Multi-frequency backscatter data collected from multibeam echosounders (MBESs) is increasingly becoming available. The ability to collect data at multiple frequencies at the same time is expected to allow for better discrimination between seabed sediments. We propose an extension of the Bayesian method for seabed classification to multi-frequency backscatter. By combining the information retrieved at single frequencies we produce a multispectral acoustic classification map, which allows us to distinguish more seabed environments. In this study we use three triple-frequency (100, 200, and 400 kHz) backscatter datasets acquired with an R2Sonic 2026 in the Bedford Basin, Canada in 2016 and 2017 and in the Patricia Bay, Canada in 2016. The results are threefold: (1) combining 100 and 400 kHz, in general, reveals the most additional information about the seabed; (2) the use of multiple frequencies allows for a better acoustic discrimination of seabed sediments than single-frequency data; and (3) the optimal frequency selection for acoustic sediment classification depends on the local seabed. However, a quantification of the benefit using multiple frequencies cannot clearly be determined based on the existing ground-truth data. Still, a qualitative comparison and a geological interpretation indicate an improved discrimination between different seabed environments using multi-frequency backscatter.

Multi-frequency multibeam backscatter (BS) has indicated, in particular for fine sediments, the potential for increasing the discrimination between different seabed environments. Fine sediments are expected to have a varying signal penetration within the frequency range of modern multibeam echosounders (MBESs). Therefore, it is unknown to what extent the multispectral MBES data represent the surface of the seabed or different parts of the subsurface. Here, the effect of signal penetration on the measured multi-frequency BS and bathymetry is investigated. To this end, two multi-frequency datasets (90 to 450 kHz) were acquired with an R2Sonic 2026 MBES, supported by ground-truthing, in the Vlietland Lake and Port of Rotterdam (The Netherlands). In addition, a model to simulate the MBES bathymetric measurements in a layered medium is developed. The measured bathymetry difference between the lowest (90 kHz) and highest frequency (450 kHz) in areas with muddy sediments reaches values up to 60 cm dependent on the location and incident angle. In spatial correspondence with the variation in the depth difference, the BS level at the lowest frequency varies by up to 15 dB for the muddy sediments while the BS at the highest frequency shows only small variations. A comparison of the acoustic results with the ground-truthing, geological setting and model indicates that the measured bathymetry and BS at the different frequencies correspond to different parts of the seabed. However, the low-frequency BS cannot be directly related to a subsurface layer because of a significant sound attenuation in the upper layer. The simulation of the MBES bottom detection indicates that the bathymetry measured at the highest and lowest frequency can be used to determine the thickness of thin layers (∼20 cm). However, with an increasing layer thickness, the bottom detection becomes more sensitive to the incident angle and small variations in the sediment properties. Consequently, an accurate determination of the layer thickness is hampered. Based on this study, it is highly recommended to analyze multi-frequency BS in combination with the inter-frequency bathymetry difference to ensure a correct interpretation and classification of multi-frequency BS data.

Obtaining an overview of the spatial and temporal distribution of seabed sediments is of high interest for multiple research disciplines. Multibeam echosounders allow for the mapping of seabed sediments with high area coverage. In this paper, the repeatability of acoustic classification derived from multibeam echosounder backscatter is addressed. To this end, multibeam echosounder backscatter data acquired on the Cleaver Bank (North Sea) during five different surveys is employed using two different classification methods, i.e., a method based on the principal component analyses and the Bayesian technique. Different vessels were used for the different surveys. The comparison of the classification results between the different surveys indicates good repeatability. This repeatability demonstrates the potential of using backscatter for long-term environmental monitoring. However, the use of different classification methods results in somewhat different classification maps. Monitoring, therefore, requires the consistent use of a single method. Furthermore, it is found that the statistical characteristics of backscatter is such that clustering algorithms are less suited to discern the number of sediment types present in the study area. The Bayesian technique accounting for backscatter statistics is therefore recommended. A strong positive correlation between backscatter and median grain size for finer sediments (0.5 mm. Consequently, for the situation considered a unique assignment of sediment type to acoustic class is not possible for these coarser sediments.

Extensive seabed sediment mapping is highly relevant to describe marine ecosystems and to quantify the distribution and extent of benthic habitats. Compared to traditional mapping methods, primarily based on bed sampling, multibeam echo sounding (MBES) is a time-efficient tool to acquire high-resolution bathymetric and backscatter data over large areas. We use a Bayesian method for unsupervised acoustic sediment classification (ASC) of MBES backscatter data of the Cleaver Bank, Netherlands Continental Shelf. On these sparsely distributed backscatter datasets, we tested and evaluated different Kriging algorithms, showing that Ordinary Kriging results in a reliable map. We introduce a new approach to classify interpolated MBES backscatter based on the Bayesian method for producing full-coverage sediment maps. Comparison to a traditional sediment map and in situ measurements shows that this approach resolves lateral heterogeneities (kilometers). When evaluating the high-resolution sediment map obtained from the Bayesian method, based on the actual backscatter, mapping laterally heterogeneous sediments significantly improved (meters). In order to create the optimal sediment map, we aimed to integrate ASC into existing maps, which, however, requires quantified spatial uncertainties of both considered maps. Finally, the low discrimination power of MBES backscatter for coarse sediments is highlighted as a shortcoming of current ASC mapping.

Biogenic reefs support marine biodiversity and play a key role in a healthy marine environment. Protecting and enhancing reef-building species, such as Sabellaria spinulosa, require mapping and monitoring strategies. A multi-scale and multi-sensor mapping campaign, including a multi-beam echosounder, side-scan sonar (SSS), box corer and ROV with an attached video camera, has been carried out in the northern Brown Bank (Dutch Continental Shelf) in August 2023. A semi-automated classification workflow, based on a support vector machine (machine learning), was developed to map Sabellaria reefs using SSS and video data. Elevated Sabellaria reefs were classified with a precision and sensitivity of 52% and 49%, respectively. The classified SSS images were merged into full-coverage percentage maps of Sabellaria reef coverage. Located between the swales of the tidal ridges, it was estimated that the reefs cover an area of 3.8 to 5.7% within the surveyed areas. The maps indicate (1) on the large-scale a preference of Sabellaria spinulosa for settlement to the east of the deepest part of the swale and (2) on the small-scale a preference for the troughs towards the stoss side of the megaripples. The employed survey strategy and the developed classification workflow can be extended to other environmental areas and further developed into a standard monitoring procedure.

A generic local shipping lane scenario is developed for a computationally effective study of the effects of mitigation measures to reduce the underwater noise from ship traffic. The scenario is designed using recorded AIS (Automatic Identification System) data for ships passing through a shipping lane north of the Dutch Wadden Islands in January 2020. Assuming that all ships sail at constant speed along a straight shipping lane in a uniform environment, the monthly statistics of the depth averaged sound pressure level (SPL) are calculated as a function of the distance to the shipping lane. The calculations are performed using the JOMOPANS-ECHO ship source level model and a parabolic equation sound propagation model. Results are presented for a baseline case (January 2020) and for scenarios in which mitigation measures are applied, such as the application of a maximum speed limit and a maximum ship radiated noise level. The results provide insight in the contribution of various ship types to underwater sound as well as the effectiveness of the studied noise reduction scenarios.