Effect of signal to noise ratio on a convolutional neural network for source ranging and environmental classification

In ocean acoustics, simultaneous estimation of both source-receiver range and environment are complicated by low signal-to-noise ratio (SNR). Range and environment class can be found with a convolutional neural network (CNN), which is chosen because of its ability to find patterns in grid-structured data. The CNN acts on synthetic pressure time series data from a single receiver generated for four canonical environments: deep mud, mud over sand,...