Popular version of 3pPA4 – Estimation of seabed properties at the New England Mud Patch using vector acoustic measurements
Presented at the 190th ASA Meeting
Read the abstract at https://eppro01.ativ.me/web/planner.php?id=ASASPRING2026
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
Shear is one of the fundamental mechanical parameter that bridges geological, engineering, and environmental aspects of the seafloor influencing loss of acoustic energy in addition to other factors such as seafloor stability, load bearing capacity, sediment transport and deposition. Shear wave velocity is one of the parameters which characterizes shear strength of the sediments. In this study we use waves propagating along the seabed (interface waves) to estimate the shear speed of the sediments.
Interface waves:
Interface waves are waves which travel along an interface between two media. Examples include Rayleigh waves (waves which travel along land) and Scholte waves (waves along seabed). Figure 1 shows a typical scenario in which a sensor on the seabed will measure Scholte waves in addition to acoustic waves along different paths (direct, surface reflected etc.).
Fig. 1: Schematic of a typical scenario in which a sensor on the seabed measures interface waves in addition to acoustic waves along different paths. Right panel shows the OBX sensor package.
The Scholte waves have the following characteristics:
They have maximum amplitude at the water-sediment interface (seabed). The data used in this study is from a receiver deployed on the seabed.
Particles in the medium traces an elliptical path in water and sediment.
The magnitude of the particle motion decreases exponentially as a function of distance from the interface in both media.
The ratio of the horizontal to vertical component of the particle motion is strongly correlated to the shear velocity and thickness of the sediment. In this study we have used this characteristics of the Scholte wave to estimate the shear velocity in the sediment.
We measured the particle velocities along three mutually orthogonal directions associated with Scholte waves using a senor package (Ocean Bottom Recorder or OBX, shown in the right panel of Figure 1) deployed on the seabed during an experiment in 2022 in the New England Mud patch (NEMP), 200 km south of Martha’s Vineyard in 70 m of water depth. As the name implies, NEMP has a layer of mud/clay sediments on top of sand. Many types of sources generated sound at different frequency bands in addition to sources of opportunity such as ships passing close to the experimental area. Figure 2 shows an example of the motion (velocity in mm/s) of the particle measured by the OBX during the experiment. This represents the motion of the particle for a short period of time (~ 1 seconds) in a narrow frequency band.
Fig.2: The trace of the particle motion (hodogram) in the source-to-receiver direction (radial, shown in pink), in the vertical direction (normal to the seabed, shown in yellow). The red curve shows the path of the particle in the vertical plane containing the source and receiver.
The strong correlation of horizontal to vertical ratio (HVSR) of the particle motion to shear speed in the sediment and sediment layer thickness is demonstrated using simulated data in Figure 3. Particle motion data were simulated for a ocean environment as shown in the left panel of Figure 3. Sound speeds in the water column, sediment and basement were assumed as 1500 m/s, 1495 m/s and 1750 m/s respectively. The shear speeds in the sediment and basement were assumed as 50 m/s and 300 m/s respectively. Densities in the water column, sediment and basement were assumed as 1025 kg/m3, 1650 kg/m3 and 2000 kg/m3 respectively.
Fig.3: Ratio of the horizontal to vertical (HVSR) particle motion amplitude as a function of frequency (right panel). Particle motion was simulated for an ocean environment as shown in the left panel.
The particle velocities of the Scholte waves for this environment were generated using a numerical model and ratio of the horizontal to vertical component of the particle motion amplitudes were calculated as a function of frequency (Figure 3; right panel). The HVSR curve shows a dominant peak at 2 Hz which correspond to the shear resonant frequency. The data measured in the NEMP experiment is used to calculate the HVSR and then identify the peak in the frequency versus HVSR curve. HVSR is then modelled for various shear speeds and layer thicknesses. The shear speed which produces the best data-model match (particularly the peak frequency) is then estimated.
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
Fig. 1: Schematic of landfast sea ice and the field setup used in this study. Landfast sea ice is attached to the seafloor near the coast, often anchored by grounded ridges (shown here). A fiber optic cable (blue line) is laid along the snow-ice interface and acts as a series of sensors that “listen” to vibrations in the ice. The figure is not to scale. (Figure by Maia LeDoux and the Applied Physics Laboratory Graphics Department; cropped and annotated by the authors to show the fiber optic cable.)
Arctic landfast sea ice is the ice attached to the seafloor near the coast (see Fig. 1). It plays an important role in ocean–atmosphere interactions and supports local communities, wildlife, and coastal stability. As the climate warms, knowing when this ice might crack or break away is increasingly important for both community safety and coastal protection.
Studying landfast ice is difficult. Researchers often have to drill through thick ice or drag along heavy instruments across large areas. Satellites help, but clouds and limited coverage can leave gaps. We need a way to continuously “listen” to the ice over long distances.
Our solution uses a technology called distributed acoustic sensing. It turns a standard fiber optic cable, similar to what brings internet to homes, into hundreds of vibration sensors. Instead of placing many separate instruments, one cable can measure motion along its entire length with high detail.
In 2025, we installed a 2-kilometer-long cable across landfast sea ice in Arctic Alaska (see Fig. 2). A custom sled cut a shallow trench in the snow and ice, laid the cable, and covered it. This setup effectively created about 600 sensors recording vibrations 500 times per second.
What did we hear? We detected waves traveling through the ice, generated by ocean swells offshore (see Fig. 3). The ice behaves like a thin floating plate sitting on the water, bending as waves pass underneath. By analyzing these motions, we can estimate how stiff or “bendy” the ice is and how much stress it is under from waves and wind.
Fig. 3: Example of measurements from the fiber optic cable. The horizontal axis shows time, and the vertical axis shows distance along the cable (farther from shore upward (see Fig. 2). Red and blue bands indicate the ice stretching and compressing as ocean waves pass underneath, causing the ice to bend. By analyzing these patterns, we can estimate how stiff the ice is and how it responds to waves.
This information will help answer key questions: How thin or weak does the ice need to be before it breaks? What role do waves and wind play? Ultimately, this can improve predictions of “breakout” events, when large pieces of ice detach, and seasonal breakup.
This work is a collaboration with a broader effort, the Arctic PISCES project, to better observe, understand and predict the ocean-ice-atmosphere system in Arctic coastal and inner-shelf regions. With continued monitoring, fiber optic sensing could become a powerful new way to track the stability of Arctic sea ice.
School of Biology, University of St Andrews, St Andrews, Fife, KY16 8LB, United Kingdom
Popular version of 3pAO2 – Long-term global ocean observing using sound
Presented at the 189th ASA Meeting
Read the abstract at https://doi.org/10.1121/10.0040876
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
Of all the ways to sense objects in the ocean, sound reaches the furthest. Light seldom travels more than hundreds of meters in the ocean, but sound can travel thousands of kilometers. For example, the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) created a network of just 6 hydrophones (underwater microphones) that can detect a nuclear explosion anywhere in the global oceans (Figure 1).
Figure 1. Map of 6 underwater acoustic sensing stations of the Comprehensive Test-Ban Treaty Organization.
Reproduced with permission from Ainslie, Michael A., et al. “Ocean soundscapes and trends from 2003 to 2021: 10–100 Hz.” The Journal of the Acoustical Society of America 157.6 (2025):4358-4384, Figure 1.
Sound can travel so far that listening has long been selected for detecting threats such as submarines, but the ocean carries acoustic signals of wind, waves, rain and sea ice that are important for monitoring climate change, and the sounds of animals vocalizing underwater tell us about their distribution and about the health of marine ecosystems.
Using sound to understand changes in the ocean requires many years of data to sort out changes on daily, seasonal, and longer-term time scales. The CTBTO hydrophone array is one of few sources of such long time series of ocean sound.
Figure 2. Measured and modeled Sound Pressure Level in the 10-40 Hz band from Wake Island in the Pacific Ocean. Reproduced with permission from Robinson, Stephen, et al. “Impact of the COVID-19 pandemic on levels of deep-ocean acoustic noise.” Scientific Reports13.1 (2023): 4631, Figure 2. Creative Commons Attribution 4.0 International License
Figure 2 shows 12 years of low frequency acoustic data from the Pacific. The strongest pattern in sound level is annual variation, but you can also see a longer-term pattern of increasing variation from 2010 to 2020.
Figure 3. Expanded view of sound level data from figure 2, here from 2018-2020. Reproduced with permission from Robinson, Stephen, et al. “Impact of the COVID-19 pandemic on levels of deep-ocean acoustic noise.” Scientific Reports13.1 (2023): 4631, Figure 4. Creative Commons Attribution 4.0 International License
Analyzing data from before COVID hit in 2020, shown with blue dots in figure 3, makes it possible to estimate what sound levels were expected in 2020 based on earlier years. The data points change color from blue to red once COVID started to affect seagoing activities. You can see that many of the red dots from January to July 2020 are lower than the gray area predicted from pre-COVID data, demonstrating that the reduction in human activities during COVID reduced sound levels around Wake Island.
Excessive sound can also harm wildlife by damaging hearing and by causing stress or disturbance. Recordings from the west coast of the US have shown that increased shipping caused underwater sound energy to more than double each decade from the 1960s to the 1990s. This increase in human-generated ocean noise stimulated concerns about increasing effects of noise on marine life. In June 2025, a coalition of 37 countries declared “ocean noise is intensifying, driven by the ongoing expansion of global shipping activities and development of ocean industries” and formed a high ambition coalition for a quiet ocean (https://www.foraquietocean.org).
Figure 4. Trends in annual mean sound pressure level (SPL) from 2006-2021 at Ascension Island at 5 frequency bands: VLF=9-14 Hz; LF=14-28 Hz, MF=28-56 Hz; HF=56-112 Hz; ADEON B = 9-89 Hz. Reproduced with permission from Ainslie, Michael A., et al. “Ocean soundscapes and trends from 2003 to 2021: 10–100 Hz.” The Journal of the Acoustical Society of America 157.6 (2025):4358-4384, Figure 18.
Analysis of CTBTO data from sites around the world document changes in ocean sound after 2000. This century has not seen significant increases in sound pressure level at any of the CTBTO sites. Figure 4 shows the long term decline in sound level in 4 frequency bands from 2006-2021 at Ascension Island in the Atlantic Ocean. A close look at the acoustic record (Figure 5A) shows that airguns used to prospect for offshore oil and gas along the African and South American coasts were a primary source. Figure 5B shows that the best predictor of sound pressure level there in the MF band is the price of Brent Crude Oil. When the price of oil is low, there are fewer surveys. The major sources of sound energy varied between sites and included natural sounds of earthquakes and whales and sounds of human activities such as shipping.
Figure 5. 5A shows a waveform on the top and spectrogram (plot of frequency against time) of a series of pulses from an airgun source recorded from the CTBTO station at Ascension Island. Figure 5B shows how the sound level recorded there correlates with the price of oil. Reproduced with permission from Ainslie, Michael A., et al. “Ocean soundscapes and trends from 2003 to 2021: 10–100 Hz.” The Journal of the Acoustical Society of America 157.6 (2025):4358-4384, 5A: Figure 16; 5B Figure 20.
Access to long time series of ocean sound data is critical for understanding how changes in sound sources cause trends in ocean sound. This information is not only critical for estimating indicators of climate change and ocean health, but also for managing the effects of sound. Noise from human activities may not have been increasing ocean sound levels globally in this century, but sound produced by more localized activities and the harm it causes to wildlife needs to be measured and managed.
Over the last 50 years, the integration of global weather data with improved computer models has drastically improved our ability to forecast weather. The applications of ocean sound to measure threats, climate change and ecosystem health are so important that we need similar expansion and improvement of our global ocean sound observation networks so that we can forecast and manage these important applications.
Monterey Bay Aquarium Research Institute, Moss Landing, CA, 95039, United States
Popular version of 4aUW7 – Wind-driven movement ecology of blue whales detected by acoustic vector sensing
Presented at the 188th ASA Meeting
Read the abstract at https://doi.org/10.1121/10.0038108
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
A technology that captures multiple dimensions of underwater sound is revealing how blue whales live, thereby informing whale conservation.
The most massive animal ever to evolve on Earth, the blue whale, needs a lot of food. Finding that food in a vast foraging habitat is challenging, and these giants must travel far and wide in search of it. The searching that leads them to life-sustaining nutrition can also lead them to a life-ending collision with a massive fast-moving ship. To support the recovery of this endangered species, we must understand where and how the whales live, and how human activities intersect with whale lives.
Toward better understanding and protecting blue whales in the California Current ecosystem, an interdisciplinary team of scientists is applying a technology called an acoustic vector sensor. Sitting just above the seafloor, this technology receives the powerful sounds produced by blue whales and quantifies changes in both pressure and particle motion that are caused by the sound waves. The pressure signal reveals the type of sound produced. The particle motion signal points to where the sound originated, thereby providing spatial information on the whales.
A blue whale in the California Current ecosystem. Image Credit: Goldbogen Lab of Stanford University / Duke Marine Robotics and Remote Sensing Lab; NMFS Permit 16111.
For blue whales, it is all about the thrill of the krill. Krill are small-bodied crustaceans that can form massive swarms. Blue whales only eat krill, and they locate swarms to consume krill by the millions (would that be krillions?). Krill form dense swarms in association with cold plumes of water that result from a wind-driven circulation called upwelling. Sensors riding on the backs of blue whales reveal that the whales can track cold plumes precisely and persistently when they are foraging.
The close relationships between upwelling and blue whale movements motivates the hypothesis that the whales move farther offshore when upwelling habitat expands farther offshore, as occurs during years with stronger wind-driven upwelling. We tested this hypothesis by tracking upwelling conditions and blue whale locations over a three-year period. As upwelling doubled over the study period, the percentage of blue whale calls originating from offshore habitat also nearly doubled. A shift in habitat occupancy offshore, where the shipping lanes exist, also brings higher risk of fatal collisions with ships.
However, there is good news for blue whales and other whale species in this region. Reducing ship speeds can greatly reduce the risk of ship-whale collisions. An innovative partnership, Protecting Blue Whales and Blue Skies, has been fostering voluntary speed reductions for large vessels over the last decade. This program has expanded to cover a great stretch of the California coast, and the growing participation of shipping companies is a powerful and welcome contribution to whale conservation.
Alba Solsona-Berga – asolsonaberga@ucsd.edu Scripps Institution of Oceanography University of California San Diego La Jolla, CA 92037 United States
Instagram: @sripps_mbarc
Popular version of 2pAO5 – Shaping the acoustic field in the Gulf of Mexico: marine mammals linked to topography and oceanographic features Presented at the 188th ASA Meeting Read the abstract at https://doi.org/10.1121/10.0037682
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
Exploring the Lives of the Ocean’s Deepest Divers After the Deepwater Horizon oil spill, restoring marine mammal populations in the Gulf of Mexico became a priority. Protecting these animals starts with understanding how they use their habitat and where they go. Sperm whales and beaked whales are some of the ocean’s most extreme divers, spending much of their lives navigating the dark depths. They rely on bursts of sound called echolocation clicks to find their prey and navigate. These clicks act like acoustic fingerprints, helping us figure out where whales go and what environments they prefer.
To track their movements, we set up 18 underwater listening stations throughout the Gulf. These instruments recorded sounds continuously for three years. By analyzing this data, we discovered patterns in where the whales appeared and how those locations were linked to oceanographic features like currents and slopes.
Video: Deploying the instruments.
Where Whales Go Different whale species tend to favor different parts of the deep Gulf. Goose-beaked whales often stay near deep eddies and steep slopes. Gervais’ beaked whales are more likely to follow surface and midwater eddies, while sperm whales mostly stick to areas where freshwater from rivers mixes with the open ocean. They tend to avoid the tropical Loop Current, a warm flow from the Caribbean into the Gulf, that seems to create conditions less favorable for these whales.
An example of how marine mammals use different parts of the Gulf of Mexico. The maps show ocean features at three depth ranges: surface (0-250 m), mid-depth (700-1250 m), and deep (1500-3000 m). Dolphins are shown in the surface plot, sperm whales in the mid-depth plot, and goose-beaked whales in the deep plot. Colors indicate water movement, with red showing strong currents and blue showing calmer areas. Circles mark recording stations, with bigger circles showing more animals detected.
Whales Shape Their Environment Whales don’t just adapt to their surroundings, they also shape them. Their powerful clicks, produced by the millions, bounce off the seafloor and underwater features, making their presence a key part of the local acoustic environment. Where whales occur, the acoustic environment changes, influenced both by their vocalizations and by the prey that may be present. Prey layers can influence how sound propagates through the water, adding complexity to the acoustic field. Detecting whales in specific areas helps us understand how the acoustic environment might vary under different conditions. Mapping where whales are present also reveals potential biological hotspots and helps us understand how sound behaves in these deep-sea habitats.
Why This Matters This research is a collaboration between scientists from the United States and Mexico, supported by NOAA’s RESTORE Science Program, the Deepwater Horizon Restoration Open Ocean Marine Mammal Trustee Implementation Group, and the Office of Naval Research Task Force Ocean. These detailed maps of whale distribution are vital for identifying critical habitats and guiding conservation strategies. They help us understand how threats like oil spills, industrial activity, and environmental changes impact whale populations, allowing us to plan effective mitigation and restoration efforts to maintain healthy ecosystems.
Brandyn Lucca – blucca@uw.edu
Bluesky: @brandynlucca.bsky.social
Instagram: @brandynmark
Applied Physics Laboratory, University of Washington, Henderson Hall (HND), 1013 NE 40th St, Seattle, Washington, 98105, United States
Joseph Warren
Instagram: @warren.bioacoustics.lab
Bluesky: @warren-lab.bsky.social
Affiliation: School of Marine and Atmospheric Sciences, Stony Brook University
Popular version of 2aAO9 – Active acoustic detection of fish and zooplankton along bathymetric features of the New York Bight
Presented at the 188th ASA Meeting
Read the abstract at https://doi.org/10.1121/10.0037522
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
Imagine standing on the beach in New York City, looking beyond the harbor over the horizon where rolling waves meet an armada of ships lined up to unload their cargo. What remains hidden from view are the vast underwater plains, valleys, and canyons teeming with marine life beneath the surface. From a bird’s-eye view, this area forms the New York Bight, a stretch of ocean off the coast of New York City situated between southern New Jersey and eastern Long Island. This seascape offers prime real estate for animals ranging from copepods to whales.
Some animals often gather along the shelfbreak, where the relatively flat, shallow seafloor of the continental shelf dramatically changes to the deep sea. Others prefer life in a well-known ecological hotspot and one of the largest marine canyons in the world: the Hudson Canyon. Like many people, marine animals choose habitats based on the amenities they offer, but their preferences can evolve as they age or in response to environmental shifts. Some may leave the New York Bight entirely, while others may settle in undiscovered hotspots elsewhere. But how can scientists find these hotspots in the first place?
How do scientists “see” beneath the waves?
Researchers use a technique called “active acoustics” to get snapshots of where animals are in the water column across large areas that can complement other sampling methods like nets. With this approach, they send out short pulses of sound from a moving ship and measure the echoes that bounce back from the seafloor or are created from animals that live in the water column. The equipment scientists use to measure these echoes is similar to bottom-finders and fish-finding systems used by fishers and boaters. The results can reveal dense fish schools clustered along the steep walls of a canyon or zooplankton aggregations in the near-surface waters along the shelfbreak. These patterns help scientists better understand how seascapes shape habitat preferences among marine organisms (Figure 1).
Echograms are one way to visualize acoustic backscatter, with color scale units corresponding to the total energy in echoes measured from marine organisms. This echogram reveals how animals are distributed vertically in the water column along a ship transect that crossed the Hudson Canyon. The dark gray region corresponds to the seafloor.
To carry out this research, scientists measure echoes from animals in the water column, collect fish and zooplankton using nets and trawls, and measure how temperature and salinity (and other environmental factors like oxygen) vary in the ocean as you go down in depth. Researchers collected the data for this study during seasonal surveys aboard a research vessel that covered the waters south of Long Island, New York, out to the shelfbreak, approximately 140 miles away (Figure 2).
Acoustic surveys were conducted along seven transect lines (black lines) with biological and seawater sampling stations at each square point. The white lines represent isobaths, or lines of constant depth, at 25, 50, 100, 500, 100, and 2000 m. The orange and red stars indicate where the Hudson Shelf Valley and Hudson Canyon begin.
Location, location, location: Hotspots change with the seasons
The New York Bight regions with the most fish and zooplankton (as measured by our echosounders) change with the seasons. In winter and early spring, most organisms concentrated farther offshore, often along the canyon edges or beyond the shelfbreak. As summer arrives, these biological hotspots grow along the shelfbreak, especially in and around the canyons, and move closer to shore. By fall, acoustic measurements showed that fish and zooplankton spread more evenly across the continental shelf.
For fish living near the seafloor, a seasonal feature called the Mid-Atlantic Cold Pool plays a major role in their movements. This layer of cold water forms on and above the seafloor over part of the continental shelf each spring and slowly decreases in volume throughout the summer. When the Cold Pool forms, many near-bottom fish shift away from their spatial extent due to the fish having temperature preferences and gather in the Hudson Canyon, other shelfbreak canyons, inshore areas, and the Hudson Shelf Valley. As the Cold Pool shrinks in late summer, their distribution becomes more like the broader patterns observed for overall biological backscatter (Figure 3).
An example echogram of biological backscatter near the shelfbreak. The 9º (gray) and 10º (black) isotherms, or lines of constant temperature, approximate the lateral and vertical extent of the Mid-Atlantic Cold Pool that, in this case, nearly walled this aggregation off from the inshore waters on the continental shelf entirely.
From underwater sound to action: Guiding management decisions
The New York Bight is a dynamic and productive ecosystem that experiences significant fishing pressure, shipping activity, and offshore energy development. By combining acoustic surveys with biological net sampling and oceanographic measurements, scientists can identify areas that fish and zooplankton may prefer (or avoid) throughout the year. Surveys such as this one help guide management decisions that balance the economic and commercial health of the New York Bight.
Fig. 1: Schematic of a typical scenario in which a sensor on the seabed measures interface waves in addition to acoustic waves along different paths. Right panel shows the OBX sensor package.
Fig.2: The trace of the particle motion (hodogram) in the source-to-receiver direction (radial, shown in pink), in the vertical direction (normal to the seabed, shown in yellow). The red curve shows the path of the particle in the vertical plane containing the source and receiver.
Fig.2: The trace of the particle motion (hodogram) in the source-to-receiver direction (radial, shown in pink), in the vertical direction (normal to the seabed, shown in yellow). The red curve shows the path of the particle in the vertical plane containing the source and receiver.
Fig.3: Ratio of the horizontal to vertical (HVSR) particle motion amplitude as a function of frequency (right panel). Particle motion was simulated for an ocean environment as shown in the left panel.
Fig.3: Ratio of the horizontal to vertical (HVSR) particle motion amplitude as a function of frequency (right panel). Particle motion was simulated for an ocean environment as shown in the left panel.
Fig. 1: Schematic of landfast sea ice and the field setup used in this study. Landfast sea ice is attached to the seafloor near the coast, often anchored by grounded ridges (shown here). A fiber optic cable (blue line) is laid along the snow-ice interface and acts as a series of sensors that “listen” to vibrations in the ice. The figure is not to scale. (Figure by Maia LeDoux and the Applied Physics Laboratory Graphics Department; cropped and annotated by the authors to show the fiber optic cable.)
Fig. 2: Satellite image of the landfast sea ice showing the 2-kilometer-long fiber optic cable (red line). The cable extends from near the coast out across the ice. Image taken on May 26, 2026. (Image © Planet Labs PBC, CC BY-NC-SA 2.0; labels, cable layout, and axes added by the authors.)
Fig. 3: Example of measurements from the fiber optic cable. The horizontal axis shows time, and the vertical axis shows distance along the cable (farther from shore upward (see Fig. 2). Red and blue bands indicate the ice stretching and compressing as ocean waves pass underneath, causing the ice to bend. By analyzing these patterns, we can estimate how stiff the ice is and how it responds to waves.
Figure 1. Map of 6 underwater acoustic sensing stations of the Comprehensive Test-Ban Treaty Organization.
Figure 4. Trends in annual mean sound pressure level (SPL) from 2006-2021 at Ascension Island at 5 frequency bands: VLF=9-14 Hz; LF=14-28 Hz, MF=28-56 Hz; HF=56-112 Hz; ADEON B = 9-89 Hz. Reproduced with permission from Ainslie, Michael A., et al. “Ocean soundscapes and trends from 2003 to 2021: 10–100 Hz.” The Journal of the Acoustical Society of America 157.6 (2025):4358-4384, Figure 18.
A blue whale in the California Current ecosystem. Image Credit: Goldbogen Lab of Stanford University / Duke Marine Robotics and Remote Sensing Lab; NMFS Permit 16111.
