Development and Implementation of an Embedded Algorithm for Essential Ocean Variable Monitoring

In 2018 the Global Ocean Observing System (GOOS) approved Ocean Sound as an Essential Ocean Variable (EOV) within the Biology and Ecosystems Panel. This designation recognized that long-term monitoring of sound in the ocean will yield information on ecosystem health, climate change, and the effects of human activity on the environment. The bulk of the current ocean acoustic data collection is performed by archival recorders, supplemented by a small number of real-time gliders, buoys and cabled observatories. All of these systems yield large data sets that must be analyzed to extract information on the sound levels, and the biologic (e.g. marine mammals, fish, crustaceans), geologic (e.g. wind, waves, ice) and man-made sources (e.g ships, sonars & seismic surveys) that contributed to the sound levels. This approach assumes that the data is available for analysis on shore. In contrast, the EOVs that are widely used are derived from either autonomous floats such as the ARGOS series, or from satellite remote sensing. The objective of this project is to define an algorithm that will identify sound sources contributing to the local soundscape based on a small number of easily computed metrics.

Intern: 
Afolarin Egbewande
Faculty Supervisor: 
Jean-Francois Bousquet;David Barclay
Province: 
Nova Scotia
Partner University: