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.

David R. Barclay¹– dbarclay@dal.ca
Dieter A. Bevans² – dbevans@ucsd.edu
Michael J. Buckingham² – mbuckingham@ucsd.edu

1. Department of Oceanography, Dalhousie University, 1355 Oxford St, Halifax, Nova Scotia, B3H 4R2, CANADA
2. Marine Physical Lab, Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive, #0238, La Jolla CA, 92093-0238

Popular version of paper 5pAOa7: “Estimating muddy seabed properties using ambient noise coherence”
Presented Friday afternoon, November 9th, 2018, 3:00 – 3:15PM, Esquimalt Room 176th ASA Meeting, Victoria, B.C.

Figure 1. The autonomous Deep Sound acoustic recorder on the rear deck of the R/V Neil Armstrong

The ocean is a natural acoustic waveguide, bounded by the sea surface and seabed, inside which sound can travel large distances. In the frequency range of 10’s – 1000’s of Hertz, seawater is nearly transparent to sound, absorbing only a small fraction of energy of the acoustic wave as it propagates in the ocean. However, sound transmitted in this shallow water ocean waveguide reflects off the bottom, losing some energy which is either transmitted into the bottom, or absorbed by the sediment. In order to predict and model the distances over which any acoustic ocean monitoring, detection, or communication systems may operate, accurate knowledge of the acoustic properties (the sound speed, attenuation, and density) of the seabed must be known.

The majority of the ocean’s bottom has a top layer of sand or gravel, where the grain sizes are large enough that gravity and friction dictate the micro-physics at inter-granular contacts and play a large roll in determining the sound speed and attenuation in the material. In silts and clays (a.k.a. muds), grain sizes are on the order of microns or less, so electrochemical forces become the dominant factor responsible for the mechanics of the medium. Mud particles are usually elongated, with high length-to-width ratios, and when consolidated they form stacks of parallel grains and ‘card-house’ structures, giving the ensemble mechanical and acoustical properties unlike larger grained sands.

In March and April 2017, as part of the ONR-supported Seabed Characterization Experiment (SCE) designed to investigate the geo-acoustic properties of fine-grained sediments, a bottom lander known as Deep Sound was deployed on the New England Mud Patch (NEMP) from the R/V Neil Armstrong. The NEMP occupies an area of approximately 13,000 km2 off the east coast of the USA, 95 km south of Martha’s Vineyard, and is 170 km wide, descending 75 km across the continental shelf with an unusually smooth bathymetry. The region is characterized by a layer of mud, accumulated over the last 10,000 years, estimated to be as thick as 13 meters [1].

Figure 2. Location of the five Deep Sound deployments, plotted over the two-way travel time, a proxy for mud layer thickness (where 16 milliseconds is equivalent to 13 meters)

Drop #1

Drop #2

Drop #3

Drop #4

Drop #5

Two way travel time [ms]

The American naturalist Louis François de Pourtalès first described this ocean feature in 1872 [2], in the context of a convenient navigation aid for whaling ships headed into Nantucket and New Bedford. Sailors would make depth soundings using a lead weight with a plug of wax on the bottom which collected a small sample of the seabed. Since the mud bottom was unique along the New England seaboard, ships were able to determine their location in relation to their home ports in foggy weather.

Deep Sound (Fig. 1) is a free-falling (untethered), four channel acoustic recorder designed to descend from the ocean’s surface to a pre-assigned depth, or until pre-assigned conditions are met, at which point it drops an iron weight and returns to the surface under its own buoyancy with a speed of ~0.5 m/s in either direction. In this case, the instrument was configured to land on the seabed, with the hydrophones arranged in an inverted ‘T’ shape, and continue recording until either a timer expired, or a battery charge threshold was crossed. Almost 30 hours of ambient noise data were collected at five locations on the NEMP, shown in Fig. 2.

From the vertical coherence of the ambient noise, information about the geo-acoustic properties of the seabed was extracted by fitting the data to a model of ocean noise, based on an infinite sheet of sources, representing the bubbles generated by breaking ocean surface waves. The inversion returned estimates of five geo-acoustic properties of the bottom: the sound speed and attenuation, the shear-wave speed and attenuation, and the density of the muddy seabed.

1. Bothner, M. H., Spiker, E. C., Johnson, P. P., Rendigs, R. R., Aruscavage, P. J. (1981). Geochemical evidence for modern sediment accumulation on the continental shelf off southern New England. Journal of Sedimentary Research, 51(1), pp. 281-292.
2. Pourtales, L.F., (1872). The characteristics of the Atlantic sea bottom off the coast of the United States: Report, Superintendent U.S. Coast Survey for 1869, Appendix 11, pp. 220-225.
3. Carbone, N. M., Deane, G. B., Buckingham, M. J., (1998). Estimating the compressional and shear wave speeds of a shallow water seabed from the vertical coherence of ambient noise in the water column. The Journal of the Acoustical Society of America, 103(2), pp. 801-813.

Christine Erbe – Curtin University, c.erbe@curtin.edu.au
Miles Parsons – Curtin University and Australian Institute of Marine Science, m.parsons@aims.gov.au
Alec Duncan – Curtin University, A.J.Duncan@curtin.edu.au
Klaus Lucke – Curtin University and JASCO Applied Sciences, Klaus.lucke@jasco.com
Alexander Gavrilov – Curtin University, A.Gavrilov@curtin.edu.au
Kim Allen – THHINK Autonomous Systems, kim.allen@thhink.com

Centre for Marine Science & Technology, Curtin University, Bentley, 6102 Western Australia, AUSTRALIA|

Popular version of paper 1aAO5
Presented Monday morning, May 7, 2018, 11:10-11:25 a.m., GREENWAY A
175^th ASA Meeting, Minneapolis, MN

Underwater sound contains a lot of information about the source that produces it. Ships, for example, have a characteristic sound signature underwater, by which the type of vessel, its speed, and its route can easily be determined. In some cases, individual vessels can be identified by their sound and information about the type of propulsion, operational mode, and load can be deduced and maintenance issues (e.g., relating to the propeller) can be picked out. Similarly, just by listening, we can study marine life from whales to fishes and shrimp; we can track their movements; monitor their behavior; and in the case of some species of dolphins, even say which family and individuals are there. Sound is an important commodity for marine life; marine mammals as well as fishes, for example, communicate through sound, sense their environment, navigate, and forage—all mediated by sound.

Video 1: Underwater video and sound recording of different water sports activities.

Given the important role sound plays in the life functions of marine fauna, the potential interference by man-made noise has received growing interest. Noise may disrupt animal behavior, affect their hearing abilities, mask communication, cause stress, and in extreme cases cause physical and physiological damage that can ultimately be fatal. The research and management focus has—quite sensibly—been on the strongest sources, such as geophysical surveys or coastal and marine construction. Non-motorised activities are expected quieter and have hardly been studied.

Within the framework of an underwater acoustic project, we had the opportunity to record ourselves and friends performing a number of recreational water sports activities in a quiet Olympic pool, with all surrounding machinery (including cleaning pumps) switched off [1,2]. Specifically, different people were filmed and acoustically recorded while swimming breaststroke, backstroke, freestyle, and butterfly; snorkeling with and without fins; paddling a surfboard with alternating single or double arms; scuba diving; kayaking; and jumping into the pool. Sound pressure and water particle velocity were measured.

Activities that occurred at the surface, involved repeatedly piercing the surface and hence created bubble clouds were the strongest sound generators. Received levels were 110-131 dB re 1 µPa (10-16,000 Hz) for all of the activities at the closest point of approach (1 m). Levels were lower than those found in environmental noise regulations, but were clearly above ambient noise levels recorded off beaches and hence predicted audible by marine fauna over tens to hundreds of meters.

The characterization and quantification of underwater sound from recreational water sports has applicability well beyond environmental management. For example, just by listening to the recordings, it is easy to identify who of the volunteers was in the pool and which activity (including which style of swimming, with or without fins, with single versus double arms, etc.) was performed. The better (i.e., faster and smoother) swimmers were the quieter swimmers. Underwater sound might be a useful tool to assess professional or competitive swimmer performance and can be used for security monitoring of pools.

[1] C. Erbe, M. Parsons, A. J. Duncan, K. Lucke, A. Gavrilov and K. Allen, “Underwater particle motion (acceleration, velocity and displacement) from recreational swimmers, divers, surfers and kayakers,” Acoustics Australia 45,  293-299 (2017). doi: 10.1007/s40857-017-0107-6

[2] C. Erbe, M. Parsons, A. J. Duncan and K. Allen, “Underwater acoustic signatures of recreational swimmers, divers, surfers and kayakers,” Acoustics Australia 44 (2),  333-341 (2016). doi: 10.1007/s40857-016-0062-7

Scott Loranger – sloranger@ccom.unh.edu
Department of Earth Science
University of New Hampshire
Durham, NH, United States

Christopher Bassett – chris.bassett@noaa.gov
Alaska Fisheries Science Center
National Marine Fisheries Service
Seattle, WA, United States

Justin P. Cole – jpq68@wildcats.unh.edu
Department of Chemistry
Colorado State University
Fort Collins, CO, United States

Thomas C. Weber – Weber@ccom.unh.edu
Department of Mechanical Engineering
University of New Hampshire
Durham, NH, United States

Popular version of paper 1pAO9, “The Acoustic Properties of Three Crude Oils at Oceanographically Relevant Temperatures and Pressures”
Presented Monday afternoon, December 04, 2017, 3:35-3:50 PM, Balcony M
174th ASA Meeting, New Orleans, LA
Click here to read the abstract

The difficulty of detecting and quantifying oil in the ocean has limited our understanding of the fate and environmental impact of oil from natural seeps and man-made spills. Oil on the surface can be detected by satellite (figure 1) and studied with optical instrumentation, however, as researchers look deeper to study oil as it rises through the water column, the rapid attenuation of light in the ocean limits the usefulness of these systems. Active sonar – where an acoustic transmitter generates a pulse of sound and a receiver listens for the sound reflected from an object – takes advantage of the low attenuation of sound in the ocean to detect things farther away than optical instruments. However, oil is difficult to detect acoustically because oil and seawater have similar physical properties. The amount of sound reflected from an object is dependent to the object’s size, shape and a physical property called the acoustic impedance – the product of the density and sound speed of the material being measured. When an object has an acoustic impedance similar to the medium that surrounds it, the object reflects relatively little sound. The acoustic impedance of oil (which differs by type of oil) and sea water is often very similar. In fact, under certain conditions oil droplets could be acoustically invisible. To study oil acoustically, we need to better understand the physical properties that affect its acoustic impedance.

Most measurements of the density and sound speed of oil come from oil exploration research which focuses on studying oil under reservoir conditions – high temperatures and pressures associated with oil deep underground.  As oil cools to oceanographically relevant temperatures it can transition from a liquid to a waxy semisolid. This transition may result in significant changes to the acoustic properties of oil which would not be predicted by measurements made at reservoir conditions. To inform models of acoustic scattering from oil and produce quantitatively meaningful measurements it is necessary to have well-understood properties at relevant temperatures and pressures. Density and sound speed can be measured directly, while the shape of an oil droplet can be predicted from the density and viscosity. Density and viscosity will tell you how quickly a droplet will rise, and how the drag force of the surrounding water will modify its shape. Droplets can range from spheres to more pancake like shapes that one could produce by pushing down on an inflated balloon.

To better understand these important properties, we obtained samples of three different crude oils. Each sample was sent for “fingerprinting” to identify differences in the molecular composition of the oils. “Fingerprinting” is a technique used by oil exploration scientists and spill responders to identify different crude oils. Measurements of the sound speed, density, and viscosity were made from -10°C (14°F) to 30°C (86°F). A sound speed chamber was specifically designed to measure sound speed at the same temperature range but with the added effects of pressure (0 to 2500 psi – equivalent to approximately 1700 m depth, deeper than the Deepwater Horizon well).

Light, medium and heavy crude oil was tested. Each of these is typically defined by their American Petroleum Institute (API) gravity. API gravity is a common descriptor of oils and is a measure of the density of oil relative to water. The properties of the medium and heavy crude oil are in the figure (2) below. The sound speed is different both in amplitude and shape, while the viscosity only differs in amplitude, suggesting that the changes to shape of the sound speed curve may not be related to the viscosity. The heavy oil is currently limited to measurements above 5°C because below that temperature it becomes very difficult to transfer sound through the oil. Part of this ongoing research is to develop new techniques to measure sound speed, and to use these techniques to extend our measurements of heavy oils to cold temperatures similar to those found in Arctic regions where oil can be trapped in ice. By better understanding these physical properties of oil, the methods and models used to detect and quantify oil in the marine environment can be improved.

Figure 1: Satellite image of surface oil slicks from natural seeps.

Figure 2: Experimental measurements of the physical properties of a Medium and Heavy crude oil.