| Summary: | M.S. University of Hawaii at Manoa 2015. Includes bibliographical references. Ocean-bottom seismic networks are often deployed in remote regions to study the structure of the oceanic crust and mantle, and are able to detect both seismic signals and ocean acoustic signals. This thesis examines records from a large ocean-bottom seismic network to decipher the various acoustic signals that were recorded over nearly one year, including baleen whale calls and volcanic landslides. Specifically, ten months of broadband seismic data, recorded on twentynine ocean-bottom seismographs located in and adjacent to the Lau Basin, were utilized to identify baleen whale species and to determine species’ spatial, temporal, and diel patterns. Probable whale sounds that could not be matched to published spectrograms, as well as nonbiologic sounds that are likely of volcanogenic origin, were also recorded. An automatic detection algorithm utilizing empirical orthogonal functions and multinomial logistical regression was developed to detect select sound types from the large data set.
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