Recent advances using mobile platforms equipped with passive acoustic sensors show great promise to map the distribution of marine organisms over large spatial scales. Ocean passive acoustics (listening tools) can be used to identify and provide the locations of sound producing animals including snapping shrimp, many fishes including those that are commercially important, marine mammals, and boats However, these mobile platforms have several limitations preventing their use to collect synoptic data on a very large scale including mechanical noise, requiring support vessels, and generally high costs. This work will seek to develop and test innovative acoustic buoys and drifters that are substantially less expensive than current mobile platforms to enable global in situ biological observations. Our goal is to make acoustic buoys and drifters widely available so that it is possible for small research groups to perform ocean-scale studies.
A low-cost acoustic buoy and drifter will enable deployments of 100x as many sensors as would be feasible using a single autonomous vehicle. The main challenge with drifters is that the data of interest must be sent over low-bandwidth satellite links. This bandwidth limitation necessitates processing data onboard and sending back the key bits of information to answer specific biological questions. This research will integrate an advanced convolutional neural network AI processing engine in the drifters and develop tools to make it easy for researchers to train their own detection and classification networks and load them onto a drifter. The drifters will also store high resolution raw data that can be accessed if the drifter is recovered. The open source design will enable other researchers to define their own signal processing schemes to detect signals of interest.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
