Fish & Shrimp & Seals, Oh My! Soundscapes of Hawaiian monk seal habitats are dominated by biophony

Kirby Parnell –

Marine Mammal Research Program, University of Hawaii Manoa, Kaneohe, HI, 96744, United States

Karlina Merkens
Aude Pacini – twitter: @audepacini
Lars Bejder – twitter: @lbejder, @MMRP_UH

Popular version of 5aUW-Underwater soundscapes at critical habitats of the endangered Hawaiian monk seal, presented at the 183rd ASA Meeting.

The ocean is a noisy place. From chatty marine mammals to territorial fish and hungry shrimp, marine animals use sound to communicate, navigate, defend territories, and find food, mates, and safe spaces to settle down. However, human activities are negatively impacting the abilities of marine animals to effectively use sound for critical life functions. For the endangered Hawaiian monk seal with a population size of approximately 1,570 seals, we’re finding that vocal communication (Audio File 1) may play an important role for reproduction, yet we lack a foundational knowledge of the seals’ acoustic environment, better known as a soundscape. In this study, we found that biological sounds produced by snapping shrimp, fish, and seals dominate and shape the underwater soundscapes at critical habitats of the Hawaiian monk seal, with little input from man-made sources (Figure 1).

Figure 1 | A) Spectrogram of a 24-hour period on 11 May 2021 at Lehua Rock. A spectrogram is a visual representation of a sound where the x-axis is time, the y-axis is frequency (or pitch), and the color represents the amplitude of the sound (how loud or soft the sound is). The black icons indicate the source of the sound: snapping shrimp, boat, scuba divers, humpback whale song, Hawaiian monk seal vocalizations, and the vertical migration of the deep scattering layer. B) Spectrogram showing overlapping Hawaiian monk seal vocalizations from 0-1 kHz and humpback whale song from 0.5-5 kHz (listen to this in Audio File.

Figure 1 | A) Spectrogram of a 24-hour period on 11 May 2021 at Lehua Rock. A spectrogram is a visual representation of a sound where the x-axis is time, the y-axis is frequency (or pitch), and the color represents the amplitude of the sound (how loud or soft the sound is). The black icons indicate the source of the sound: snapping shrimp, boat, scuba divers, humpback whale song, Hawaiian monk seal vocalizations, and the vertical migration of the deep scattering layer. B) Spectrogram showing overlapping Hawaiian monk seal vocalizations from 0-1 kHz and humpback whale song from 0.5-5 kHz (listen to this in Audio File 1).

We sought to describe the underwater soundscape, or the acoustic environment, at locations that Hawaiian monk seals utilize for foraging, breeding, communication, and other critical life functions. We wanted to know 1) how loud are ambient (background) sound levels, 2) are sound sources biological, geophysical, or manmade, 3) how do sound sources and levels change throughout the day, and 4) how does the soundscape compare between the more-densely human-populated main Hawaiian Islands and the remote Northwestern Hawaiian Islands. To do this, we deployed passive acoustic recorders, known as SoundTraps, at four critical habitats of the Hawaiian monk seal: Rabbit Island, Oahu; Lehua Rock, Niʻihau; French Frigate Shoals; and Pearl and Hermes Reef. The SoundTraps recorded sounds from 20 Hz up to 24 kHz – this includes low-frequency sounds like earthquakes to high-frequency sounds like dolphin echolocation.

Our results indicate that sound levels are generally loud at these nearshore reef environments thanks to the persistent crackling sounds of snapping shrimp, low-frequency vocalizations of monk seals, and a variety of fish sounds. With little input from manmade sound sources, except at the popular scuba diving site Lehua Rock, we suspect that the elevated sound levels are indicative of healthy reef environments. This is good news for Hawaiian monk seals – less manmade noise means less acoustic masking making it easier to hear and “speak” to each other under water. We also opportunistically recorded sounds from Hurricane Douglas (Category 4) and a 6.2 magnitude earthquake around the time Kilauea began erupting. Overall, this study provides the first description of underwater soundscapes at Hawaiian monk seal critical habitats. These measurements can serve as a baseline for future studies to understand the impact of human activity on underwater soundscapes.

2pUW2 – Pacific Echo: A deep ocean collaborative experiment

Ross Chapman –
University of Victoria
3800 Finnerty Road
Victoria, BC V8P 5C2

Popular version of 2pUW2- Pacific Echo: A deep ocean collaborative experiment
Presented Tuesday afternoon, May 24, 2022
182nd ASA Meeting
Click here to read the abstract

The ocean bottom in large regions of the Pacific Ocean consists of a thin layer of deep ocean sediment on top of oceanic crust (Figure 1).  Crustal rock created at deep ocean fractures at spreading zones moves slowly away outward over millions of years, generating a rugged crustal layer of increasing geological age with increasing distance from the spreading zone.  The presence of solid basalt crustal rock close to the sea floor creates a strikingly different ocean bottom environment compared to most other ocean regions.

Pacific Echo

Figure 1.  The ocean bathymetry in a region of the older Pacific Echo crust sites.  Ocean depth is ~5400 m.

In the latter stages of the Cold War, researchers in navy laboratories carried out a series of experiments at sea to study the impact of this solid rock ocean bottom on sound propagation and underwater target detection.  The experimental programme, Pacific Echo, was a collaboration between researchers at the US Naval Research Laboratory in Washington and the Canadian Defence Research Establishment Pacific in Victoria.  Four sea trials were carried out between 1986 and 1992 at various deep water Pacific sites.  The research objective was to understand the physics of sound interaction with the solid rock ocean bottom, where the dominant reflection of sound was from an interface beneath the sea floor.  Interaction of sound with the rock generates an additional energy loss due to shear waves that propagate in the rock.  This type of energy loss is not significant in other ocean bottom environments that consist of layers of unconsolidated sediment where shear waves in the sediment material are very weak.

Figure 2. Deploying the hydrophone line array from the stern of CFAV Endeavour at sea.

The experimental plan in Pacific Echo involved measurements of the ocean bottom reflection coefficient using a towed horizontal hydrophone line array (Figure 2).  A new technique, the broadside reflectivity measurement (BRM), was developed for efficient acquisition of high quality data.  The BRM method involved two ships, USNS DeSteiguer deployed sound sources while CFAV Endeavour towed the hydrophone array along headings shown in Figure 3.  The array acts as a directional receiver to enable separation of the specular or mirror-like reflection from unwanted contributions arising from basalt outcrop features.

Figure 3. Schematic diagram of ship tracks during the BRM measurement.

The measured reflection coefficients, as in the example shown in Figure 4, revealed large energy loss at low grazing angles less than ~55°.  This loss, due to shear waves generated in the rock, confirmed the hypothesis of reflectivity dominated by the oceanic crust.

Figure 4.  Reflection coefficient measured at one of the older sites in Pacific Echo.

The Pacific Echo data also provided new information about an underlying research question in marine geophysics related to the aging process in oceanic crust.  Estimates of sound speed in basalt derived from the Pacific Echo data revealed sound speeds as low as ~2500 m/s in very young basalt (0-3 million years old), increasing to ~3600 m/s at the oldest sites (~70 million years old).  These results gave support to the research hypothesis that sound speed in oceanic crust increased with the age of the basalt.

2pUWb2 – Study of low frequency flight recorder detection

I Yun Su –
Wen-Yang Liu –
Chi-Fang Chen –
Engineering Science and Ocean Engineering,
National Taiwan University,
No. 1 Roosevelt Road Sec.#4
Taipei City, Taiwan

Li-Chang Chuang –
Kai-Hong Fang –
Taiwan Transportation Safety Board
11th Floor, 200, Section 3,
Beixin Road, Xindian District,
New Taipei City, Taiwan

Popular version of paper 2pUWb2
Presented Tuesday afternoon, December 8, 2020
179th ASA Meeting, Acoustics Virtually Everywhere

A flight recorder is installed in every aircraft to record the flight status. When the aviation accident occurs, this recorder can help clarify the cause of the incident. Furthermore, if the plane crashes into the ocean, the underwater locater beacon (ULB) inside the flight recorder will be triggered which would make a sound that could be located by the rescue team.

In 2009, there was a serious accident involving the Air French flight 447. According to the final report, French Civil Aviation Safety Investigation Authority suggested that the ULB should acquire extended transmission time up to 90 days and increased transmission range. In Taiwan, the flight recorder has already been installed with a 37.5 kHz ULB inside the tail section of every vehicle, and now Taiwan Transportation Safety Board considered to put in an additional 8.8 kHz ULB in the flight belly. (Picture 1)

underwater locater beacon (ULB) inside the flight recorder

Picture 1: The positions of the 37.5 kHz and 8.8 kHz ULB on the plane.

The main propose of this study is to understand the performance of the newly bought 8.8 kHz ULB – DUKANE SEACOM DK180. Firsts off, I did the simulation on both ULB to compare the detection ranges (DR), and according to the beacon specifications, the source level (SL) of the both is 160 dB re 1μPa.

For the DR to be simulated, the transmission loss (TL) which is affected by a lot of different environmental parameters must be determined first. This study is based on the Taiwan database, and using the Gaussian beam propagation to calculate the TL. After the TL is acquired, the noise level (NL) which also has certain impact on the DR has to be determined. Generally, the lower the frequency, the longer the DR. DR can be determined by passive sonar equation, and can be derive the FOM = SL – NL – DT. The DT is Detection Threshold and the FOM is Figure of Merit, which is the maximum TL that can detect. The intersection of the TL and FOM is DR. In the study, the DT is set to be zero. At the Point A, the NL of the 8.8 kHz is 78 dB re 1μPa and for 37.5 kHz is 65 dB re 1μPa, so the FOM of the 8.8 kHz is 82 dB re 1μPa and for 37.5 kHz is 95 dB re 1μPa. The DR in 8.8 kHz ULB is about twice than 37.5 kHz ULB at Point A. (Picture 2)

Picture 2: Detection Ranges of 8.8 kHz ULB and 37.5 kHz ULB in the Point A.

In the study, I have also done the experiment in Taiwan Miaoli offshore. The results also show that the newly bought 8.8 kHz ULB would have a smaller TL and longer DR. In summary, with an additional 8.8 ULB, the more precise prediction of the beacon location could be obtained.

Controlled source level measurements of whale watch boats and other small vessels

Jennifer L. Wladichuk –
David E. Hannay, Zizheng Li, Alexander O. MacGillivray
JASCO Appl. Sci., 2305 – 4464 Markham St.
Victoria, BC V8Z 7X8, Canada

Sheila Thornton
Sci. Branch
Fisheries and Oceans Canada
Vancouver, BC, Canada

Popular version of paper 2pUWb8
Presented Tuesday afternoon, Nov 6, 2018
176th ASA Meeting, Victoria, BC, Canada

The Vancouver Fraser Port Authority’s Enhancing Cetacean Habitat and Observation (ECHO) program sponsored deployment of two autonomous marine acoustic recorders (AMAR) in Haro Strait (BC), from July to October 2017, to measure sound levels produced by large merchant vessels transiting the strait. Fisheries and Oceans Canada (DFO), a partner in ECHO, supported an additional study using these same recorders to systematically measure underwater noise emissions (0.01–64 kHz) of whale watch boats and other small vessels that operate near Southern Resident Killer Whales (SRKW) summer feeding habitat. During this period, 20 different small vessels were measured operating at a range of speeds (nominally 5 knots, 9 knots, and cruising speed). The measured vessels were catagorized into six different types based primarily on hull shape: ridged-hull inflatable boats (RHIBs), monohulls, catamarans, sail boats, landing craft, and one small boat (9.9 horsepower outboard). Acoustic data were analyzed using JASCO’s PortListen® software system, which automatically calculates source levels from calibrated hydrophone data and vessel position logs, according to the ANSI S12.64-2009 standard for ship noise measurements. To examine potential behavioural effects on SRKW, vessel noise emissions were analyzed in two frequency bands (0.5–15 kHz and >15 kHz) corresponding to the whales’ communication and echolocation ranges, respectively (Heise et al. 2015). We found that generally, with increased speed, decibel levels increased across the different vessel types, particularly in the echolocation band (Table 1). However, the speed trends were not as strong as those of large merchant vessels. Of the vessels measured, monohulls commonly had the lowest source levels in both SRKW frequency bands, while catamarans had the highest source levels in the communication band and the landing craft had the highest levels in the echolocation band at all speeds (Figure 1). Another key finding was the amount of noise onboard echosounders produced; a significant peak at approximately 50 kHz was present in some vessels, which is within the most sensitive hearing range of SRKW.


Table 1. Average source level for each vessel type in the SRKW communication and echolocation frequency bands for slow, medium, and fast vessel speeds.


Figure 1. Average one-third octave band source levels for each vessel type for the slow speed passes (≤7 kn, ie. whale-watching speed). Due to non-vessel related noise at frequencies below approximately 200 Hz (grey vertical line), levels at those low frequencies cannot be associated with vessel source levels. The peak observed at around 50 kHz is from onboard echosounders.

Literature cited:
Heise, K.A., L. Barret-Lennard, N.R. Chapman, D.T. Dakin, C. Erbe, D. Hannay, N.D. Merchant, J. Pilkington, S. Thornton, et al. 2017. Proposed metrics for the management of underwater noise for southern resident killer whales. Coastal Ocean Report Series. Volume 2, Vancouver, Canada. 30 pp.

5aUW1 – Ship-of-opportunity noise inversions for geoacoustic profiles of a layered mud-sand seabed

Dag Tollefsen –
Norwegian Defence Research Establishment,
Horten, NO-3191, Norway

Stan E. Dosso –
School of Earth and Ocean Sciences,
Victoria BC, V8W 3P6, Canada

David P. Knobles –
Knobles Scientific and Analysis, Austin, Texas 78755, USA

Popular version of paper 5aUW1
Presented Friday morning, November 9, 2018
176th ASA Meeting, Victoria, BC, Canada

We infer geoacoustic properties of seabed sediment layers via remote sensing using underwater sound from a container ship.  While several techniques are available for seabed characterization via acoustic remote sensing, this is a first demonstration using noise from a large ship-of-opportunity (i.e., a passing ship with no connection to an experiment).  A benefit of this passive acoustics approach is that such sound is readily available in the ocean.

Our data were collected as part of a large coordinated ocean acoustic experiment conducted at the New England Mud Patch in March, 2017 [1].  To investigate properties of a muddy seabed, the experiment employed multiple techniques including direct measurements by geophysical coring [2] and remote sensing using sound from various controlled acoustic sources.  Our team deployed a 480-m long linear array of hydrophones on the seabed to record noise from passing ships.

The array was located two nautical miles from a commercial shipping lane leading to the Port of New York and New Jersey.  An average of three ships passed the array daily.  We identified ship passages by “bathtub” interference patterns (Fig. 1) in the recordings.  The structure seen in Fig. 1 is due to the interaction between ship-to-hydrophone sound paths, some interacting with the seabed, shifting with time as the ship passes the hydrophone.

Ship locations were obtained from Automatic Identification System data provided by the US Coast Guard Navigation Center.  This enabled us to run a numerical model of underwater sound propagation from source to receivers.  The final step is inverse modeling, where a large number of possible seabed models were sampled probabilistically, with each model used to predict the corresponding sound field that was matched with the recorded data.  We used Bayesian sampling and statistical inference methods based on [3].

Inferred geoacoustic profiles (Fig. 2) indicate fine-grained sediment (mud) in the upper seabed and coarse-grained (higher sound speed and density) sediment (sand) in the lower seabed.  Results are in overall good agreement with sediment core data.  This work establishes that noise from large commercial ships can contain usable information for seabed characterization.

[Work supported by the Office of Naval Research, Ocean Acoustics. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of ONR].


Figure 1. Spectrogram of noise measured on a hydrophone on the seabed (left) due to a passing container ship (right).


Figure 2. Inversion results in terms of probability densities for sediment layer interface depths (left), and geoacoustic properties of sound speed (middle), and density (right) as a function of depth into the seabed. Warm colors (e.g., red) indicate high probabilities.

[1]            P.S. Wilson and D.P. Knobles, “Overview of the seabed characterization experiment 2017,” in Proc. 4th Underwater Acoustics Conference and Exhibition, 743-748, ISSN 2408-0195 (2017).

[2]       J.D. Chaytor, M.S. Ballard, B. Buczkowski, J.A. Goff, K. Lee, A. Reed, “Measurements of Geologic Characteristics, Geophysical Properties, and Geoacoustic Response of Sediments from the New England Mud Patch,” submitted to IEEE J. Ocean Eng. (2018).

[3]            S.E. Dosso, J. Dettmer, G. Steininger, and C.W. Holland, “Efficient trans-dimensional Bayesian inversion for seabed geoacoustic profile estimation,” Inverse Problems, 30, 29pp (2014).

5aUW7 – Using Noise to Probe Seafloor

Tsuwei Tan –
Oleg A. Godin –
Physics Dept., Naval Postgraduate School
1 University Cir.
Monterey CA, 93943, USA

Popular version of paper 5aUW7
Presented Friday morning, November 9, 2018, 10:15-10:30 AM
176th ASA Meeting, Victoria, BC Canada

Scientists have long used sound to probe the ocean and its bottom. Breaking waves, roaring earthquakes, speeding supertankers, snapping shrimp, and vocalizing whales make the ocean a very noisy place. Rather than “shouting” above this ambient noise with powerful dedicated sound sources, we are now learning how to measure ocean currents and seafloor properties using the noise itself. In this paper, we combine long recordings of ambient noise with a signal processing skill called time warping to quantify seafloor properties. Time warping changes the signal rate so we can extract individual modes, which carry information about the ocean’s properties.

Experiment & Data
We pulled our data from Michael Brown and colleagues [1].  They recorded ambient noise in the Straits of Florida with several underwater microphones (hydrophones) continuously over six days (see Figure 1). We applied time warping to this data. By measuring (cross-correlating) noise recordings made at points A and B several kilometers apart, one obtains a signal that approximates the signal received at A when a sound source is placed at B. With this approach, a hydrophone becomes a virtual sound source. The sound of the virtual source (the noise cross-correlation function) can be played in Figure 2. There are two nearly symmetric peaks in the cross-correlation function shown in Figure 1 because A also serves as a virtual source of sound at B. Having two virtual sources allowed Oleg Godin and colleagues to measure current velocity in the Straits of Florida [2].

Figure 1. Illustration of the site of the experiment and the cross-correlation function of ambient noise received by hydrophones A and B in 100 m-deep water at horizontal separation of about 5 km in the Straits of Florida. Figure 2. Five-second audio of correlated ambient noise from Figure 1: At receiver A, a stronger impulsive sound starts at 3.25 sec, which is the time it takes underwater acoustic waves to travel from B to A. Listen here

Retrieving Environmental Information
Sound travels faster or slower underwater depending on how soft or hard the seafloor is. We employ time warping to analyze the signal produced by the virtual sound source. Time warping is akin to using a whimsical clock that makes the original signal run at a decreasing pace rather than steadily (Figure 3a  3b). The changing pace is designed to split the complicated signal into simple, predictable components called normal modes (Figure 3c  3d). Travel times from B to A of normal modes at different acoustic frequencies prove to be very sensitive to sound speed and density in the ocean’s bottom layers. Depth-dependence of these geo-acoustic parameters at the experimental site as well as precise distance from B to A can be determined by trying various sets of the parameters and finding the one that best fits the acoustic normal modes revealed by the ambient noise measurements. The method is illustrated in Figure 4. The sound of the virtual source (Figure 2), which emerges from ambient noise, reveals that the ocean bottom at the experimental site is an 11 m-thick layer of sand overlying a much thicker layer of limestone (Figure 5).

Figure 3. Time warping process: Components of the virtual source signal from noise are separated in the spectrogram of the warped signal from (c) to (d). Figure 4. Comparison of measured travel times of normal modes to the travel time theoretically predicted for various trial models of the ocean bottom and the geometry of the experiment. The measured and theoretically predicted travel times are shown by circles and lines, respectively. Individual normal modes are distinguished by color. By fixing the geo-acoustic parameters (sound speed and density), the precise range r between hydrophones A and B can be found by minimizing the difference between the measured and predicted travel times. The best fit is found at r = 4988m. Watch here

Figure 5. Ocean bottom properties retrieved from ambient noise. Blue and red lines show sound speed in water and bottom, respectively, at different depths below the ocean surface. The ratios ρs and ρb of the bottom density to seawater density are also shown in two bottom layers.  

Conclusion Ambient noise does not have to be an obstacle to acoustic remote sensing of the ocean.  We are learning how to use it to quantify ocean properties. In this research, we used ambient noise to probe the ocean bottom. Time warping has been applied to ambient noise records to successfully measure sound speeds and densities at different depths below the seafloor in the Straits of Florida. Our passive acoustic approach is inexpensive, non-invasive, and environmentally friendly. We are currently working on applying the same approach to the extensive underwater ambient noise recordings obtained at several sites off New Jersey during the Shallow Water 2006 experiment.  


[1] M. G. Brown, O. A. Godin, N. J. Williams, N. A. Zabotin, L. Zabotina, and G. J. Banker, “Acoustic Green’s function extraction from ambient noise in a coastal ocean environment,” Geophys. Res. Lett. 41, 5555–5562 (2014). [

2] O. A. Godin, M. Brown, N. A.  Zabotin, L. Y. Zabotina, and N. J. Williams, “Passive acoustic measurement of flow velocity in the Straits of Florida.” Geoscience Lett. 1, 16 (2014).