Acoustic Green's function extraction in the ocean [electronic resource]
出版項
2016
說明
1 online resource (120 p.)
附註
Source: Dissertation Abstracts International, Volume: 78-03(E), Section: B
Adviser: Michael G. Brown
Thesis (Ph.D.)--University of Miami, 2016
Includes bibliographical references
The acoustic Green's function (GF) is the key to understanding the acoustic properties of ocean environments. With knowledge of the acoustic GF, the physics of sound propagation, such as dispersion, can be analyzed; underwater communication over thousands of miles can be understood; physical properties of the ocean, including ocean temperature, ocean current speed, as well as seafloor bathymetry, can be investigated. Experimental methods of acoustic GF extraction can be categorized as active methods and passive methods. Active methods are based on employment of man-made sound sources. These active methods require less computational complexity and time, but may cause harm to marine mammals. Passive methods cost much less and do not harm marine mammals, but require more theoretical and computational work. Both methods have advantages and disadvantages that should be carefully tailored to fit the need of each specific environment and application. In this dissertation, we study one passive method, the noise interferometry method, and one active method, the inverse filter processing method, to achieve acoustic GF extraction in the ocean
The passive method of noise interferometry makes use of ambient noise to extract an approximation to the acoustic GF. In an environment with a diffusive distribution of sound sources, sound waves that pass through two hydrophones at two locations carry the information of the acoustic GF between these two locations; by listening to the long-term ambient noise signals and cross-correlating the noise data recorded at two locations, the acoustic GF emerges from the noise cross-correlation function (NCF); a coherent stack of many realizations of NCFs yields a good approximation to the acoustic GF between these two locations, with all the deterministic structures clearly exhibited in the waveform. To test the performance of noise interferometry in different types of ocean environments, two field experiments were performed and ambient noise data were collected in a 100-meter deep coastal ocean environment and a 600-meter deep ocean environment
In the coastal ocean environment, the collected noise data were processed by coherently stacking five days of cross-correlation functions between pairs of hydrophones separated by 5 km, 10 km and 15 km, respectively. NCF waveforms were modeled using the KRAKEN normal mode model, with the difference between the NCFs and the acoustic GFs quantified by a weighting function. Through waveform inversion of NCFs, an optimal geoacoustic model was obtained by minimizing the two-norm misfit between the simulation and the measurement. Using a simulated time-reversal mirror, the extracted GF was back propagated from the receiver location to the virtual source, and a strong focus was found in the vicinity of the source, which provides additional support for the optimality of the aforementioned geoacoustic model. With the extracted GF, dispersion in experimental shallow water environment was visualized in the time-frequency representation. Normal modes of GFs were separated using the time-warping transformation. By separating the modes in the frequency domain of the time-warped signal, we isolated modal arrivals and reconstructed the NCF by summing up the isolated modes, thereby significantly improving the signal-to-noise ratio of NCFs. Finally, these reconstructed NCFs were employed to estimate the depth-averaged current speed in the Florida Straits, based on an effective sound speed approximation
In the mid-deep ocean environment, the noise data were processed using the same noise interferometry method, but the obtained NCFs were not as good as those in the coastal ocean environment. Several highly possible reasons of the difference in the noise interferometry performance were investigated and discussed. The first one is the noise source composition, which is different in the spectrograms of noise records in two environments. The second is strong ocean current variability that can result in coherence loss and undermine the utility of coherent stacking. The third one is the downward refracting sound speed profile, which impedes strong coupling between near surface noise sources and the near-bottom instruments
The active method of inverse filter processing was tested in a long-range deep-ocean environment. The high-power sound source, which was located near the sound channel axis, transmitted a pre-designed signal that was composed of a precursor signal and a communication signal. After traveling 1428.5 km distance in the north Pacific Ocean, the transmitted signal was detected by the receiver and was processed using the inverse filter. The probe signal, which was composed of M sequences and was known at the receiver, was utilized for the GF extraction in the inverse filter; the communication signal was then interpreted with the extracted GF. With a glitch in the length of communication signal, the inverse filter processing method was shown to be effective for long-range low-frequency deep ocean acoustic communication. (Abstract shortened by ProQuest.)
Electronic reproduction. Ann Arbor, Mich. : ProQuest, 2017
