Long-term noise interferometry analysis is conducted over six years of data using two hydrophones on the Ocean Observatories Initiative Cabled Array. The two hydrophones are separated by 3.2 km and are bottom-mounted at 1500 m. We demonstrate the ability of ambient noise interferometry to reliably detect multi-path arrivals in the deep ocean from bottom-mounted hydrophones. An analysis of the multi-path arrival peak emergence is presented, as well as long-term trends of the signal-to-noise ratio of the arrival peaks. Last, we show that long-term ambient noise interferometry provides the opportunity for monitoring directional, coherent ambient sound such as the fin whale chorus.
A seismic reflection survey conducted directly over two bottom-mounted hydrophones in the north-east Pacific Ocean is used to explore how surface source locations affect ambient noise interferometry for the two hydrophones. The airgun shots are used as an approximation of an impulsive sound source at a discrete location, which allows us to investigate spatial contributions to the cross correlation between the two hydrophones. Simulated and experimental results are presented. The contributions to the cross correlations are explained by different reflections off the surface or bottom of the ocean, and a discussion about what can and cannot be inferred about the emergence to the Green's function is presented.
Ambient noise interferometry is a passive acoustic technique for environment characterization. The technique uses coherent ambient sound to approximate the Green’s function between two sensors. It has previously been used in ocean acoustics to passively estimate water temperature, sound-speed structure, and mode shapes as well as for sensor localization. Since this technique utilizes the ambient noise field, whose characteristics are often unknown, understanding the effects of non-isotropic ambient sound is important for ambient noise interferometry. In this talk, an overview of the theoretical literature for noise interferometry will be presented with a specific emphasis on the effects of non-isotropic ocean noise source distributions. Additionally, simulations that explore the emergence of the Green’s function will be presented. Specifically, sound source distributions and environmental parameters such as sound speed profile will be explored. Lastly, the implications of these works on the possibilities and limitations of ambient noise interferometry will be discussed. [Work supported by the ONR.]
Fiber optic distributed acoustic sensing (DAS) is a recent innovation utilized primarily in the seismic community for measuring seismic acoustics signals at low frequencies (single digit Hz and below). The technique utilizes strain rates in a fiber optic cable, observed via the backscatter of light pulses, to measure the acoustic field. Recently, the capabilities of this technology to measure higher frequency acoustic fields (10s to 100s of Hz) have been explored. Low frequency marine mammals calls at ∼20 Hz and ship noises have been successfully recorded, and a recent experiment demonstrated the capability to record up to ∼500 Hz. This talk provides an overview of DAS technology and introduces two recent experiments for studying water column acoustics with DAS. A 4-day experiment conducted in November 2020 as part of the Ocean Observatories Initiative (OOI) provides data along two fiber optic cables extending west from the coast of Oregon by 65 km and 95 km, reaching depths of 590 m and 1575 m, respectively. DASCAL22, a recent experiment from October 2022, simultaneously recorded data using DAS at 2 kHz sampling rate on a cable extending 3.54km at ∼100 m depth and multiple moored hydrophones placed close to the DAS cable, allowing direct comparison between a new and existing technology.
The recent advent of long-term, large-scale, and publicly available underwater acoustic datasets can unlock new discoveries and fuel a deeper understanding of the ocean environment. One such dataset is provided by the Ocean Observatories Initiative (OOI), who maintain multiple research arrays distributed across the Pacific and Atlantic Ocean that feature a variety of instruments and platforms from hydrophones to water column profilers and surface buoys. However, analyzing the OOI data can be challenging, as tools for accessing the data are either completely lacking, or require a significant amount of additional programming effort to prepare data for analysis. With the development of the Python package OOIPy we aim to address these problems for the OOI data products. OOIPy combines low level functions for accessing OOI data in Python with higher level functions for data processing, analysis, and visualization. Currently, data products from low-frequency and broadband hydrophones, which are only available through the OOI raw data server, as well as conductivity, temperature, depths sensors in the northeast Pacific Ocean are supported. Our vision for the future is to expand OOIPy and allow scientists to focus on the analysis, rather than the accessing and preprocessing, of OOI data products. [Work supported by ONR.]
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