Search

Nowadays Multi-Beam Echo-Sounder (MBES) systems are used for obtaining information of the sea/river bed bathymetry and sediment composition. For the latter, use is usually made of the backscatter strength and depth derivatives, such as depth residuals. However, the depth derivatives are affected by the uncertainties inherent to the MBES varying with the sensors used, survey configuration and operational environment. Although models are available for the vertical uncertainty prediction, the question is how well these models can capture the estimated uncertainties of real observations. The present contribution addresses this issue by comparing the measured with modelled depth uncertainty accounting for the most recent insights of the error contributors. Data was acquired in water depths of around 2m, 10m and 30m with pulse lengths of 27 μs, 54 μs and 134 μs in the Oosterschelde estuary, the Netherlands, enabling the assessment of depth and pulse length dependence of the uncertainties. In general, the predicted and measured uncertainties are in the same order of magnitude. With increasing depth the discrepancy between the modelled and measured uncertainties increases. The effect of changing pulse length is found to be captured by the model, except for the angles close to nadir. The most dominant contributors to the vertical uncertainty are those induced by the angle of impact and range measurements. These contributors thus require further investigation to obtain a more realistic estimate of the vertical uncertainties.

In a world of high precision sensors, one of the few remaining challenges in multibeam echosounding is that of refraction-based uncertainty. A poor understanding of oceanographic variability or a poor choice of equipment can lead directly to poor quality bathymetric data. Post-processing software tools have existed for some time to allow data processors to correct for these artifacts. These tools typically involve the manual review of soundings and manual adjustment of a small set of parameters to achieve the desired correction. Though there are a number of commercial solutions currently available, they all have the same inherent weaknesses: (1) they are manual, thus time-consuming, (2) they are subjective, thus not repeatable, (3) they require expert training and thus are typically only usable by experienced personnel. QPS and the Technical University of Delft, The Netherlands (TU Delft) have worked together to implement an algorithm to address these issues in QPS’ post-processing software, Qimera. The algorithm, the TU Delft Sound Speed Inversion, works by taking advantage of the overlap between survey lines, harnessing the power of redundancy of the multiple observations. For a given set of pings, the algorithm simultaneously estimates sound speed corrections for the chosen pings and their neighbors by computing a best-fit solution that minimizes the mismatch in the areas of overlap between lines. This process is repeated across the entire spatial area, allowing for an adaptive solution that responds to changes in oceanographic conditions. This process is completely automated and requires no operator interaction or data review. The algorithm is also physics-based in that it honors the physics of acoustic ray bending. For accountability, the algorithm preserves the output of the inversion process for review, vetting, adjustment, and reporting. In this paper, we briefly explain how the algorithm works in simple terms. We also explore two data sets that cover differing oceanographic conditions, seabed morphologies, and survey line planning geometries in order to establish some early guiding principles on how far the algorithm can be pushed for performance.

Knowing the morphology and sediment composition of the seabed is of high importance for various applications. In this contribution, the repeatability of acoustic seafloor classification (ASC) results obtained from MBES backscatter value is investigated. The unsupervised classification algorithm based on Principal Component Analysis has been applied to the MBES backscatter acquired in the Cleaver Bank, Netherlands Continental Shelf, during five different surveys with two vessels. In general, there is good repeatability between surveys demonstrating the potential of using backscatter for marine environmental monitoring. To increase the discrimination performance the so-called depth residuals can be used. These are derived from the bathymetric measurements and considered to be representative for the sediment roughness. The challenge is that the small-scale depth variations are not solely dependent on the sediment roughness but also on the intrinsic uncertainties inherent to the MBES system. An A-Priori Multibeam Uncertainty Simulation Tool (AMUST) has been developed to predict the depth errors induced by various contributors. Correcting the measured depths for these uncertainties, as predicted by AMUST, theoretically provides information about the actual sediment roughness and this should improve the ASC algorithms. This was first tested on a MBES data set from Shallow Survey Conference Plymouth, 2015. It was shown that for the water depth of 20 m the standard deviation of the depth measurements was in agreement with AMUST predictions indicating a smooth seafloor, however, discrepancies between the predictions and real measurements occurred for the water depth of 8 m which is an indication of roughness or morphological features. This indicates the necessity of knowledge about the uncertainties when the objective is to derive the sediment roughness from MBES measurements.