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.

Riverbed and seafloor sediment classification using acoustic remote sensing techniques is of high interest due to their high coverage capabilities at limited cost. This contribution presents the results of riverbed sediment classification using multi-beam echo-sounder data based on an empirical method. Two data sets are considered, both taken at the Waal River, namely Sint Andries and Nijmegen. This work is a follow-up to the work carried out by Amiri-Simkooei et al. [J. Acoust. Soc. Am. 126(4), 1724-1738 (2009)]. The empirical method bases the classification on features of the backscatter strength and depth residuals. A principal component analysis is used to identify the most appropriate and informative features. Clustering is then applied to the principal components resulting from this set of features to assign a sediment class to each measurement. The results show that the backscatter strength features discriminate between different classes based on the sediment properties, whereas the depth residual features discriminate classes based on riverbed forms such as the "fixed layer" (stone having riprap structure) and riverbed ripples. Combination of these two sets of features is highly recommended because they provide complementary information on both the composition and the structure of the riverbed.

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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.

This paper presents single receiver geoacoustic inversion of a combustive sound source signal, recorded during the 2017 Seabed Characterization Experiment on the New England Mud Patch, in an area where water depth is around 70 m. There are two important features in this study. First, it is shown that high-order modes can be resolved and estimated using warping (up to mode number 18 over the frequency band 20-440 Hz). However, it is not possible to determine mode numbers from the data, so that classical inversion methods that require mode identification cannot be applied. To solve this issue, an inversion algorithm that jointly estimates geoacoustic properties and identifies mode number is proposed. It is successfully applied on a range-dependent track, and provides a reliable range-average estimation of geoacoustic properties of the mud layer, an important feature of the seabed on the experimental area.