Kerstin Siemes

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.

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 model-based 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.

Observations regarding coarse sediment classification based on multi-beam echo-sounder's backscatter strength and depth residuals in Dutch rivers

This contribution investigates the behavior of two important riverbed sediment classifiers, derived from multi-beam echo-sounder (MBES)-operating at 300 kHz-data, in very coarse sediment environments. These are the backscatter strength and the depth residuals. Four MBES data sets collected at different parts of rivers in the Netherlands are employed. From previous research the backscatter strength was found to increase for increasing mean grain sizes. Depth residuals, however, are often found to have lower values for coarser sediments. Investigation of the four data sets indicates that these statements are valid only for moderately coarse sediment such as sand. For very coarse sediments (e.g., coarse gravel) the backscatter strength is found to decrease and the depth residuals increase for increasing mean grain sizes. This is observed when the sediment mean grain size becomes significantly larger than the acoustic wavelength of the MBES (5 mm). Knowledge regarding this behavior is of high importance when using backscatter strength and depth residuals for sediment classification purposes as the reverse in behavior can induce ambiguity in the classification.

A model-based method for reducing the sound speed induced errors in multi-beam echo-sounder bathymetric measurements

We present a method for accurately estimating the bathymetry from multi-beam echo-sounder (MBES) travel-time measurements in environments with large variations in the water column sound speeds (both temporally and spatially). In this type of environments the water column sound speeds at the time of the MBES transmission are often not known, preventing a reliable conversion from the measured travel-times to bathymetry. In addition, accurate sound speed information is required for the MBES beamsteering process. Situations where information regarding the prevailing sound speeds is insufficient occur, for example, in estuaries where fresh river water mixes with seawater. The method fully exploits the redundancy in the MBES measurements obtained from the overlap of adjacent swathes. It searches for those water column sound speeds that result in a maximum agreement in the bathymetry along the overlapping swathes. The Gauss-Newton method is employed for the optimization. This method is very efficient, requiring a few iterations only and therefore very well suited for processing the large datasets as typically obtained by the MBES. From simulations it is shown that the method performs well and allows for an almost exact determination of the bathymetry and sound speeds. In principle, the method eliminates the need for sound speed measurements, as long as sufficient overlap exists.