A multi-sensor approach for remotely modeling and mapping sediment properties

info:eu-repo/semantics/publishe

Toward an Efficient and Comprehensive Assessment of Marine Sediments Through Combining Hydrographic Surveying and Geoacoustic Inversion

Acoustic remote sensing techniques are an attractive means for obtaining information on the composition of marine sediments since they have high coverage capabilities and thus allow for efficient surveying. Operating at a wide range of specific frequencies, the acoustic sensors provide insight into the sediment body at different depths. This article suggests an efficient manner to combine high- and low-frequency acoustics for obtaining a comprehensive description of fine-grained sediments in a shallow water environment, as was aimed at by the Maritime Rapid Environmental Assessment/Blue Planet trial (MREA/BP'07). This trial was carried out in the Mediterranean Sea in 2007, employing a variety of acoustic sensors, including echosounders, seismic systems, and dedicated array configurations. In a previous paper (Siemes ,IEEE J. Ocean. Eng. vol. 35, no. 4, pp. 766-778), we established a three-dimensional picture of the sediment distribution in the MREA/BP'07 area by associating high-frequency echosounder data with low-frequency seismics. A classification based solely on these hydrographic measurements, however, could not provide the physical properties of the near-surface sediments. In the current article we complement this environmental picture with results from a geoacoustic inversion effort, which do provide information on the actual physical properties, such as sound speed, density, attenuation, and layer thickness. In contrast to carrying out the inversion over the complete area, only a limited number of locations was selected for inversion, to limit the computational efforts. This selection was based on the hydrographic environmental picture obtained in (Siemes ,IEEE J. Ocean. Eng. vol. 35, no. 4, pp. 766-778). Inverted sediment properties obtained within similar hydrographic regions confirm the similarity in sediment type among these regions, whereas differences in sediment properties between different hydrographic regions are confirmed as well. Variations in the inversion results within an area with a single sediment type could be attributed to the presence of gas. These results show the suitability of the proposed approach, where backscatter and seismic data discern areas that a priori would differ in their near-surface sediment properties and where geoacoustic inversion assigns actual sediment parameters to these different areas.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

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

Acoustic discrimination of relatively homogeneous fine sediments using Bayesian classification on MBES data

Highlights: • Acoustically meaningful clustering correlating with ground truth data • Seascape scale acoustic mapping through classification per beam footprint • Bayesian approach for discriminating sedimentary units with low heterogeneity • Geo-acoustic resolution: a new measure of sedimentary acoustic class separability Modern seafloor mapping is based on high resolution MBES systems that provide detailed bathymetric and acoustic intensity (backscatter) information. We examine and validate the performance of two unsupervised MBES classification techniques for discriminating acoustic classes of sedimentary units with small grain size variability. The first technique, based on a principal components analysis (PCA), is commonly used in literature and has been applied for comparison with the more recent approach of Bayesian statistics. By applying these techniques to a MBES dataset from an estuarine area in The Netherlands, we tested their ability to discriminate fine grained sediments (at least 70% silt) holding small percentages of coarser material such as sand, shell hash or shells. We focus on the Bayesian technique as it outputs acoustically significant classes related to backscatter values. This technique utilizes backscatter values averaged over scatter pixels (projected pulse lengths) inside the footprint of each beam. The originality of our application lies in the fact that, the optimal number of classes is derived by utilizing a number of beams simultaneously. It is assumed that the backscatter values per beam vary relatively to the varying seafloor types. By treating the beams separately, across track variation in the seafloor type can also be accounted for. Thereby the classification is guided by outer, more discriminative beams. Additionally we control the optimal number of classes by employing the quantitative criterion of goodness of fit (χ2). The Bayesian acoustic classes show correlation with grain size parameters such as coarse fraction (>500μm) percentage and mean of the grain size (<500μm) when analyzed with multiple linear regression. In order to examine the relative scale of the acoustic classification results we compare the Bayesian acoustic classes with underwater video interpretation. Our results reveal that the Bayesian approach enhances the sedimentological interpretation of MBES high resolution data, by providing classification on seascape scale (here meters to tens of meters). Hence we suggest that backscatter processing techniques are more commonly applied to produce classes that discriminate sediments with low grain size contrast. To describe this ability we introduce the term geoacoustic resolution. We want to encourage the use of the Bayesian technique also in deep sea applications, based on AUV data, where sediments express low variability but sampling would be time consuming and costly. The advantages of this method would favor mapping of macro-habitats which appear at meter-scale and require datasets of sufficient resolution in order to be quantitatively described

Predicting spatial variability of sediment properties from hydrographic data for geoacoustic inversion

Seafloor classification using acoustic remote sensing techniques is an attractive approach due to its high coverage capabilities and limited costs compared to taking samples of the seafloor. This paper focuses on the characterization of sediments in a coastal environment by combining different hydrographic systems, which are a multibeam echosounder (MBES), a single-beam echosounder (SBES), and seismic systems. The area is located close to the west coast of Italy, southeast of Elba Island, which is known to be composed of very fine-grained material. Both MBES and SBES are, in general, high-frequency systems (≤100 kHz), providing bathymetry and backscatter information of the upper part of the sea bottom. MBES systems provide this information with a high resolution, due to the beam opening angle of typically 1°- 3° ,and high coverage. An SBES provides measurements directly underneath the ship only, but is widespread. For the classification by means of MBES data, we use the Bayesian approach, employing backscatter measurements per beam. For the SBES, echo shape parameters are determined and are combined in a principal component analysis (PCA). Both approaches give results that are in very good agreement with respect to the distribution of different surficial sediment types. Complementary, low-frequency seismic systems (< 20 kHz) give insight into the sediment layering. Combining the different acoustic approaches is shown to be an essential ingredient for establishing the environmental picture. This picture is of use for a large range of applications, such as habitat mapping, cable laying, or mine hunting. For the current research, it is aimed to act as a basis for selecting areas for subseafloor sediment classification by geoacoustic inversion techniques. Contrary to the hydrographic systems, geoacoustic inversion techniques provide the actual physical properties, i.e. densities, compression and shear wave speeds, and respective attenuations of the sediment body, and allow sediment characterization over large areas without the need to cover the complete area. A validation is given that the environmental picture, obtained by the hydrographic systems, indeed identifies regions with different acoustic properties. © 2005 IEEE.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

High-frequency multibeam echosounder classification for rapid environmental assessment

For shallow water naval operations, obtaining rapidly an accurate picture of the environmental circumstances often is of high importance. The required information typically concerns water column properties, sea surface roughness, and sediment geo-acoustic properties. Hereto a multi-sensor approach is required. In this context, the BP'07 experiment has been carried out south of Elba (Mediterranean Sea), where several techniques of environmental characterization have been combined. A part of BP'07 was dedicated to measurements carried out with a multibeam echosounder. This system provides depth information, but also allows for seafloor classification. The classification approach taken is model-based employing the backscatter data. It discriminates between sediments in the most optimal way by applying the Bayes decision rule for multiple hypotheses, implicitly accounting for ping-to-ping variability in backscatter strength. For validating the resulting geo-acoustic estimates sediment samples were collected. Here, besides the analysis of the depth measurements, the results of the seafloor classification using the multibeam data and a preliminary comparison with the sediment sample analysis are presented.SCOPUS: cp.pinfo:eu-repo/semantics/publishe

Model-based sediment classification using single-beam echosounder signals

Acoustic remote sensing techniques for mapping sediment properties are of interest due to their low costs and high coverage. Model-based approaches directly couple the acoustic signals to sediment properties. Despite the limited coverage of the single-beam echosounder (SBES), it is widely used. Having available model-based SBES classification tools, therefore, is important. Here, two modelbased approaches of different complexity are compared to investigate their practical applicability. The first approach is based on matching the echo envelope. It maximally exploits the information available in the signal but requires complex modeling and optimization. To minimize computational costs, the efficient differential evolution method is used. The second approach reduces the information of the signal to energy only and directly relates this to the reflection coefficient to obtain quantitative information about the sediment parameters. The first approach provides information over a variety of sediment types. In addition to sediment mean grain size, it also provides estimates for the spectral strength and volume scattering parameter. The need to account for all three parameters is demonstrated, justifying computational expenses. In the second approach, the lack of information on these parameters and the limited SBES beamwidth are demonstrated to hamper the conversion of echo energy to reflection coefficient.Remote SensingAerospace Engineerin