam backscatter have taken a variety of approaches (e.g., image-based, textural, angular range analysis). For image-based classification, little research has focused on the roles of operational parameters of vessel and sonar system in affecting the final classification. Repeat multibeam surveys (2005 and 2006) conducted at the same area with different sounding densities were classified using QTC-Multiview. Comparison of class areas revealed 78% agreement between classifications derived from the two surveys. Cross-tabulation of ground truth video and class demonstrate 71% agreement in the low-density survey and 77% for the high-density. Differences between classifications are primarily attributed to variation in along track data density, errors in the compensation process, and/ or insufficient quality control of the input data. Natural change detection at the scales observed was determined not to be practically discernable from the errors associated with the classification process.     

Modelling the distribution of hard seabed using calibrated multibeam acoustic backscatter data in a tropical,macrotidal embayment: Darwin Harbour,Australia

Spatial information on the distribution of seabed substrate types in high use coastal areas is essential to support their effective management and environmental monitoring. For Darwin Harbour, a rapidly developing port in northern Australia, the distribution of hard substrate is poorly documented but known to influence the location and composition of important benthic biological communities (corals, sponges). In this study, we use angular backscatter response curves to model the distribution of hard seabed in the subtidal areas of Darwin Harbour. The angular backscatter response curve data were extracted from multibeam sonar data and analysed against backscatter intensity for sites observed from seabed video to be representative of “hard” seabed. Data from these sites were consolidated into an “average curve”, which became a reference curve that was in turn compared to all other angular backscatter response curves using the Kolmogorov–Smirnov goodness-of-fit. The output was used to generate interpolated spatial predictions of the probability of hard seabed (p-hard) and derived hard seabed parameters for the mapped area of Darwin Harbour. The results agree well with the ground truth data with an overall classification accuracy of 75% and an area under curve measure of 0.79, and with modelled bed shear stress for the Harbour. Limitations of this technique are discussed with attention to discrepancies between the video and acoustic results, such as in areas where sediment forms a veneer over hard substrate.     

Inhomogeneous substrate analysis using EM300 backscatter imagery

Backscatter reflectivity from multibeam echo-sounders provides a powerful tool to efficiently characterize seafloor substrates. A comprehensive EM300 bathymetric and backscatter survey has been completed of Cook Strait, in central New Zealand. This paper presents a detailed analysis of the realtime corrections applied to the raw EM300 multibeam data and additional corrections required to compute angular variations of the backscatter strength. The corrections, including the local absorption coefficient, the influence of seafloor topography and sound refraction in the water column, are determined for different Cook Strait seafloor substrates. Modifying MB-System software code, we extracted the backscatter signal parameters in order to quantify the raw backscatter strength and apply additional processing. Profiles of backscatter strength versus incidence angle were computed for a variety of sites characterized by flat seafloor and homogeneous substrates, and for which ground-truth data were available. For each homogeneous site, different but characteristic backscatter profiles are observed that can be interpreted in terms of sediment facies. To analyze heterogeneous substrates, we present a statistical technique, based on a 3-dimensional distribution of (incidence angle, backscatter strength) couples that preserves geological information of the substrate components. This analysis, using backscatter data acquired on a submarine volcano, north of New Zealand, clearly differentiates soft sediments and lava flows within a heterogeneous substrate.     

Analysis of seafloor backscatter strength dependence on the survey azimuth using multibeam echosounder data

The sediment backscatter strength measured by multibeam echosounders is a key feature for seafloor mapping either qualitative (image mosaics) or quantitative (extraction of classifying features). An important phenomenon, often underestimated, is the dependence of the backscatter level on the azimuth angle imposed by the survey line directions: strong level differences at varying azimuth can be observed in case of organized roughness of the seabed, usually caused by tide currents over sandy sediments. This paper presents a number of experimental results obtained from shallow-water cruises using a 300-kHz multibeam echosounder and specially dedicated to the study of this azimuthal effect, with a specific configuration of the survey strategy involving a systematic coverage of reference areas following “compass rose” patterns. The results show for some areas a very strong dependence of the backscatter level, up to about 10-dB differences at intermediate oblique angles, although the presence of these ripples cannot be observed directly—neither from the bathymetry data nor from the sonar image, due to the insufficient resolution capability of the sonar. An elementary modeling of backscattering from rippled interfaces explains and comforts these observations. The consequences of this backscatter dependence upon survey azimuth on the current strategies of backscatter data acquisition and exploitation are discussed.     

基于多波束背向散射数据的海底底质分类

基于精细处理后的多波束数据生成背向散射影像图,利用灰度纹理共生阵提取影像纹理特征参数,采用支持向量机的神经网络(SVM)对背向散射影像进行底质分类研究。通过实测大面积、海量数据对该方法进行评价和验证,结果表明,该方法可获得比传统分类方法更高的分类精度,这种面状的分类弥补了传统点状分类的缺陷,使得大规模、大范围、高效快速的海底底质分类成为可能,为海洋地质调查、海洋工程建设、海底矿产资源开发等提供一种新型的科学的技术方法和可靠的地质基础资料。     

Seafloor acoustic remote sensing with multibeam echo-sounders and bathymetric sidescan sonar systems

This paper examines the potential for remote classification of seafloor terrains using a combination of quantitative acoustic backscatter measurements and high resolution bathymetry derived from two classes of sonar systems currently used by the marine research community: multibeam echo-sounders and bathymetric sidescans sonar systems. The high-resolution bathymetry is important, not only to determine the topography of the area surveyed, but to provide accurate bottom slope corrections needed to convert the arrival angles of the seafloor echoes received by the sonars into true angles of incidence. An angular dependence of seafloor acoustic backscatter can then be derived for each region surveyed, making it possible to construct maps of acoustic backscattering strength in geographic coordinates over the areas of interest. Such maps, when combined with the high-resolution bathymetric maps normally compiled from the data output by the above sonar systems, could be very effective tools to quantify bottom types on a regional basis, and to develop automatic seafloor classification routines.     

Shallow-water imaging multibeam sonars: A new tool for investigating seafloor processes in the coastal zone and on the continental shelf

Hydrographic quality bathymetry and quantitative acoustic backscatter data are now being acquired in shallow water on a routine basis using high frequency multibeam sonars. The data provided by these systems produce hitherto unobtainable information about geomorphology and seafloor geologic processes in the coastal zone and on the continental shelf.Before one can use the multibeam data for hydrography or quantitative acoustic backscatter studies, however, it is essential to be able to correct for systematic errors in the data. For bathymetric data, artifacts common to deep-water systems (roll, refraction, positioning) need to be corrected. In addition, the potentially far greater effects of tides, heave, vessel lift/squat, antenna motion and internal time delays become of increasing importance in shallower water. Such artifacts now cause greater errors in hydrographic data quality than bottom detection. Many of these artifacts are a result of imperfect motion sensing, however, new methods such as differential GPS hold great potential for resolving such limitations. For backscatter data, while the system response is well characterised, significant post processing is required to remove residual effects of imaging geometry, gain adjustments and water column effects. With the removal of these system artifacts and the establishment of a calibrated test site in intertidal regions (where the seabed may be intimately examined by eye) one can build up a sediment classification scheme for routine regional seafloor identification.When properly processed, high frequency multibeam sonar data can provide a view of seafloor geology and geomorphology at resolutions of as little as a few decimetres. Specific applications include quantitative estimation of sediment transport rates in large-scale sediment waves, volume effects of iceberg scouring, extent and style of seafloor mass-wasting and delineation of structural trends in bedrock. In addition, the imagery potentially provides a means of quantitative classification of seafloor lithology, allowing sedimentologists the ability to examine spatial distributions of seabed sediment type without resorting to subjective estimation or prohibitively expensive bottom-sampling programs. Using Simrad EM100 and EM1000 sonars as an example, this paper illustrates the nature and scale of possible artifacts, the necessary post-processing steps and shows specific applications of these sonars.