A multi-sensor approach for remotely modeling and mapping sediment properties

Detailed information about the oceanic environment is essential for many applications in the field of marine geology, marine biology, coastal engineering, and marine operations. Especially, knowledge of the sediment properties is often required. Acoustic remote sensing techniques have become highly attractive for classifying the sea bottom and mapping the distribution of sediment properties, due to their high coverage capabilities and low costs compared to common sampling methods. In 2007, the Maritime Rapid Environmental Assessment / Blue Planet trial (MREA/BP'07) was carried out in a shallow water environment with fine-grained sediments in the Mediterranean Sea, in order to investigate means of efficiently obtaining a detailed picture of the underwater environment [1]. Measurements were taken by a large set of sensors, including echosounders, seismic systems, and dedicated array configurations, thereby providing all information required to fully describe the environment. Since these systems operate at different frequencies, they provide information on the sea bottom at different depths. This gave reason for a combination of the acoustic data as taken by the different sensors. Especially, the combination of the classification of the upper sediment layer achieved by the hydrographic surveying and the sediment layering obtained from the seismic systems resulted in a well-described three-dimensional picture of the distribution of the underwater sediments [2]. In this paper we present this environmental picture in the context of geoacoustic inversion. Geoacoustic inversion techniques are an attractive means for estimating a variety of physical properties of underwater environments, such as sound speed, density, and attenuation in each sediment layer, as well as layer thicknesses. Geoacoustic inversion, however, comes at the price of high computational efforts. Especially, in cases in which large numbers of parameters need to be inverted for, finding those parameters that provide the best fit between the measurements and model predictions requires hundreds of iterations. Efficient global optimization tools can reduce these efforts. We propose that both the surveying and inversion efforts can be reduced further by carrying out geoacoustic inversion at a small number of selected sites, only. In this paper it is investigated whether a combination of hydrographic and seismic surveying is suitable for selecting areas for sub-seafloor sediment classification by geoacoustic inversion techniques.