WHOI Theses

Permanent URI for this collection

https://hdl.handle.net/1912/1172

WHOI's educational role, at the graduate level, was formalized in 1968 with a change in its charter and the signing of an agreement with the Massachusetts Institute of Technology for a Joint Program leading to doctoral (Ph.D. or Sc.D.) or engineer's degrees. Joint master's degrees are also offered in selected areas of the program. Woods Hole Oceanographic Institution is also authorized to grant doctoral degrees independently.

New theses are added as they are published.

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Browsing WHOI Theses by Author "Bonnel, Julien"
  • Thesis
    Automatic baleen whale detection and 2D localization using a network of unsynchonized passive acoustic sensors
    (Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 2024-02) Goldwater, Mark H. ; Bonnel, Julien ; Zitterbart, Daniel
    Underwater acoustics is a powerful tool for learning about the ocean’s soniferous marine life. However, most modern acoustic sensing systems consist of expensive arrays of timesynchronized recorders which require a crewed research vessel and significant expertise to deploy, operate, and recover. Recently, there has been a growing corpus of research related to algorithms for low-cost and accessible acoustic hardware. Deep learning methods have shown great promise when applied to underwater acoustics inverse problems. While many signal processing or physics-based algorithms exhibit long run times and require manual labor to extract signals of interest, tune parameters, as well as visually verify the results, an appropriately trained neural network can quickly process data with no human supervision. Both low-cost passive acoustic monitoring (PAM) sensing platforms and algorithms that can analyze massive amounts of raw data are critical to accessible and scalable approaches in ocean acoustic monitoring. This thesis presents a method for detection and 2D (latitude-longitude) localization of underwater acoustic sources without requiring synchronized sensors. The signals of interest here are the dispersive low-frequency impulsive gunshot vocalizations of North Pacific and North Atlantic right whales (NPRWs, NARWs). In shallow-water channels, the timefrequency representation of the received signal is strongly dependent on source-receiver range, making these impulses ideal candidates for range-based localization. The first step in the localization pipeline uses a temporal convolutional network (TCN) to simultaneously detect gunshot vocalizations and predict their ranges. Trained on spectrograms of synthetic data simulated in a variety of environments, the TCN is applied to PAM data from moorings in the Bering Sea. Gunshots are detected with high precision, and the range estimates are comparable to those estimated using traditional physics-based processing. Both methods use a minimal set of a priori environmental information including water column depth, sound speed, and density. Depending on the sensor layout, the TCN may need to scan large windows of data, so the number of unique acoustic sources is unknown. To automatically associate and localize range measurements, the proposed method seeks subsets of measurements across unique sensors which are internally consistent. For every considered measurement subset, locations are estimated with single constituent measurements left out and checked to be sufficiently close to the excluded measurement’s set of potential locations. If a measurement subset is entirely consistent in this manner, the measurements are added as neighboring nodes in a graph-based representation, and strongly connected components are used to determine data associations and calculate the final source location estimates. Informed by the methods developed in this thesis, an array of low-cost TOSSIT moorings was deployed in Cape Cod Bay and used to collect experimental PAM data. The localization results are comparable to another similar physics-based inversion approach. Overall, this thesis aims to fill a gap in acoustic data processing methods where data from a low-cost network of unsynchronized acoustic sensors are fused to localize acoustic sources. The presented methods and data processing pipeline demonstrate the great potential of low-cost acoustic sensing systems.
  • Thesis
    Oceanic ambient noise in the Arctic on the Chukchi Shelf: broadband characteristics and environmental drivers
    (Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 2022-09) Fung, Kathryn ; Bonnel, Julien
    This thesis encompasses an analysis of underwater ambient noise collected by the yearlong Canada Basin Acoustic Propagation Experiment (CANAPE) on the Chukchi Shelf of the Arctic. This location contained the Beaufort Duct, a significant effect of climate change on the Arctic’s underwater soundscape. A study of the statistical and probability metrics was conducted on a frequency band of 50-1900 Hz to examine the relation between environmental drivers and noise patterns. The presence of ice typically decreases broadband ambient noise, when compared to ice-free seas. However, the Beaufort Duct under ice increases the ambient noise levels below 1 kHz. The relationship between ambient noise and the environment is further explored by studying the link between distant ice movements and ambient levels Correlation between the two is found to exist from 300-1500 Hz, as distant ( 500 km) ice drift motion appears to drive noise levels at the receiver.