Research

Distributed Acoustic Sensing (DAS) in Ocean Acoustics

Distributed Acoustic Sensing (DAS) represents a novel observational technique involving the interrogation of an optical fiber through repeated laser pulses. By applying interferometry to the Rayleigh backscattered light, this method measures changes in strain along the fiber. DAS is capable of operating over distances of up to 100 km, offering a spatial resolution in the order of meters. Its broad frequency sensitivity spans from less than 0.001 Hz to over 1 kHz, depending on the configuration. In Ocean Data Lab, we explore the use of DAS in ocean acoustics which will provide new capabilities for Passive Acoustic Monitoring (PAM) and Ocean Surveilence.

a)   b) c)
Example of a (a) fin whale, (b) blue whale, and (c) ship noise recording on DAS. (Wilcock et al., 2023)

 

To learn more about this project, watch this talk:

 

  • Exploring the use of Distributed Acoustic Sensing (DAS) for Ocean Acoustic Monitoring – Shima Abadi – DAS RCN Webinar [Video]

or read these articles:

  1. Wilcock, Abadi, Lipovsky (2023) “Distributed Acoustic Sensing recordings of low-frequency whale calls and ship noises offshore central Oregon,” J. Acoust. Soc. Am., Express Lett. 3 (2), 026002. [PDF]
  2. Douglass, Abadi, Lipovsky (2023) “​​Distributed Acoustic Sensing for detecting near surface hydroacoustic signals” J. Acoust. Soc. Am., Express Lett. 3 (6), 066005.

Characterizing Ocean Ambient Noise

Ocean ambient noise reveals important information about marine life, natural phenomena, and the human footprint in the ocean. Here, we take a data-driven approach to characterize the ocean ambient noise. We leverage an existing, public, long-term ocean acoustic dataset recorded by hydrophone arrays at the Ocean Observatories Initiative (OOI). We have used 6 years of acoustic recordings to (1) understand the long-term trend in ocean ambeint noise, (2) characterize underwater wind noise, and (3) characterize underwater rain noise.

To learn more about this project, watch these talks:

  • Ragland, Abadi, (2021), “Long-term noise interferometry analysis in the northeast Pacific Ocean”, Journal of the Acoustical Society of America, Journal of the Acoustical Society of America, 149 (A90). [Video]

  • Schwock, Abadi, (2021), “Long-term Analysis of Ocean Noise Floor in the Northeast Pacific Ocean”, Journal of the Acoustical Society of America, Journal of the Acoustical Society of America, 149 (A90). [Video]

  • Munson, Abadi, (2021), “Long-term analysis of ocean ambient noise recorded at the northeast Pacific continental margin”, Journal of the Acoustical Society of America, Journal of the Acoustical Society of America, 149 (A137). [Video]

  • Schwock, Abadi, (2020) “Statistical analysis and modeling of rain-generated ocean noise in the northeast Pacific Ocean,” Journal of the Acoustical Society of America, 148 (4) [Video].

  • Schwock, Abadi, (2020) “Statistical analysis and modeling of wind-generated ocean noise in the northeast Pacific Ocean,” Journal of the Acoustical Society of America, 148 (4) [Video].

or read these articles:

  • Schwock, Abadi, “Characterizing Underwater Rain Noise in the Northeast Pacific Continental Margin ,” Journal of the Acoustical Society of America, Journal of the Acoustical Society of America, Vol. 149 (6), 4579. [Link][PDF]

  • Schwock, Abadi, “Statistical analysis and modeling of underwater wind noise at the Northeast Pacific continental margin,” Journal of the Acoustical Society of America, in review.

  • Ragland, Schwock, Abadi, Munson, “An overview of ambient sound using Ocean Observatories Initiative hydrophones,” Journal of the Acoustical Society of America, in review.

Ocean Ambient Noise Interferometry

Ocean ambient noise interferometry is a method of estimating the time domain Green’s function by correlating and averaging sound from adjacent hydrophones. In this project, we use two hydrophones located in the caldera of the Axial Seamount volcano approximately 470 km off the Oregon coast to estimate 6 years of the time domain Green’s function that contains the direct arrival as well as the multi-path arrivals. We invert these multipath arrival times to estimate ocean variables such as water temperature.

To learn more about this project, listen to this talk:

  • Ragland, Abadi, (2021), “Long-term noise interferometry analysis in the northeast Pacific Ocean”, Journal of the Acoustical Society of America, Journal of the Acoustical Society of America, 149 (A90). [Video]

or read this article:

  • Ragland, Abadi, Sabra, “Long-term noise interferometry analysis in the northeast Pacific Ocean,” Journal of the Acoustical Society of America, in review.

Airgun Sound Propagation

Seismic reflection surveys use low frequency acoustic energy to image the structure of the seafloor. Broadband impulsive sound signals are generated by sound sources (airguns) and recorded by single or multiple long horizontal hydrophone arrays towed behind the vessel (streamers). The airgun signals can propagate long distances through the ocean and the high sound intensity of those signals has led to concerns over their effects on marine life. Most of the energy from seismic surveys is low frequency, so concerns are particularly focused on Baleen whales that communicate in the same frequency range. These concerns usually fall into these two categories:

  1. Vocalization Masking: Airgun pulses mask marine mammals’ vocalizations and decrease the performance of existing passive acoustic monitoring (PAM) methods which often use data recorded by a short hydrophone array towed close to the airgun array. To mitigate against this issue, we have developed a  sound source localization method that uses data recorded by seismic streamers for localizing Baleen whales in the vicinity of seismic surveys. Read more about this project at

    • Abadi, Tolstoy, Wilcock, (2017) “Ranging baleen whale calls using towed hydrophone arrays during seismic reflection surveys and studying effectiveness of a mitigation process”, PLoSONE 12(2): e0171115. doi:10.1371/journal.pone.0171115 [Link][PDF].

    • Abadi, Wilcock, Tolstoy, Crone, Carbotte, (2015) “Sound source localization using data recorded by hydrophone streamers during seismic surveys”, Journal of the Acoustical Society of America, Vol. 138, Issue 6, [Link][PDF].

  1. Exposure Area: The exposure area (i.e., the area in which the acoustic level is above safe levels   defined for marine mammals) is site-dependent. Defining an accurate exposure area depends on understanding the propagation of airgun pulses in short ranges. To characterize airgun pulses and predict their acoustic levels in short ranges, we have developed a machine learning algorithm that uses data recorded by streamers in real-time. Read more about this project at

  • Abadi, Freneau, (2019) ”Short-range propagation characteristics of airgun pulses during marine seismic reflection surveys”, Journal of the Acoustical Society of America, Vol. 146(4), 2430–2442 [Link][PDF].

  • Abadi, Freneau, (2019), “Spectral Analysis of Airgun Pulses During Marine Seismic Reflection Surveys,” IEEE OCEANS, Seattle, WA, USA, [Link].

Spectrogram of marine mammal vocalizations recorded by a streamer

  

SEL drop vs range in deep ocean

Graph Signal Processing

Classical signal processing analyzes sensor data on Euclidean spaces (time, frequency, or wavelet) without considering the underlying structure of the data. Due to the complexity of ocean networks, data now reside on irregular and dynamic structures which make classical signal processing methods ineffective. Graph Signal Processing is an emerging field that merges spectral graph theory with signal processing to process high-dimensional data represented by graphs. In this project, we study Direction-of-arrival (DOA) estimation from the perspective of graph signal processing.

To learn more about this project, please watch this talk:

  • Alcantara, Abadi, Atlas, (2020) “Improving the Performance of the Bartlett Method for Single-Snapshot Direction-of-Arrival (DOA) Estimation Using Signal Processing on Graphs (SPG),” Journal of the Acoustical Society of America, 148 (4) [Video].

or read these articles:

  • Alcantara, Abadi, Atlas, (2021) “Direction-of-arrival Estimation using Signal Processing on Graphs”, 2021 IEEE Statistical Signal Processing Workshop (SSP), 2021, pp. 1-5, doi: 10.1109/SSP49050.2021.9513776 [Link]

  • Alcantara (2020) “Direction-of-Arrival Estimation Using Signal Processing on Graphs” [Doctoral dissertation, University of Washington] [Link]

 

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