Estimation of shipping noise from sparse measurements via generative adversarial networks

There is growing interest in prediction of anthropogenic noise levels in the ocean. Evidence suggests that sources of ambient noise such as shipping traffic may be destructive to marine organisms that rely on acoustics for communication. Characterizing and predicting ambient noise are also critical to effective naval operations. Understanding ocean noise is constrained by the limited ability to directly measure the spatial distribution of noise levels. In this work, we present a deep-learning method to estimate the spatial distribution of ambient noise due to shipping from a small number of measurements. Noise levels are typically estimated using forward models based on statistical information about shipping routes and source levels and predictions or measurements of environmental variables including bathymetry and sound-speed profile. Inverse methods are sometimes used to estimate input parameters from in situ data. In contrast, we demonstrate the robust estimation of shipping noise using a pretrained generative adversarial network (GAN) as a prior. By using a context and prior loss, our algorithm is able to accurately predict the entire spatial distribution of noise from sparsely sampled measurements. This can yield greater accuracy than a forward model based on environmental parameters taken from archival databases or inferred in situ.