Title:
Passive acoustic monitoring of the deep ocean using ambient noise
Passive acoustic monitoring of the deep ocean using ambient noise
Author(s)
Woolfe, Katherine F.
Advisor(s)
Sabra, Karim G.
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Abstract
In the ocean, changes in the speed of sound can be related to changes in water temperature. By leveraging this relationship, acoustic methods – namely acoustic tomography- have been used to monitor temperature changes in the deep ocean for the purposes of providing inputs to climate change models. Traditionally, these acoustic methods involve loud, active sound sources which can be logistically challenging to operate and have been criticized for potentially disturbing marine animals. Therefore, this work demonstrates a passive acoustic method - previously only used in shallow water for short monitoring durations- that uses only recordings of low-frequency (1-40 Hz) ambient noise to continuously monitor variations in deep ocean temperature with an unprecedented degree of precision and temporal resolution. Numerical simulations were conducted to show the portions of the ocean that are monitored with this passive method. This work also provides recommendations (regarding sensor placement around the world) for future development of a global passive acoustic sensor network that makes use of distant noise sources (sea-ice or seismic sources) to extract meaningful information (whether temperature, currents, etc.) about the ocean. Finally, an optimization method is proposed to overcome one of the fundamental limitations of previous applications of this passive monitoring method: tracking oceanic fluctuations that occur over short time scales. Hence, the results of this study may assist in the development of more reliable climate models that include an enhanced understanding of the ocean’s role as a global heat sink. Finally, an optimization method was proposed to enhance the emergence rate of coherent arrivals from ambient noise correlations, thus allowing this passive monitoring method to track acoustic medium fluctuations on a shorter time scale. This optimization could also be used in other applications of noise-based passive monitoring in a rapidly fluctuating medium (seismic, structural health monitoring, biomedical, etc.).
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Date Issued
2015-07-01
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Text
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Dissertation