Automated detection of sedimentary features using wavelet analysis and neural networks on single beam echosounder data: A case study from the Venice Lagoon, Italy

Acoustic methods are well established and widely used for the exploration of the seafloor and the sub-bottom sediments. However, the mapping and reconstruction of the sedimentary features revealed by acoustics can require a very long time because often large acoustic datasets need to be described and interpreted. To reduce the time of the geophysical visual interpretation, we implemented a new procedure for facies classification based on wavelet analysis and neural networks applied to the acoustic profiles. The optimized algorithm applied to a data set of the very shallow Lagoon of Venice classifies automatically the echo shape parameters to identify and map the main lagoon sedimentary features, such as palaeochannels and palaeosurfaces. The classification algorithm contains a set of wavelet transformation parameters as inputs to a neural network analysis based on the self-organizing map (SOM). The analysis was applied on 580 km of acoustic profiles acquired in a very shallow (less than 1 m) and turbid area of the lagoon with a sub-bottom penetration of about 6-7 m under the bottom. Without any special pre-requirement on the data, the algorithm was successfully tested against the results of the visual interpretation and allowed an automated and more efficient full 2D mapping of the sedimentary features of the area. We could distinguish and map different types of palaeochannels, buried creeks, palaeosurfaces as well as areas characterized by homogeneous mudflat facies. The results were validated by comparison with 5 cores sampled in the survey area corresponding with the main sedimentary features revealed by the acoustics.