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) using a frequency of 300 kHz is observed within the study area, but an ambiguity is found for sediments with median grain sizes >0.5 mm. Consequently, for the situation considered a unique assignment of sediment type to acoustic class is not possible for these coarser sediments.@en

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.

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Quantitative mapping of seafloor sediment properties (eg. grain size) requires the input of comprehensive Multi-Beam Echo Sounder (MBES) datasets along with adequate ground truth for establishing a functional relation between them. MBES surveys in extensive shallow shelf areas can be a rather challenging and time-consuming task, resulting in time and cost intensive data collection. It is therefore often the problem of dealing with sparse data and/or data without full coverage. This study deals with MBES data acquired within an area of approximately 30 x 55 km (Cleaverbank, North Sea, the Netherlands) with a line spacing of ~2 km in 50 m average water depth. Additionally ground truth samples were taken with a Hamon grab in a regular grid of ~1 x 1 km with samples at and in-between some of the MBES survey lines. These ground truth data cover a subset of the area under investigation from variable depth and sediment types ranging from silty clay to boulders. Here we combine geostatistical and multivariate modelling for predictive mapping of the median grain size across the whole area. First bathymetric data was cleaned and raw backscatter values were classified into 7 classes representing different sediment types using a Bayesian method. A multiple linear regression was performed with the ground truth (sediment grain size) data resulting in an empirical function that shows high correlation (90%) of the median grain size with depth and backscatter classes. However missing information in un-surveyed areas needs to be estimated before predictive mapping can take place. For this purpose we interpolated sparse bathymetric and backscatter class data in order to obtain layers of continuous information for the whole area. To achieve this, MBES lines were treated as Single Beam Echo-Sounder data by producing a reduced, equally-spaced grid of bathymetry and backscatter class points, for each MBES line. Interpolation of this bathymetric data set using Ordinary Kriging (OK) yielded satisfactory results with low root-mean-square errors. For the backscatter class we utilized both OK and a Bayesian Maximum Entropy (BME) algorithm which is regarded as more suitable for categorical data. As backscatter classes are ordered by increasing acoustic intensity makes the interpolation results useful as they also include values between classes. Interpolated bathymetry and backscatter classes were validated with additional data from crossing lines. Subsequently, the interpolated layers produced by geostatistical modelling became input to the empirical regression function connecting median grain size and MBES data. The predictive layer of median grain size was assessed by using independent ground truth data from not-mapped locations. By applying linear regression to the interpolated data it was found that both, OK and BME provided results that predict median grain sizes with a high correlation (up to r2=0.85) to the observed values. The overall approach suggests that the combination of geostatistical with multivariate modelling can offer good results for large scale seafloor classification when data are limited. Additionally the prediction accuracy for each layer (bathymetry, backscatter class) can be estimated and spurious results can be identified. @en