Beach sand ripples can be fingerprints for ancient weather conditions

When a coastal tide rolls out, it can reveal beautiful ripples in the temporarily exposed sand. These same undulating patterns can also be seen in ancient, petrified seabeds that have been exposed in various parts of the world and preserved for millions or even billions of years.

Geologists look to ancient sand ripples for clues to the environmental conditions in which they formed. For instance, the spacing between ripples is proportional to the depth of the water and the size of the waves that molded the underlying ripples.

But sand ripples aren’t always perfectly parallel, carbon-copies of each other, and can display various kinks and sworls. Can these more subtle, seemingly random deviations or defects tell us something about the conditions in which a sandy seabed formed?

The answer, according to researchers from MIT and elsewhere, is yes. In a paper published online and appearing in the Oct. 1 issue of Geology, the team reports that some common defects found in both ancient and modern seabeds are associated with certain wave conditions. In particular, their findings suggest that ripple defects resembling hourglasses, zigzags, and tuning forks were likely shaped in periods of environmental flux — for instance, during strong storms, or significant changes in tidal flows.

“The type of defect you see in ripples could tell you about how dramatic the shifts in weather conditions were at the time,” says Taylor Perron, associate professor of geology and associate head of MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS). “We can use these defects as fingerprints to tell not just what the average conditions were in the past, but how things were changing.”

Ripple defects in ancient sandbeds may also influence how fluids flow through sedimentary rocks, including underground reservoirs that hold water, oil and gas, or even stored carbon dioxide, according to Perron.

In addition, he says, ripple patterns in modern sand act to roughen the seabed, slowing down ocean currents near the shore. Knowing how ripples change in response to shifting waves and tides may therefore help predict coastal erosion and flooding.

Perron’s co-authors are on the paper are former MIT graduate student Kimberly Huppert ’11, PhD ’17, former undergraduate and current postdoc Abigail Koss ’12, Paul Myrow of Colorado College, and former undergraduate Andrew Wickert ’08 of the University of Minnesota.

Wrinkles preserved

The team began looking into the significance of ripple defects several years ago, when Myrow, who at the time was spending his sabbatical at MIT, showed Perron some photos that he had taken of sedimentary rocks etched with ripples and grooves. The rocks were, in fact, ancient sandbeds that were hundreds of millions of years old.

Wave-sculpted ripples form as waves travel across the surface of a body of liquid. These waves cause water beneath the surface to circle around and around, generating oscillating flows that pick up sand grains and set them down in a process that eventually carves out troughs and grooves throughout the sandbed.

But how could such delicate patterns be preserved for millions of years? Perron says that various processes could essentially set ripples in place. For instance, if the water level suddenly dropped, it could leave a sand bed’s ripples exposed to the air, drying them out and hardening them to some extent, so that they retained their patterns even as more sediment slowly layered itself on top of them over billions of years.

Similarly, if a finer sediment like mud or silt covers a sand bed, such as after a large storm, these sediments could blanket the existing ripples. As Perron explains, this would essentially “armor them, keeping the waves from eroding the ripples before more sediment buries them.” Over time, the sediments turn into rock as they are buried deep below Earth’s surface. Later, the rock overlaying the ripples can naturally erode away, exposing the preserved ripples at the surface again.

In looking through photos of sand ripples, Perron and Myrow noticed small defects resembling tuning forks, zigzags, and hourglasses, across both ancient and modern sandbeds.

“People have noticed these defects before, but we wondered, are they just random, or do they actually contain some information?” Perron says.

Paddling through waves

The researchers set out to study the various wave conditions that generate certain ripple patterns and defects. To do this, they built an acrylic wave tank measuring 60 centimers wide, 50 centimers deep, and 7 meters long. At one end of the tank, they attached a motor-driven paddle, which swished back and forth to generate waves that traveled across the tank.

At the other end of the tank, they erected an artificial sloping “beach” covered in a polymer mesh. This setup served to minimize any wave reflections: As a wave crashed onto the artificial beach, the energy dissipated within the mesh instead of splashing back and influencing oncoming waves.

The team filled the tank with a 5-centimeter-thick bed of fine sand and enough water to reach 40 centimeters in depth. For each experiment, they set the paddle to swish back and forth at a constant distance, and recorded the sand bed as ripples formed. At a certain point, they observed that the ripples — and in particular, the spacing between the ripples — reaches a stable, consistent pattern. They recorded this spacing, along with the speed and amplitude of the paddle, and then, over 32 experimental runs, either increased or decreased the paddle’s motion, causing the ripples to morph again to either a wider or narrower spacing.

Interestingly, they found that, in the process of adjusting to a new spacing, ripples formed intermediary defects resembling zigzags, hourglasses, and tuning forks, depending on the wave conditions set by the tank’s paddle.

As the researchers shortened the paddle’s back-and-forth motion, this created shorter waves, narrower ripples, and patterns that resembled hourglasses. If the paddle’s motion was shortened even further — creating faster, shorter waves — a pattern of “secondary crests,” in which existing ripples appeared to form temporary “shadow” ripples on either side, took over. When the researchers widened the paddle’s motion, generating longer waves, the ripples formed zigzag patterns as they shifted to a wider spacing.

“If you see these types of defects in nature, we argue that the seabed was undergoing some kind of change in weather conditions, tides, or something else that affected water depth or waves, probably over the course of hours or days,” Perron says. “For instance, if you’re seeing lots of secondary crests, you can tell there was a pretty big change in the waves as opposed to a smaller change, which might give you hourglasses instead.”

The researchers observed that in all scenarios, patterns resembling tuning forks cropped up, even after ripples had reached a new, stable spacing.

“These tuning forks tend to stick around for a long time,” Perron says. “If you see these in modern or ancient rock, they suggest a seabed experienced a change, but then the conditions remained steady, and the bed had a long time to adjust.”

Going forward, Perron says geologists can use the team’s results as a blueprint to connect certain ripple defects with the water conditions that may have created them, in both the modern environment and in the ancient past.  

“We think these small defects can tell you a lot more about an ancient environment than just what the average size of the waves and water depth was,” Perron says. “They could tell you if it was an environment that had tides that were large enough to change ripples by this much, or if a place was experiencing periodic storms, even billions of years ago. And if we find ancient wave ripples on Mars, we’ll know how to read them.”

This research was supported, in part, by the National Science Foundation.

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Advancing undersea optical communications

Nearly five years ago, NASA and Lincoln Laboratory made history when the Lunar Laser Communication Demonstration (LLCD) used a pulsed laser beam to transmit data from a satellite orbiting the moon to Earth — more than 239,000 miles — at a record-breaking download speed of 622 megabits per second.

Now, researchers at Lincoln Laboratory are aiming to once again break new ground by applying the laser beam technology used in LLCD to underwater communications.

“Both our undersea effort and LLCD take advantage of very narrow laser beams to deliver the necessary energy to the partner terminal for high-rate communication,” says Stephen Conrad, a staff member in the Control and Autonomous Systems Engineering Group, who developed the pointing, acquisition, and tracking (PAT) algorithm for LLCD. “In regard to using narrow-beam technology, there is a great deal of similarity between the undersea effort and LLCD.”

However, undersea laser communication (lasercom) presents its own set of challenges. In the ocean, laser beams are hampered by significant absorption and scattering, which restrict both the distance the beam can travel and the data signaling rate. To address these problems, the Laboratory is developing narrow-beam optical communications that use a beam from one underwater vehicle pointed precisely at the receive terminal of a second underwater vehicle.

This technique contrasts with the more common undersea communication approach that sends the transmit beam over a wide angle but reduces the achievable range and data rate. “By demonstrating that we can successfully acquire and track narrow optical beams between two mobile vehicles, we have taken an important step toward proving the feasibility of the laboratory’s approach to achieving undersea communication that is 10,000 times more efficient than other modern approaches,” says Scott Hamilton, leader of the Optical Communications Technology Group, which is directing this R&D into undersea communication.

Most above-ground autonomous systems rely on the use of GPS for positioning and timing data; however, because GPS signals do not penetrate the surface of water, submerged vehicles must find other ways to obtain these important data. “Underwater vehicles rely on large, costly inertial navigation systems, which combine accelerometer, gyroscope, and compass data, as well as other data streams when available, to calculate position,” says Thomas Howe of the research team. “The position calculation is noise sensitive and can quickly accumulate errors of hundreds of meters when a vehicle is submerged for significant periods of time.”

This positional uncertainty can make it difficult for an undersea terminal to locate and establish a link with incoming narrow optical beams. For this reason, "We implemented an acquisition scanning function that is used to quickly translate the beam over the uncertain region so that the companion terminal is able to detect the beam and actively lock on to keep it centered on the lasercom terminal’s acquisition and communications detector," researcher Nicolas Hardy explains. Using this methodology, two vehicles can locate, track, and effectively establish a link, despite the independent movement of each vehicle underwater.

Once the two lasercom terminals have locked onto each other and are communicating, the relative position between the two vehicles can be determined very precisely by using wide bandwidth signaling features in the communications waveform. With this method, the relative bearing and range between vehicles can be known precisely, to within a few centimeters, explains Howe, who worked on the undersea vehicles’ controls.

To test their underwater optical communications capability, six members of the team recently completed a demonstration of precision beam pointing and fast acquisition between two moving vehicles in the Boston Sports Club pool in Lexington, Massachusetts. Their tests proved that two underwater vehicles could search for and locate each other in the pool within one second. Once linked, the vehicles could potentially use their established link to transmit hundreds of gigabytes of data in one session.

This summer, the team is traveling to regional field sites to demonstrate this new optical communications capability to U.S. Navy stakeholders. One demonstration will involve underwater communications between two vehicles in an ocean environment — similar to prior testing that the Laboratory undertook at the Naval Undersea Warfare Center in Newport, Rhode Island, in 2016. The team is planning a second exercise to demonstrate communications from above the surface of the water to an underwater vehicle — a proposition that has previously proven to be nearly impossible.

The undersea communication effort could tap into innovative work conducted by other groups at the laboratory. For example, integrated blue-green optoelectronic technologies, including gallium nitride laser arrays and silicon Geiger-mode avalanche photodiode array technologies, could lead to lower size, weight, and power terminal implementation and enhanced communication functionality.

In addition, the ability to move data at megabit-to gigabit-per-second transfer rates over distances that vary from tens of meters in turbid waters to hundreds of meters in clear ocean waters will enable undersea system applications that the laboratory is exploring.

Howe, who has done a significant amount of work with underwater vehicles, both before and after coming to the laboratory, says the team’s work could transform undersea communications and operations. “High-rate, reliable communications could completely change underwater vehicle operations and take a lot of the uncertainty and stress out of the current operation methods."

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Collaboration to expand the study of microbial oceanography

Microbes sustain all of Earth’s habitats, including its largest biome, the global ocean. Microbes in the sea capture solar energy, catalyze biogeochemical transformations of important elements, produce and consume greenhouse gases, and fuel the marine food web. Measuring and modeling the distribution, composition, and function of microbial communities, and their interactions with the environment, are key to understanding these fundamental processes in the ocean.

The Simons Foundation, which provides generous funding for several lines of research within MIT's Department of Earth, Atmospheric and Planetary Sciences, recently extended its support for microbial oceanography with the establishment of the Simons Foundation Collaboration on Ocean Computational Biogeochemical Modeling of Marine Ecosystems (CBIOMES). Led by MIT professor of oceanography Michael Follows, CBIOMES draws together an multidisciplinary group of both U.S. and international investigators bridging oceanography, statistics, data science, ecology, biogeochemistry, and remote sensing.

The goal of CBIOMES (pronounced “sea biomes”), which leverages and extends Follow’s Darwin Project activity, is to develop and apply quantitative models of the structure and function of marine microbial communities at seasonal and basin scales.

As Follows explains, “Microbial communities in the sea mediate the global cycles of elements including climatically significant carbon, sulfur and nitrogen. Photosynthetic microbes in the surface ocean fix these elements into organic molecules, fueling food webs that sustain fisheries and most other life in the ocean. Sinking and subducted organic matter is remineralized and respired in the dark, sub-surface ocean, maintaining a store of carbon about three times the size of the atmospheric inventory of CO₂.”

The communities of microbes that sustain these global-scale cycles are functionally and genetically extremely diverse, non-uniformly distributed and sparsely sampled. Their biogeography reflects selection according to the relative fitness of myriad combinations of traits that govern interactions with the environment and other organisms. Trait-based theory and simulations provide tools with which to interpret biogeography and microbial mediation of biogeochemical cycles. Follows says, “Several outstanding challenges remain: Observations to constrain the biogeography of marine microbes are still sparse and based on eclectic sampling methods. Theories of the organization of the system have not been quantitatively tested, and the models used to simulate the system still lack sufficiently mechanistic biological foundations. Addressing these issues will enable meaningful, dynamic simulations and state estimation.”

CBIOMES seeks to integrate key new data sets in real-time as they are collected at sea to facilitate direct tests of theoretical predictions to synthesize an atlas of marine microbial biogeography suitable for testing a range of specific ecological theories and quantifying the skill of numerical simulations. It also aspires to develop new trait-based models and simulations of regional and global microbial communities bringing to bear the power of metabolic constraints and knowledge of macro-molecular composition; to analyze these data and models using statistical tools to interpolate and extrapolate the sparse data sets, formally quantify the skill of numerical simulations, and employ data assimilation technologies to identify and optimize compatible model frameworks. “Together, the results of these efforts will advance new theoretical approaches and lead to improved global ocean-scale predictions and regional state-estimates, constrained by observed biogeography. They will provide a quantification of the associated biogeochemical fluxes,” says Follows.

Working with Follows on CBIOMES are principal investigators Stephanie Dutkiewicz of MIT; Jacob Bien, Christopher Edwards, and Jed Fuhrman of the University of Southern California; Zoe Finkel and Andrew Irwin of Mount Allison University in Canada; Shubha Sathyendranath of Plymouth Marine Laboratory in the U.K., and Joseph Vallino of the Woods Hole Oceanographic Institute.

A meeting held at the Simons Foundation in New York City May 21 through 23 provided a first opportunity for collaborators to meet face-to-face, and provided a forum for investigators to educate one another about each others expertise and areas of activity, share initial progress, and coordinate collaborative efforts.

Discussions centered around how to determine the biogeography of marine microbes from empirical date, the role of statistical models in determining the relationships in space and time between organisms, traits, and environments, the complimentary role of mechanistic models and how to simulate the systems that are observed, and, in the context of model-date synthesis, how to best utilize empirical data to test theory and improve simulation skill.

“While the central question 'What is the functional biogeography of a group of organisms in the oceans?' is relatively focused, the techniques being used are extremely varied focusing a lot on computational tools, but uniquely, hand-in-hand with data collection and data compilation,” says Follows. “I am particularly excited by everyone’s enthusiasm, the number of cross-connections and collaborations already underway, and the rapid progress that is happening on many fronts.”

Complementary to CBIOMES is the Simons Collaboration on Ocean Processes and Ecology (SCOPE) co-led by Ed DeLong of the MIT Department of Civil and Environmental Engineering and David Karl of the University of Hawaii. SCOPE’s focus is advancing understanding of marine biology, biogeochemistry, ecology and evolution of microbial processes by focusing on a representative ocean benchmark, Station ALOHA, located in the North Pacific Subtropical Gyre.

SCOPE-Gradients, a related project, with a focus on understanding transitions between the North Pacific Subtropical Gyre and neighboring ecosystems, brings a rich stream of observational data to the CBIOMES effort. The North Pacific Subpolar Gyre is a region of open ocean notable for exhibiting steep changes in environmental conditions (gradients) associated with dramatic changes in the microbial ecosystem. Several members of the SCOPE-Gradients team accompanied project principal investigator Virginia Armbrust of the University of Washington to the May CBIOMES meeting, where they shared a preliminary implementation of the dynamic atlas they are constructing to curate disparate observational data and model results within a common framework.

The mission of the Simons Foundation is to advance the frontiers of research in mathematics and the basic sciences. Co-founded in New York City by Jim and Marilyn Simons, the foundation exists to support basic — or discovery-driven — scientific research undertaken in the pursuit of understanding the phenomena of our world.

As well as Michael Follows, other Simons Foundation funded investigators in the MIT Department of Earth, Atmospheric and Planetary Sciences include Tanja Bosak, Gregory Fournier, and Roger Summons. Several MIT postdocs have been recipients of Simons Postdoctoral Fellowships, among them Alexandria Johnson, Sukrit Ranjan and Christopher Follett.

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Ties with MIT run deep for the US Navy’s top officer

Looking back on his MIT graduate student days in the late 1980s, Admiral John M. Richardson SM ’89, EE ’89, ENG ’89 recalls a quieter time. He was not yet helming the world’s most powerful navy nor was global competition at sea nearly so high.

Richardson is now the chief of naval operations (CNO), the senior four-star admiral leading the U.S. Navy. This position places him on the Joint Chiefs of Staff as adviser to the secretary of defense and the president. He draws on his deep ties to academe to help the Navy keep pace.

From his graduate student days to today what has remained unchanged are the depth of his attachment to MIT and the warmth and respect between Richardson and his mentors in the MIT-Woods Hole Oceanographic Institution Joint Program.

“As a graduate student, John clearly stood out as brilliant, a leader, and wonderfully warm and friendly,” says Alan Oppenheim, an MIT Ford Professor of Engineering.

After his time at MIT and Woods Hole, Richardson went on to command the submarine USS Honolulu, a ship known in the Navy for the important missions for which it was tasked. Before that command he was posted at the White House as President Clinton’s Navy adviser. Just before being selected to be the CNO he was in charge of all of the nuclear reactor technology in the Navy.

“It is so striking that through his ascendancy in the Navy, John never lost these professional and personal qualities. He is as approachable today as he was back then,” Oppenheim says.

The power of relationships

Richardson recently took time from his schedule to articulate the significance of MIT in his life and career. He says friendships that began during graduate work quickly expanded to bluefish barbeques, bike riding, wind surfing, and listening to jazz and country music together, and many other things that “we still share even 30 years later.”

He speaks with affection of strong relationships with academics such as Oppenheim and Arthur Baggeroer, an MIT professor of mechanical, ocean, and electrical engineering and a Ford Professor of Engineering, Emeritus. “What I value most about my time at MIT are the enduring relationships with amazing people. Al, Art, and so many others have enriched my life so much — they are my mentors, my senseis.”

Richardson insists other alumni have made what he describes as “far more important contributions to the field of engineering.” And says for his part, he’s been able to apply his time at MIT to leading the Navy.

“In the end, it’s all about making our sailors the best in the world,” Richardson says. “The Navy that I'm so privileged to lead has always used world-leading technology, brought to life by our partnership with academe. MIT has always been a bright star in that constellation of innovation and excellence.” 

More like a family reunion

Richardson recalls a fall 2017 symposium about the future of signal processing in honor of Oppenheim, a pioneer in the field. “I'll never forget the warm feelings of camaraderie that defined Al’s conference on the future of signal processing and 80th birthday celebration.” He describes himself as “super nervous” after accepting the invitation to speak because he knew “the world’s best would be there to listen.”

“All of that anxiety was instantly dispelled by the love and respect Al engenders in others, and that will always be part of his legacy. We all felt like family by our association with him and MIT,” Richardson says.

At the symposium, the admiral outlined the challenges ahead for the Navy and invited solutions. “I want to share with you my problems to provide a template for those of you all with solutions,” he said, standing in full dress uniform. “This is a continuation of a great tradition that we have between the Navy and MIT.”

The Navy faced a submarine problem in the Atlantic during World War II that MIT helped solve through a rigorous application of emerging science in operations research, he said. “Academe came to our rescue there.”

The same was true for the Battle of Britain, during which MIT-developed naval anti-aircraft technology played a pivotal role in beating back large-scale attacks by Nazi Germany. “We have a long tradition of working together.”

Among other things, MIT has a long-standing Graduate Program in Naval Construction and Marine Engineering in close cooperation with the Navy dating back to 1901. The program prepares U.S. Navy, U.S. Coast Guard, foreign naval officers, and other graduate students for careers in ships design and construction.

Challenges in the maritime

The traffic on the ocean has increased by a factor of four over the past 25 years, Richardson said to a packed room during the conference on the future of signal processing. “Just picture that curve in your mind. The amount of food we get from the sea has increased by more than a factor of 10 in the same time period.”

“The Arctic ice cap is the smallest it has been since we started taking measurements and getting smaller, and that has tremendous implications for traffic routes and access to resources,” he said.

The internet of things will include 30 billion devices connected by 2020. And 99 percent of web data rides on undersea cables on the sea floor. “It’s not about a cloud, it’s about the ocean,” said Richardson. “If cables are disturbed or disrupted, you can’t reconstitute that via satellites or anything else, you can only fight back and get about two percent.”

“Things are moving very quickly. It’s very competitive. We’ve done a lot of work to try and figure out — how should the Navy respond?” he said. Multiple analyses show a need for heightened naval capability. Yet even the most aggressive shipbuilding plan equates to reaching 350 ships in about 17 years.

In his presentation, Richardson pointed to a chart with icons representing the U.S. fleet: ships, satellites, submarines, and aircraft. Let’s redefine the axis, he said. The measure of naval capability no longer rests only on the numerical metric of physical things but also on the ability to network platforms and to manage information.

“Signal processing has a terrific and important role in helping us transcend just making more ships. We must make our ships – and our Navy – more capable as well,” said Richardson. He pointed to a new graph in which U.S. naval power rises beyond exponential curves as the fleet is deeply networked with the assistance of technologies such as artificial intelligence, human and machine teams, and quantum computing.

Drawing on academe

More recently, Richardson created Task Force Ocean, which seeks to link innovative research concepts with the needs of the U.S. Navy, especially undersea forces. The senior academic involved in these Navy efforts is Arthur Baggeroer.

“I have known every chief of naval operations over the last two decades, and John is by far the most engaged with academia,” says Baggeroer, who was the director of the MIT-WHOI program when Richardson enrolled in 1985. He also acted as academic advisor to Richardson and five additional naval officers in the program.

Over the years, Baggeroer kept up with Richardson as he rose through the ranks.

“He has been very supportive of the MIT-WHOI Joint Program and has taken steps to attract to the program younger officers with the same qualifications he had at the time,” adds Baggeroer. 

Setting a high bar

Richardson was, by all accounts, a star graduate student. His career track and leadership continue to inspire Navy students, says Tim Stanton, scientist emeritus at the Woods Hole Oceanographic Institution. He joined Oppenheim as Richardson’s thesis advisor.

“Admiral Richardson sets the gold standard for excellence and leadership in the Navy,” says Stanton. “As I advised many Navy students for the nearly 30 years after Admiral Richardson graduated, they frequently referenced his leadership as a benchmark for their career goals. Through his leadership, he not only directly impacted Navy operations, but also the next generation of leaders in the Navy.”

“I’m so grateful for the continued friendship, partnership and leadership of MIT with the Navy,” says Richardson. “MIT has had an amazing impact on me and my life. It literally changed the way I think about things.”

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Fundamental equations guide marine robots to optimal sampling sites

Observing the world’s oceans is increasingly a mission assigned to autonomous underwater vehicles (AUVs) — marine robots that are designed to drift, drive, or glide through the ocean without any real-time input from human operators. Critical questions that AUVs can help to answer are where, when, and what to sample for the most informative data, and how to optimally reach sampling locations.

MIT engineers have now developed systems of mathematical equations that forecast the most informative data to collect for a given observing mission, and the best way to reach the sampling sites.

With their method, the researchers can predict the degree to which one variable, such as the speed of ocean currents at a certain location, reveals information about some other variable, such as temperature at some other location — a quantity called “mutual information.” If the degree of mutual information between two variables is high, an AUV can be programmed to go to certain locations to measure one variable, to gain information about the other.  

The team used their equations and an ocean model they developed, called  Multidisciplinary Simulation, Estimation, and Assimilation Systems (MSEAS), in sea experiments to successfully forecast fields of mutual information and guide actual AUVs.

“Not all data are equal,” says Arkopal Dutt, a graduate student in MIT’s Department of Mechanical Engineering. “Our criteria … allow the autonomous machines to pinpoint sensor locations and sampling times where the most informative measurements can be made.”

To determine how to safely and efficiently reach ideal sampling destinations, the researchers developed a way to help AUVs use the uncertain ocean’s activity, by forecasting out a “reachability front” — a dynamic three-dimensional region of the ocean that an AUV would be guaranteed to reach within a certain time, given the AUV’s power constraints and the ocean’s currents. The team’s method enables a vehicle to surf currents that would bring it closer to its destination, and avoid those that would throw it off track.

When the researchers compared their reachability forecasts with the routes of actual AUVs observing a region of the Arabian Sea, they found their predictions matched where the vehicles were able to navigate, over long periods of time.

Ultimately, the team’s methods should help vehicles explore the ocean in an intelligent, energy-efficient manner.

“Autonomous marine robots are our scouts, braving the rough seas to collect data for us,” says mechanical engineering graduate student Deepak Subramani. “Our math equations help the scouts reach the desired locations and reduce their energy usage by intelligently using the ocean currents.”

The researchers, led by Pierre Lermusiaux, professor of mechanical engineering and ocean science and engineering at MIT, have laid out their results in a paper soon to appear in a volume of the book series, “The Sea,” published by the Journal of Marine Research.

In addition to Dutt and Subramani, Lermusiaux’s team includes Jing Lin, Chinmay Kulkarni, Abhinav Gupta, Tapovan Lolla, Patrick Haley, Wael Hajj Ali, Chris Mirabito, and Sudip Jana, all from the Department of Mechanical Engineering.

Quest for the most informative data

To validate their approach, the researchers showed that they could successfully predict the measurements that were the most informative for a varied set of goals. For example, they forecast the observations that were optimal for testing scientific hypotheses, learning if the ocean model equations themselves are correct or not, estimating parameters of marine ecosystems, and detecting the presence of coherent structures in the ocean. They confirmed that their optimal observations were 50 to 150 percent more informative than an average observation.

To reach the optimal observing locations, AUVs must navigate through the ocean. Traditionally, planning paths for robots has been done in relatively static environments. But planning through the ocean is a different story, as strong currents and eddies can constantly change, be uncertain, and push a vehicle off its preplanned course.

The MIT team thus developed path-planning algorithms from fundamental principles with the ocean in mind. They modified an existing equation, known as the Hamilton-Jacobi equation, to determine an AUV’s reachability front, or the furthest perimeter a vehicle is guaranteed to reach in a given amount of time. The equation is based on three main variables: time, a vehicle’s specific propulsion constraints, and advection, or the transport by the dynamic ocean currents — a variable which the group predicts by using its MSEAS ocean model.

With the new system, the AUVs can map out the feasible most informative paths and adapt their sampling plans as the uncertain ocean’s currents shift over time. In a first large, open-ocean test, the team calculated probabilistic reachability fronts and the most informative paths for autonomous floats and gliders in the Indian Ocean, as part of the Northern Arabian Sea Circulation-autonomous research (NASCar) initiative of the Office of Naval Research (ONR).

Over several months, the researchers, working out of their MIT offices, provided daily reachability forecasts to the ONR team to help guide the underwater vehicles, collecting optimal observations along the way.

“It was basically not much sleeping,” Lermusiaux recalls. “The forecasts were three to seven days out, and we would assimilate data and update every day. We did quite well. On average, the gliders and floats ended up where desired and within the probabilistic areas that we predicted.”

A moment of truth pays off

Lermusiaux and his colleagues also utilized their systems to plan “time-optimal paths” — trajectories that would get an AUV to a certain location in the shortest amount of time, given the forecast ocean current conditions.

With colleagues from the MIT Lincoln Laboratory and Woods Hole Oceanographic Institution, they tested these time-optimal paths in real time by holding “races” between identical propelled AUVs, off the coast of Martha’s Vineyard. In each race, one AUV’s course was determined by the team’s time-optimal path, while another AUV followed a path with the shortest distance to the same destination.

“It was tense — who will win?” Subramani recalls. “This was the moment of truth for us, after all those years of theoretical development with math equations and proofs.”

The team’s work paid off. In every race, the AUV operating under the team’s forecast reached its destination first, performing about 15 percent faster than the competing AUV. The team’s forecast helped the winning AUV to avoid strong currents that at times acted to block the other AUV.

“It was amazing,” Kulkarni says. “Even though physically the two paths were only less than a mile apart, following our predictions gave up to a 15 percent reduction in travel times. It shows our paths are truly time optimal.”

Among other applications, Lermusiaux, as a member of MIT’s Tata Center for Technology and Design, will be applying his ocean forecasting methods to help guide observations off the coast of India, where the vehicles will be tasked with monitoring fisheries to provide a potentially low-cost management system.

“AUVs are not very fast, and their autonomy is not infinite, so you really have to take into account the currents and their uncertainties, and model things rigorously,” Lermusiaux says. “Machine intelligence for these autonomous systems comes from rigorously deriving and merging governing differential equations and principles with control theory, information theory, and machine learning.”

This research was funded, in part, by the Office of Naval Research, the MIT Lincoln Laboratory, the MIT Tata Center, and the National Science Foundation.

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Understanding microbial competition for nitrogen

Nitrogen is a hot commodity in the surface ocean. Primary producers including phytoplankton and other microorganisms consume and transform it into organic molecules to build biomass, while others transform inorganic forms to access their chemical store of energy. All of these steps are part of the complex nitrogen cycle of the upper water column.

About 200 meters down, just below the ocean’s sunlit zone, resides a layer of nitrite, an intermediate compound in the nitrogen cycle. Scientists have found this robust feature, called the primary nitrite maximum, throughout the world’s oxygenated oceans. While several individual hypotheses have been put forward, none have convincingly explained this marine signature until now.

A recent Nature Communications study led by researchers in the Program in Atmospheres, Oceans and Climate (PAOC) within MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS) uses theory, modeling, and observational data to investigate the ecological mechanisms producing the observed nitrite accumulation and dictating its location in the water column. Lead author Emily Zakem — a former EAPS graduate student who is now a postdoc at the University of Southern California — along with EAPS Principal Research Scientist Stephanie Dutkiewicz and Professor Mick Follows show that physiological constraints and resource competition between phytoplankton and nitrifying microorganisms in the sunlit layer can yield this ocean trait. 

Regulating the biological pump

Despite its low oceanic concentration, nitrite (NO2-) plays a key role in global carbon and nitrogen cycles. Most of the nitrogen in the ocean resides in the inorganic form of nitrate (NO3-), which primary producers and microorganisms chemically reduce it to build organic molecules. Remineralization occurs when the reverse process takes place: Phytoplankton and other heterotrophic bacteria break down these organic compounds into into ammonium (NH4+), a form of inorganic nitrogen. Ammonium then can be consumed again by primary producers, which get their energy from light. Other microorganisms called chemoautotrophs also use the ammonium both to make new biomass and as a source of energy. To do this, they extract oxygen from seawater transform it, a process called nitrification, which occurs in two steps. First, the microbes convert ammonium into nitrite and then to nitrate.

Somewhere along the line, nitrite has been accumulating at the base of the sunlit zone, which has implications for ocean biogeochemistry. “Broadly, we’re trying to understand what controls the remineralization of organic matter in the ocean. It’s that remineralization that is responsible for forming the biological pump, which is the extra storage of carbon in the ocean due to biological activity,” says Zakem. It’s this strong influence that nitrogen has on the global carbon cycle that captures Follows’ interest. “Growth of phytoplankton on nitrate is called ‘new production’ and that balances the amount that’s sinking out of the surface and controls how much carbon is stored in the ocean. Growth of phytoplankton on ammonium is called recycled production, which does not increase ocean carbon storage,” Follows says. “So we wish to understand what controls the rates of supply and relative consumption of these different nitrogen species.”

Battle for nitrogen 

The primary nitrite maximum resides between two groups of microorganisms in most of the world’s oceans. Above it in the sunlit zone are the phytoplankton, and in the primary nitrite maximum and slightly below that rest an abundance of nitrifying microbes in an area with high rates of nitrification. Researchers classify these microbes into two groups based on their preferred nitrogen source: the ammonium oxidizing organisms (AOO) and nitrite oxidizing organisms (NOO). In high latitudes like the Earth’s subpolar regions, nitrite accumulates in the surface sunlit zone as well as deeper.

Scientists have postulated that there might be two not mutually exclusive reasons for the build-up of nitrite: Nitrification by chemoautotrophic microbes, and when stressed, phytoplankton can reduce nitrate to nitrite. Since isotopic evidence does not support the latter, the group looked into the former. 

“The long-standing hypothesis was that the locations of nitrification were controlled by the inhibition of light of these [nitrifying] microorganisms, so the microorganisms that carry out this process were restricted from the surface,” Zakem says, implying that these nitrifying chemoautotrophs got sunburned. But instead of assuming that was true, the group examined the ecological interactions among these and other organisms in the surface ocean, letting the dynamics fall out naturally. To do this they collected microbial samples from the subtropical North Pacific and evaluated them for metabolism rates, efficiencies and abundances, and assessed the physiological needs and constraints of the different nitrifying microbes by reducing the biological complexity of their metabolisms down to its underlying chemistry and thus hypothesizing some of the more fundamental constrains. They used this information to inform the dynamics of the nitrifying microbes in both a one-dimensional and three-dimensional biogeochemical models.

The group found that by employing this framework, they could resolve the interactions between these nitrifying chemoautotrophs and phytoplankton and therefore simulate the accumulation of nitrite at the primary nitrite maximum in the appropriate locations. In the surface ocean when inorganic nitrogen is a limiting factor, phytoplankton and ammonium oxidizing microbes have similar abilities to acquire ammonium, but because phytoplankton need less nitrogen to grow and have a faster growth rate, they are able to outcompete the nitrifiers, excluding them from the sunlit zone. In this way, they were able to provide an ecological explanation for where nitrification happens without having to rely on light inhibition dictating the location.

Comparing the fundamental physiologies of the nitrifiers revealed that differences in metabolisms and cell size could account for the nitrite build-up. The researchers found that the second step of the nitrification process that’s carried out by the nitrite oxidizers requires more nitrogen for the same amount of biomass being created by these organisms, meaning that the ammonia oxidizers can do more with less, and that there are fewer nitrite oxidizers than the ammonia oxidizers. The nitrite oxidizing microbes also have a higher surface to volume constraint than the smaller and ubiquitous ammonium oxidizing microbes, making nitrogen uptake more difficult. “This is an alternative explanation for why nitrite should accumulate,” Zakem says. “We have two reasons that point in the same direction. We can’t distinguish which one it is, but all of the observations are consistent with either of these two or some combination of both being the control.”

The researchers were also able use a global climate model to reproduce an accumulation of nitrite in the sunlit zone of places like subpolar regions, where phytoplankton are limited by another resource other than nitrogen like light or iron. Here, nitrifiers can co-exist with phytoplankton since the phytoplankton since there’s there’s more nitrogen available to them. Additionally, the deep mixed layer in the water can draw resources away from the phytoplankton, giving the nitrifiers a better chance at survival in the surface.

“There’s this long standing hypothesis that the nitrifiers were inhibited by light and that’s why they only exist at the subsurface,” Zakem says. “We’re saying that maybe we have a more fundamental explanation: that this light inhibition does exist because we’ve observed it, but that’s a consequence of long-term exclusion from the surface.”

Thinking bigger

“This study pulled together theory, numerical simulations, and observations to tease apart and provide a simple quantitative and mechanistic description for some phenomena that were mysterious in the ocean,” Follows says. “That helps us tease apart the nitrogen cycle, which has an impact on the carbon cycle. It's also opened up the box for using these kind of tools to address other questions in the microbial oceanography.” He notes that the fact that these microbes are shunting ammonium into nitrate near the sunlit zone complicates the story of carbon storage in the ocean.

Two researchers who were not involved with the study, Karen Casciotti, associate professor in the Stanford University Department of Earth System Science, and Angela Landolfi, a scientist in the marine biogeochemical modeling department at the GEOMAR Helmholtz Centre for Ocean Research Kiel, agree. “This study is of great significance as it provides evidence of how organisms’ individual traits affect competitive interactions among microbial populations and provide a direct control on nutrients' distribution in the ocean,” says Landolfi. “In essence Zakem et al., provide a better understanding of the link between different levels of complexity from individual- to community up to environmental level, providing a mechanistic framework to predict changes in community composition and their biogeochemical impact under climatic changes,” says Landolfi.

This research was funded by the Simons Foundation’s Simons Collaboration on Ocean Processes and Ecology, the Gordon and Betty Moore Foundation, and the National Science Foundation.

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Soft robotic fish swims alongside real ones in coral reefs

This month scientists published rare footage of one of the Arctic’s most elusive sharks. The findings demonstrate that, even with many technological advances in recent years, it remains a challenging task to document marine life up close.

But MIT computer scientists believe they have a possible solution: using robots.

In a paper out today, a team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) unveiled “SoFi,” a soft robotic fish that can independently swim alongside real fish in the ocean.

During test dives in the Rainbow Reef in Fiji, SoFi swam at depths of more than 50 feet for up to 40 minutes at once, nimbly handling currents and taking high-resolution photos and videos using (what else?) a fisheye lens.

Using its undulating tail and a unique ability to control its own buoyancy, SoFi can swim in a straight line, turn, or dive up or down. The team also used a waterproofed Super Nintendo controller and developed a custom acoustic communications system that enabled them to change SoFi’s speed and have it make specific moves and turns.

“To our knowledge, this is the first robotic fish that can swim untethered in three dimensions for extended periods of time,” says CSAIL PhD candidate Robert Katzschmann, lead author of the new journal article published today in Science Robotics. “We are excited about the possibility of being able to use a system like this to get closer to marine life than humans can get on their own.”

Katzschmann worked on the project and wrote the paper with CSAIL director Daniela Rus, graduate student Joseph DelPreto and former postdoc Robert MacCurdy, who is now an assistant professor at the University of Colorado at Boulder.

How it works

Existing autonomous underwater vehicles (AUVs) have traditionally been tethered to boats or powered by bulky and expensive propellers.

In contrast, SoFi has a much simpler and more lightweight setup, with a single camera, a motor, and the same lithium polymer battery that’s found in consumer smartphones. To make the robot swim, the motor pumps water into two balloon-like chambers in the fish’s tail that operate like a set of pistons in an engine. As one chamber expands, it bends and flexes to one side; when the actuators push water to the other channel, that one bends and flexes in the other direction.

These alternating actions create a side-to-side motion that mimics the movement of a real fish. By changing its flow patterns, the hydraulic system enables different tail maneuvers that result in a range of swimming speeds, with an average speed of about half a body length per second.

“The authors show a number of technical achievements in fabrication, powering, and water resistance that allow the robot to move underwater without a tether,” says Cecilia Laschi, a professor of biorobotics at the Sant'Anna School of Advanced Studies in Pisa, Italy. “A robot like this can help explore the reef more closely than current robots, both because it can get closer more safely for the reef and because it can be better accepted by the marine species.”

The entire back half of the fish is made of silicone rubber and flexible plastic, and several components are 3-D-printed, including the head, which holds all of the electronics. To reduce the chance of water leaking into the machinery, the team filled the head with a small amount of baby oil, since it’s a fluid that will not compress from pressure changes during dives.

Indeed, one of the team’s biggest challenges was to get SoFi to swim at different depths. The robot has two fins on its side that adjust the pitch of the fish for up and down diving. To adjust its position vertically, the robot has an adjustable weight compartment and a “buoyancy control unit” that can change its density by compressing and decompressing air.

Katzschmann says that the team developed SoFi with the goal of being as nondisruptive as possible in its environment, from the minimal noise of the motor to the ultrasonic emissions of the team’s communications system, which sends commands using wavelengths of 30 to 36 kilohertz.

“The robot is capable of close observations and interactions with marine life and appears to not be disturbing to real fish,” says Rus.

The project is part of a larger body of work at CSAIL focused on soft robots, which have the potential to be safer, sturdier, and more nimble than their hard-bodied counterparts. Soft robots are in many ways easier to control than rigid robots, since researchers don’t have to worry quite as much about having to avoid collisions.

“Collision avoidance often leads to inefficient motion, since the robot has to settle for a collision-free trajectory,” says Rus, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT. “In contrast, a soft robot is not just more likely to survive a collision, but could use it as information to inform a more efficient motion plan next time around.”

As next steps the team will be working on several improvements on SoFi. Katzschmann plans to increase the fish’s speed by improving the pump system and tweaking the design of its body and tail.

He says that they also plan to soon use the on-board camera to enable SoFi to automatically follow real fish, and to build additional SoFis for biologists to study how fish respond to different changes in their environment.

“We view SoFi as a first step toward developing almost an underwater observatory of sorts,” says Rus. “It has the potential to be a new type of tool for ocean exploration and to open up new avenues for uncovering the mysteries of marine life.”

This project was supported by the National Science Foundation.

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