Alexandra M Padilla – apadilla@ccom.unh.edu
Thomas C Weber – weber@ccom.unh.edu
University of New Hampshire
24 Colovos Road
Durham, NH, 03824

Frank Kinnaman – frank_kinnaman@ucsb.edu
David L Valentine – valentine@ucsb.edu
University of California – Santa Barbara
Webb Hall
Santa Barbara, CA, 93106

Popular version of paper 2aAO5
Presented Wednesday morning, June 9, 2021
180^th ASA Meeting, Acoustics in Focus

Researchers have been studying the release of methane, a greenhouse gas, in the form of bubbles from different regions of the ocean’s seafloor for decades to understand its impact on global climate change and ocean acidification (Kessler, 2014). One region, the Coal Oil Point (COP) seep field, is a well-studied natural hydrocarbon (e.g., oil droplets and methane gas bubbles) seep site, known for its prolific hydrocarbon activity (Figure 1; Hornafius et al., 1999). Researchers that have studied the COP seep field have observed both spatial and temporal changes in the gas flow in the area, that has been thought to be linked to external processes such as tides (Boles et al., 2001) and offshore oil production from oil rigs within the seep field (Quigley et al., 1999).

Figure 1. Video of methane gas bubbles rising through the ocean’s water column within the COP seep field.

In recent years, an oil platform within the COP seep field, known as Platform Holly, has become inactive and decommissioned, and there has been a resurgence in natural hydrocarbon seepage activity in the vicinity of the platform based on anecdotal observations. This led a group  from UNH and UCSB to map the hydrocarbon activity in the COP seep field (Padilla et al., 2019), where we were able to identify a large patch of high seepage activity near Platform Holly (Figure 2). The shut-in at Platform Holly provided us with the opportunity to deploy a long-term acoustic monitoring system to study both the spatial and temporal changes in hydrocarbon gas flow in the region and to assess how it is affected by external processes.

Figure 2. a) Acoustic map of natural hydrocarbon activity within the COP seep field (Padilla et al., 2019). b) Zoomed in acoustic map near Platform Holly. c) Image of Platform Holly.

We mounted a split-beam echosounder, at a depth of approximately 8 m  below the sea surface, on one of Platform Holly’s cross beams. The echosounder was programmed to emit an acoustic signal every 10 seconds and has been collecting acoustic data since early September 2019, providing us with more than a year’s worth of acoustic data to process and analyze (Figure 3). The acoustic signal emitted by the echosounder interacts with scatterers in the water column, mostly methane gas bubbles in our case, and measures the target strength of these scatterers. The target strength represents how strong a scatterer scatters sound back towards the echosounder (for more information of acoustics and gas bubbles, see article by Weber, 2016).

Figure 3. Acoustic observations of hydrocarbon activity (ranges between 10-140 m) west of Platform Holly as a function of range from the echosounder and time. Warm and cool colors represent high and low target strength, which correspond, roughly, to high and low seepage activity, respectively.

The acoustic measurements, shown in Figure 3, indicate that there are temporal changes in the location and the target strength of the hydrocarbons in the region; however, it does not tell us how the amount of gas flow of these hydrocarbons is changing with time. Exploiting the split-beam capability of the echosounder, allowed us to track the position of scatterers in the acoustic data, so we can identify and classify different hydrocarbon structure types (Figure 4) and use the appropriate mathematical equations to convert acoustic measurements into gas flow. This will allow us to track changes in gas flow of hydrocarbons near Platform Holly and learn more about how gas flow is affected by external processing, like tides, storms, and earthquakes.

Figure 4. a) Acoustic observations of hydrocarbon activity. b) Acoustic classification map of different hydrocarbon structure types.

John P. Ryan – ryjo@mbari.org
Monterey Bay Aquarium Research Institute
7700 Sandholdt Road
Moss Landing, CA 95039

John E. Joseph – jejoseph@nps.edu
Tetyana Margolina – tmargoli@nps.edu
Department of Oceanography
Naval Postgraduate School
Monterey, CA 93943

Leila T. Hatch – leila.hatch@noaa.gov
Stellwagen Bank National Marine Sanctuary, NOS-NOAA
175 Edward Foster Road
Scituate, MA 02066

Andrew DeVogelaere – andrew.devogelaere@noaa.gov
Monterey Bay National Marine Sanctuary, NOS-NOAA
99 Pacific Street, Bldg. 455A
Monterey, CA  93940

Lindsey E. Peavey Reeves – lindsey.peavey@noaa.gov
NOAA Office of National Marine Sanctuaries
National Marine Sanctuary Foundation
Silver Spring, MD 20910
and
Channel Islands National Marine Sanctuary
University of California, Santa Barbara
Santa Barbara, CA  93106

Brandon L. Southall – brandon.southall@sea-inc.net
Southall Environmental Associates, Inc.
9099 Soquel Drive, Suite 8
Aptos, CA 95003

Simone Baumann-Pickering – sbaumann@ucsd.edu
Scripps Institution of Oceanography, UC San Diego
Ritter Hall 200F
La Jolla, CA 92093

Alison K. Stimpert – astimpert@mlml.calstate.edu
Moss Landing Marine Laboratories
Moss Landing, CA, 95039

Popular version of paper 4pAO1
Presented Thursday afternoon, December 10, 2020
179^th ASA Meeting, Acoustics Virtually Everywhere

Imagine speaking with only your voice – no technology – and being heard by someone over a hundred kilometers away.  Because sound travels much more powerfully in water than it does in air, great whales can communicate over such vast distances in the ocean.

Whales and other oceanic animals produce and perceive sound for essential life activities – communicating, finding food, navigating, reproducing, and surviving.  This means that we can learn a lot about their underwater lives by recording and analyzing the sounds they produce and hear.  It also means that the noise we introduce into the ocean can cause harm.  Protecting oceanic species and their habitats requires an understanding of the detrimental impacts of our noise and strategies to mitigate these impacts.

There are many sources of anthropogenic noise in the ocean, but the most pervasive and persistent source is that of vessels, notably large commercial ships engaged in global trade.  This worldwide bustling is among the many human activities influenced by the COVID-19 pandemic.  Using sound recordings from the deep sea and information about vessel traffic, we examined oceanic quieting caused by reduced shipping traffic within Monterey Bay National Marine Sanctuary (Figure 1) during this ongoing pandemic.

Figure 1.  Study context.  Shaded regions represent Monterey Bay National Marine Sanctuary.  The black circle shows the location of a deep-sea (890 m) observatory connected to shore by a cable, through which we recorded sound.  Red and blue lines define nearby shipping lanes.

Our first question was whether the quieting we measured during 2020 could be explained by reduced traffic of large vessels.  We quantified vessel traffic using two independent data sources: (1) economic data representing vessel activity across all California ports, and (2) location data sent from vessels to shore continuously as they transit between ports.  Both of these data sources yielded the same answer: quieting within the sanctuary during January–June 2020 was caused by reduced shipping traffic.  Further, a rebound in noise levels during July 2020 was associated with an increase in vessel traffic.

Our second question was how much quieter 2020 was compared to previous years.  Using the previous two years as a baseline, we found that 2020 was quieter than both previous years during the months of February through June.  Low-frequency noise levels during June 2020, the quietest month having the least shipping activity, were reduced by nearly half compared to June of the previous two years.  For baleen whales that use low-frequency sound to communicate, potential consequences of this quieting include less time exposed to noise-induced interference and stress, and greater distance over which communication can occur.

The effects of this pandemic on oceanic noise will differ from place to place, depending on proximity to hubs of maritime activity, the nature of noise produced by each activity, and the degree and timing of pandemic influence.  These changes are being examined across U.S. National Marine Sanctuaries and all around the world.  The COVID-19 pandemic resulted in an unexpected global experiment in oceanic noise, one that could reveal better ways to care for ocean health and its powerful support of humanity.

Study overview

Gabriel R. Venegas – gvenegas@arlut.utexas.edu

Applied Research Laboratories, The University of Texas at Austin
10000 Burnet Rd
Austin, TX 78758

Popular version of paper 3pID3
Presented Wednesday afternoon, December 4, 2019. 1:45pm-2:05pm
178th ASA Meeting, San Diego, CA

Sound is an effective way to study the ocean by non-invasively and quickly surveying large areas, and acoustical oceanography has lent an extra pair of ears to help scientists monitor climate change. This talk will showcase the work of some of the many acoustical oceanographers in the Acoustical Society of America (ASA) that have made valuable contributions to aid in climate change related monitoring, in the hope of inspiring other members to think of new potential acoustic monitoring applications.

Heat
The planet is warming and so are its oceans. This warming causes the seawater to expand and large volumes of ice to break off from glaciers and melt in the ocean, ultimately resulting in sea level rise. An acoustic technique called passive acoustic thermometry^1,2 takes the noise created by these calving events at the north and south poles to calculate the speed of sound averaged over path lengths as long as 132 km. Temperature can then be inferred from sound speed using a well-established formula relating the two quantities.

As the glaciers melt, they release tiny compressed air bubbles that make loud popping sounds underwater.³ If these popping sounds can be reasonably characterized at one or many glacial bays, at a safe distance, these sounds can be used to estimate the glacial melt rate.^4,5

An increase in ocean temperature also causes methane hydrate, a material in ocean sediments that can store large amounts of methane, to turn from solid to greenhouse gas, which bubbles up from the seafloor and is ultimately released into the atmosphere. The sound of these bubbles has also been exploited to estimate the volume of methane released from hydrates and seeps.^6–8

CO₂
Global CO₂ concentrations are higher than they have been over the last 800,000.⁹ A quarter of this gas is absorbed into the ocean and has caused the what is thought to be the fastest increase in ocean acidity in the last 60 million years.¹⁰ An increase in ocean temperature, actually decreases the ocean’s capacity to store CO₂, causing it to be released back into the atmosphere. The relationship between ocean acidity and the absorption of sound is well understood. A passive acoustic technique using the sound of wind over the water is being investigated to estimate the absorption and thus ocean acidity.¹¹

Ocean acidity also causes damage to many coastal ecosystems including valuable “blue carbon” stores such as mangroves, salt marshes, and seagrasses, which store 50% of the ocean’s organic carbon.¹² The destruction of these carbon stores can also release CO₂ back into the atmosphere. An ultrasonic sensor that will improve organic carbon estimates in these ecosystems is currently under development.¹³ These climate-altering feedback loops can cause rapid and catastrophic consequences for future generations, and should be the responsibility of all scientists, elected officials, and the general public, alike

References

Gaëtan Richard – gaetan.richard@ensta-bretagne.fr
Flore Samaran – flore.samaran@ensta-bretagne.fr
ENSTA Bretagne, Lab-STICC UMR 6285
2 rue François Verny
29806 Brest Cedex 9, France

Julien Bonnel –  jbonnel@whoi.edu
Woods Hole Oceanographic Institution
266 Woods Hole Rd
Woods Hole, MA 02543-1050, USA

Christophe Guinet – christophe.guinet@cebc-cnrs.fr
Centre d’Études Biologiques de Chizé, UMR 7372 – CNRS & Université de La Rochelle,
79360 Villiers-en-Bois, France

Popular version of paper
Presented Wednesday afternoon, December 4, 2019
178th ASA Meeting, San Diego, CA

Toothed whales feeding on fish caught on longlines is a growing issue worldwide. This issue named depredation has a serious socio-economic impact and raise conservation questions. Costs for fishermen include damages to the fishing gear and increased fishing effort to complete quotas. For marine predators, depredation increases risks of mortality (lethal retaliation from fishers or bycatch on the gear) and behavior changes, with a loss of natural foraging behavior for an easy human-related food source. Most of studies assessing depredation by odontocetes on longline fisheries have primarily relied on surface observation performed from the fishing vessels during the hauling phase (i.e. when gears are retrieved on board). The way odontocetes interact with longlines underwater thus remains poorly known. In particular, depredation by odontocetes on demersal longlines (i.e. lines that are set on the seafloor) has always been considered to occur only during hauling phases, when the fish are pulled up from the bottom to the predators at the surface.

Figure 1

In our study, we focused on the depredation by killer whales on a demersal longline fisheries around Crozet Archipelago (Southern Ocean, Figure 1). Here, we aimed at understanding how, when and where interactions really occur. Recent studies revealed that killer whales could dive up to 1000 m, suggesting that they can actually depredate on longlines that are set on seafloor (remember that the traditional hypothesis was that depredation occurs only during hauling, i.e. close from the sea surface when the lines are brought back to the ship). In order to observe what can’t be seen, we used hydrophones to record sounds of killer whales, fixed on the fishing gears (Figure 2). This species is known to produce vocalization to communicate but also echolocation clicks as a sonar to estimate the direction and the range of an object or a prey (Figure 3). Altogether, communication and echolocation sounds can be used as clues of both presence and behaviour of these toothed whales. Additionally, as killer whales also sense the environment by listening to ambient sounds, we recorded the sounds produced by the fishing vessels, in order to understand more how these predators can detect and localize the fishing activities.

Figure 2. Scheme of fishing phases (setting, soaking and hauling) with the hydrophone deployed on a longline.

Figure 3. Spectrogram of killer whales’ sounds recorded around a fishing gear. This figure is a visual representation of the variation of intensities (color palette) of frequencies of sounds as they vary with time. On the recording we observe both calls (communication sounds) and clicks of echolocation, which can be heard as ‘buzzes’ when the emission rate is too fast to dissociate each click. Click image to listen.

Our main result is that killer whales were present and probably looking for food (production of echolocation clicks) around the longline equipped with the hydrophone while the boat was not hauling or too far to be interacting with the whales. This observation strongly suggest that depredation occurs on soaking longlines, which contradict the historical hypothesis that depredation only occurs during the hauling phases when the behavior is most easily observed from the fishing vessels. However, this new results raises the question on how killer whales know where to find the longlines in the ocean immensity. However, we also observed that the fishing vessels produced different sounds between the setting of longlines and their hauling (Figure 4). We therefore hypothesize that killer whales are able to recognize and to localize the vessel activity using the ship noise, allowing them to find the longlines.

Figure 4. Spectrograms of a fishing vessel setting a longline (left panel) and maneuvering during hauling (right panel). On the first spectrogram, we observed a difference of sound intensity between the setting (until 38 s) and the post setting, while the vessel was still moving forward (after 38 s). On the second spectrogram we recorded a vessel going backwards while hauling the longline, such maneuver characterize the activity and increase the range that killer whale can detect the fishing vessel.

Chen-Fen Huang – chenfen@ntu.edu.tw
KuangYu Chen – seven5172002@gmail.com
IO.NTU – Acoustic Oceanography Lab

Sheng-Wei Huang – swhuang1983@ntu.edu.tw
JenHwa Guo – jguo@ntu.edu.tw
ESOE.NTU – Underwater Vehicles Lab
Taipei, 10617, Taiwan, R.O.C.

Popular version of paper 5pAOb1, “Acoustic mapping of ocean currents using moving vehicles”
Presented Friday afternoon, November 9, 2018, 1:00 PM – 1:20 PM, Balcony L
176th ASA Meeting, Victoria, BC Canada

With the increased availability of highly maneuverable unmanned vehicles, abundant ocean environmental data can be collected.  Among the various ways of collecting the ocean temperature and current data, ocean acoustic tomography (OAT) is probably the most efficient method to obtain a comprehensive view of those properties in the interior ocean.

OAT uses differential travel times (DTTs) to estimate the currents.  Imagine two transceivers are separated by a distance R in a moving medium with sound speed of c.  The sound transmitted from the transceiver upstream will travel faster than the sound from the transceiver downstream.  By measuring the sound traveling in both directions, we can obtain the DTTs and from the DTTs we can determine the path-averaged current between the transceivers.

What happens if the vehicles carrying the transceivers are moving?  First, the DTTs are affected. The magnitude of the DTTs is reduced by the average speed of the vehicles [1].  Second, the acoustic signals are Doppler distorted due to the relative motion between the moving vehicles.

To determine the Doppler shift, we correlated the transmitted signals of different hypothetical Doppler shifts (replicas) with the received signals.  The hypothetical Doppler shift yielding the maximum correlation is used to compensate the acoustic measurements and determine the acoustic arrival patterns.

The Doppler shift measures the relative speed between two vehicles; however, relative speed isn’t sufficient to determine the ocean current speed – absolute speed (projected onto the path connecting the two vehicles) is required.  If only one of the vehicles is moving, then the Doppler shift indicates the projected speed of the moving vehicle.  If both of the vehicles are moving, we determine their average speeds by measuring the ground speed of at least one of the mobile vehicles.

We determined the DTTs using the correlation-based method.  The time series of the acoustic arrivals received at each pair of transceivers (reciprocal arrival patterns) are correlated to obtain the cross-correlated function (CCF).  We selected the lag time corresponding to the maximum peak in the CCF as an average estimate of the DTT.

We conducted a moving-vehicles experiment using two moving vehicles (auv and ship) and one moored station (buoy) in WangHiXiang Bay nearby Keelung City, Taiwan.  The AUV sailed near the shore while the ship surveyed in counterclockwise direction along a square trajectory. We installed the tomographic transceivers on the moving vehicles and the moored station. A DVL was on the ship for the validation of our current estimate.  Taken together, the moving vehicles and the moored station construct a triangular formation which can be used to map the ocean currents.

We used the distributed sensing method [2] to obtain the current field.  The estimated current velocities near the ship show consistency with the point measurements from the DVL.  We reconstructed the current distribution in the Bay using the acoustic data (the path-averaged currents) collected over the last 20 minutes.  A small-scale eddy was revealed.

Figure 1. Illustration of the acoustic mapping of ocean currents. Estimation of the current velocities near the ship for a) eastward direction and b) northward direction. The red circle and line indicate the DVL measurement while the black color indicates the DTT estimate. c) Spatial distribution of the estimated current field (yellow arrows) using the acoustic transmission paths indicated by the white lines.

[1] W. Munk, P. F. Worcester, and C. Wunsch, Ocean Acoustic Tomography, Cambridge University Press, 1995.

[2] C.-F. Huang, T. C. Yang, J.-Y. Liu, and J. Schindall, “Acoustic mapping of ocean currents using networked distributed sensors,” J. Acoust. Soc. Am., vol. 134, pp. 2090–2105, 2013.