Search

The Seabed Characterization Experiment 2017 yielded a rich set of environmental and acoustical data, and subsequent geoacoustic inversions have estimated seabed properties. Seventeen of these seabed parameterizations are now used to define seabed classes, and a convolutional neural network (CNN) is trained to select a seabed class using explosive sounds. The CNN is trained on synthetic 1-s pressure time series and then applied to measured data samples from a single pressure sensor. While the environmental variability in the training data impacts the seabed classification, physical insights are gained by considering the classification results as a function of the sound speed ratio across the sediment–water interface and the interval velocity of the top sediment layer. These results indicate that the selected seabed classes for the data samples with longer propagation distances consistently have similar sediment–water sound speed ratios that are less than unity, while the data samples with the shortest propagation distances consistently have higher interval velocities. These classification results indicate that the CNN has learned physical features associated with acoustic sound propagation and points to future work that needs to be considered if the seabed classes have acoustically distinct signatures in the data.

A broad overview of the field work conducted during the SBCEX has been provided. SBCEX included a comprehensive subbottom chirp profile of the experimental area, about 200 sediment cores, and a number of direct in situ measurements of the acoustic properties of the seabed. Numerous acoustic propagation measurements throughout the experimental site provided data for inversion processes and statistical inference techniques to remotely sense the acoustic properties of the bottom. The scientific plan of these measurements and the supporting oceanographic and environmental measurements allowed the various principal investigators to begin to achieve the goals outlined in the introduction of this editorial, and thereby increase the community's understanding of the acoustic properties of the fi ne-grained sediment at the site.

A rotated coordinates inversion algorithm is used on subsets of the Inversion Techniques 2001 Geoacoustic Workshop data, to which white Gaussian noise is added. The resulting data sets are equivalent to noisy broad-band signals received on a horizontal line array (HLA) during a single integration time interval. The inversions are performed using a technique called systematic decoupling using rotated coordinates (SDRC), which expands the original idea of rotated coordinates by using multiple sets of rotated coordinates, each corresponding to a different set of bounds, to systematically decouple the unknowns in a series of efficient simulated annealing inversions. The cost function minimized in the inversion is based on the coherent broad-band correlation between data and model cross spectra, which increases the coherence gain of the signal relative to incoherent noise. Using the coherent broad-band cost function with sparse HLA-like data sets, the SDRC inversion method yields good estimates for the sensitive environmental parameters for signal-to-noise ratios as low as -15 dB.

Simulated acoustic data for two-dimensional waveguides were used to estimate the properties of a sea bed over which the depth of the water column varies with range. The simulated data were produced by a parabolic equation method. No approximate knowledge of the local sea-bed representation or parameter bounds was given prior to the inversions. In addition, little approximate information was given on the nature of the range dependence of the sea bed. The inversion methodology used a simulated annealing approach and a variation of the algorithm that generated the original acoustic data. For the purpose of consistency, a second method was employed, which used the same simulated annealing approach but with a range-dependent ray approach in which the sea-bed interaction is described by a complex plane wave reflection coefficient. Various combinations of frequencies and source-receiver positions commonly used in real acoustic measurements at sea were selected for the inversions. The goal of the inversion was to efficiently achieve a coarse description of the environment by several inversions; short-range data at low frequencies were employed for the purpose of efficiency. In the later stages of the inversion process, data from longer ranges and higher frequencies were included to provide additional details of the sea bed. Sea-bed parameters were assumed to vary linearly with range between geoacoustic profiles defined at specific ranges. Inversion results obtained prior to the reporting of the actual solutions are presented.