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.     

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.     

Seafloor multibeam backscatter calibration experiment: comparing 45°-tilted 38-kHz split-beam echosounder and 30-kHz multibeam data

Obtaining absolute seafloor backscatter measurements from hydrographic multibeam echosounders is yet to be achieved. We propose a low-cost experiment to calibrate the various acquisition modes of a 30-kHz Kongsberg EM 302 multibeam echosounder in a range of water depths. We use a 38-kHz Simrad EK60 calibrated fisheries split-beam echosounder mounted at 45° angle on the vessel’s hull as a reference for the calibration. The processing to extract seafloor backscatter from the EK60 requires bottom detection, ray tracing and motion compensation to obtain acceptable geo-referenced backscatter measurements from this non-hydrographic system. Our experiment was run in Cook Strait, New Zealand, on well-known seafloor patches in shallow, mid, and deep-water depths. Despite acquisition issues due to weather, our results demonstrate the strong potential of such an approach to obtain system’s absolute calibration which is required for quantitative use of backscatter strength data.     

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.     

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.     

Sea-floor classification using multibeam echo-sounding angular backscatter data: a real-time approach employing hybrid neural network architecture

The presently studied numerical model, e.g., composite roughness, is successful for the purpose of seafloor classification employing processed multibeam angular backscatter data from manganese-nodule-bearing locations of the Central Indian Ocean Basin. Hybrid artificial neural network (ANN) architecture, comprised of the self-organizing feature map and learning vector quantization (LVQ), has been implemented as an alternative technique for sea-floor roughness classification, giving comparative results with the aforesaid numerical model for processed multibeam angular backscatter data. However, the composite-roughness model approach is protracted due to the inherent need for processed data including system-gain corrections. In order to establish that tedious processing of raw backscatter values is unessential for efficient classification, hybrid ANN architecture has been attempted here due to its nonparametric approach. In this technical communication, successful employment of LVQ algorithm for unprocessed (raw) multibeam backscatter data indicates true real-time classification application.     

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.     

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.     

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.     

Seafloor Classification of Multibeam Sonar Data Using Neural Network Approach

In this study, the self-organizing map (SOM), which is an unsupervised clustering algorithm, and a supervised proportional learning vector quantization (PLVQ), are employed to develop a combined method of seafloor classification using multibeam sonar backscatter data. The PLVQ is a generalized learning vector quantization based on the proportional learning law (PLL). The proposed method was evaluated in an area where there are four types of sediments. The results show that the performance of the proposed method is better than the SOM and a statistical classification method.     

Seafloor Classification of Multibeam Sonar Data Using Neural Network Approach

In this study, the self-organizing map (SOM), which is an unsupervised clustering algorithm, and a supervised proportional learning vector quantization (PLVQ), are employed to develop a combined method of seafloor classification using multibeam sonar backscatter data. The PLVQ is a generalized learning vector quantization based on the proportional learning law (PLL). The proposed method was evaluated in an area where there are four types of sediments. The results show that the performance of the proposed method is better than the SOM and a statistical classification method.     

基于回波时间的多波束测深与声纳数据匹配方法

以多波束精确的水深数据为参照源,采用原始回波时间对多波束测深数据与其同源声纳数据进行匹配,从而获得高精度和高分辨率的海底影像数据,并避免了传统声纳图像处理过程中斜距改正所带来的几何形变。匹配结果采用光照图输出,并与三维水深图、原始声纳图像和CARIS处理后的声纳图像进行比较分析。该方法有效地提高了多波束数据的利用率,增强了对海底地形的探测分辨率。     

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.     

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.     

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.     

Recommendations for improved and coherent acquisition and processing of backscatter data from seafloor-mapping sonars

Multibeam echosounders are becoming widespread for the purposes of seafloor bathymetry mapping, but the acquisition and the use of seafloor backscatter measurements, acquired simultaneously with the bathymetric data, are still insufficiently understood, controlled and standardized. This presents an obstacle to well-accepted, standardized analysis and application by end users. The Marine Geological and Biological Habitat Mapping group (Geohab.org) has long recognized the need for better coherence and common agreement on acquisition, processing and interpretation of seafloor backscatter data, and established the Backscatter Working Group (BSWG) in May 2013. This paper presents an overview of this initiative, the mandate, structure and program of the working group, and a synopsis of the BSWG Guidelines and Recommendations to date. The paper includes (1) an overview of the current status in sensors and techniques available in seafloor backscatter data from multibeam sonars; (2) the presentation of the BSWG structure and results; (3) recommendations to operators, end-users, sonar manufacturers, and software developers using sonar backscatter for seafloor-mapping applications, for best practice methods and approaches for data acquisition and processing; and (4) a discussion on the development needs for future systems and data processing. We propose for the first time a nomenclature of backscatter processing levels that affords a means to accurately and efficiently describe the data processing status, and to facilitate comparisons of final products from various origins.     

Processing of high-frequency multibeam echo sounder data for seafloor characterization

Processing simultaneous bathymetry and backscatter data, multibeam echosounders (MBESs) show promising abilities for remote seafloor characterization. High-frequency MBESs provide a good horizontal resolution, making it possible to distinguish fine details at the water-seafloor interface. However, in order to accurately measure the seafloor influence on the backscattered energy, the recorded sonar data must first be processed and cleared of various artifacts generated by the sonar system itself. Such a preprocessing correction procedure along with the assessment of its validity limits is presented and applied to a 95-kHz MBES (Simrad EM 1000) data set. Beam pattern effects, uneven array sensitivities, and inaccurate normalization of the ensonified area are removed to make possible further quantitative analysis of the corrected backscatter images. Unlike low-frequency data where the average backscattered energy proves to be the only relevant feature for discriminating the nature of the seafloor, high-frequency MBES backscatter images exhibit visible texture patterns. This additional information involves different statistical distributions of the backscattered amplitudes obtained from various seafloor types. Non-Rayleigh statistics such as K-distributions are shown to fit correctly the skewed distributions of experimental high-frequency data. Apart from the effect of the seafloor micro-roughness, a statistical model makes clear a correlation between the amplitude statistical distributions and the signal incidence angle made available by MBES bathymetric abilities. Moreover, the model enhances the effect of the first derivative of the seafloor backscattering strength upon statistical distributions near the nadir and at high incidence angles. The whole correction and analysis process is finally applied to a Simrad EM 1000 data set.     

High-resolution mapping of large gas emitting mud volcanoes on the Egyptian continental margin (Nile Deep Sea Fan) by AUV surveys

Two highly active mud volcanoes located in 990–1,265 m water depths were mapped on the northern Egyptian continental slope during the BIONIL expedition of R/V Meteor in October 2006. High-resolution swath bathymetry and backscatter imagery were acquired with an autonomous underwater vehicle (AUV)-mounted multibeam echosounder, operating at a frequency of 200 kHz. Data allowed for the construction of ~1 m pixel bathymetry and backscatter maps. The newly produced maps provide details of the seabed morphology and texture, and insights into the formation of the two mud volcanoes. They also contain key indicators on the distribution of seepage and its tectonic control. The acquisition of high-resolution seafloor bathymetry and acoustic imagery maps with an AUV-mounted multibeam echosounder fills the gap in spatial scale between conventional multibeam data collected from a surface vessel and in situ video observations made from a manned submersible or a remotely operating vehicle.