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Creators/Authors contains: "Van_Komen, David_F"

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  1. ; ; ; ; (, Physical Review D)
  2. ; ; ; (, JASA Express Letters)
    While seabed characterization methods have often focused on estimating individual sediment parameters, deep learning suggests a class-based approach focusing on the overall acoustic effect. A deep learning classifier—trained on 1D synthetic waveforms from underwater explosive sources—can distinguish 13 seabed classes. These classes are distinct according to a proposed metric of acoustic similarity. When tested on seabeds not used in training, the classifier obtains 96% accuracy for matching such a seabed to one of the top-3 most acoustically similar classes from the 13 training seabeds. This approach quantifies the performance of a seabed classifier in the face of real seabed variability. 
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