| Summary: | This paper is part of a special issue on machine learning in acoustics. A model-based convolutional neural network (CNN) approach is presented to test the viability of this method as an alternative to conventional matched-field processing (MFP) for underwater source-range estimation. The networks are trained with simulated data generated under a particular model of the environment. When tested with data simulated in environments that deviate slightly from the training environment, this approach shows improved prediction accuracy and lower mean-absolute-error (MAE) compared to MFP. The performance of this model-based approach also transfers to real data, as demonstrated separately with field data collected in the Beaufort Sea and off the coast of Southern California. For the former, the CNN predictions are consistent with expected source range while for the latter, the CNN estimates have lower MAE compared to MFP. Examination of the trained CNNs' intermediate outputs suggests that the approach is more constrained than MFP from outputting very inaccurate predictions when there is a slight environmental mismatch. This improvement appears to be at the expense of decreased certainty in the correct source range prediction when the environment is precisely modeled.
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