Transfer learning to account for sound speed changes: Example for source ranging in underwater laboratory tank

Transfer learning (TL) is used to predict source-receiver range in a laboratory tank with varying water temperature. The input data are single-hydrophone spectral levels from linear chirps over the 50–100 kHz band recorded at different ranges. Data measured in room temperature water are used to train one-dimensional convolutional neural networks. When the trained models are applied to data measured in warmer water, a bias is introduced. TL with a small dataset improves the generalization results at the new temperature, demonstrating the potential of TL to improve performance under variable environmental conditions.