Satellite Data from Ship Captures Landslide-Generated Tsunami: A Breakthrough in Early Warning Systems

Landslide-generated tsunamis pose a significant threat to coastal communities, particularly within narrow fjords where tall cliffs can trap and amplify waves. Scientists have traditionally relied on earthquake-based observation systems to issue tsunami warnings, but these methods often fail to capture localized ground movement caused by landslides.

In a groundbreaking study published in Geophysical Research Letters, researchers from the University of Colorado Boulder’s Cooperative Institute for Research in Environmental Sciences (CIRES) and the University of Alaska Fairbanks have successfully detected tsunami waves generated by a landslide using data from a ship’s satellite receiver. This achievement has significant implications for improving early warning systems and saving lives.

On May 8, 2022, a landslide near the port city of Seward, Alaska, sent debris tumbling into Resurrection Bay, creating a series of small tsunami waves. The R/V Sikuliaq, a research ship owned by the National Science Foundation and operated by the University of Alaska Fairbanks, was moored 650 meters (0.4 miles) away from the landslide site. Fortunately, it was equipped with an external Global Navigation Satellite System (GNSS) receiver previously installed by Ethan Roth, the ship’s science operations manager.

Researchers took advantage of this unique opportunity to analyze data from the ship’s GNSS receiver and open-source software to calculate changes in the vertical position of the R/V Sikuliaq down to the centimeter level. They created a time series showing the ship’s height before, during, and after the landslide. By comparing the data to a landslide-tsunami model, they confirmed that the ship’s vertical movement was consistent with the event, marking the first detection of a landslide-generated tsunami from a ship’s satellite navigation system.

This breakthrough has significant implications for early warning systems. As researcher Adam Manaster noted, “If we process the data fast enough, warnings can be sent out to those in the affected area so they can evacuate and get out of harm’s way.” The study builds upon previous CIRES-led research demonstrating how GPS data from commercial shipping vessels could be used to improve tsunami early warning systems.

To implement this approach on a larger scale, researchers emphasize the need for collaboration with the shipping industry to make onboard data accessible to scientists. As researcher Anne Sheehan noted, “The science shows that this approach works. So many ships now have real-time GPS, but if we want to implement on a larger scale, we need to collaborate with the shipping industry.”

The University of Washington-led team has developed a system called ProxiCycle that logs when a passing car comes too close to a cyclist (within four feet). This small, inexpensive sensor plugs into bicycle handlebars and tracks the passes, sending them to the rider’s phone. The team tested the system for two months with 15 cyclists in Seattle and found a significant correlation between the locations of close passes and other indicators of poor safety, such as collisions.

“The threat of cars is the biggest deterrent to cycling,” said lead author Joseph Breda, a UW doctoral student in the Paul G. Allen School of Computer Science & Engineering. “We want to help novice cyclists navigate safer bike routes through cities.”

The team surveyed 389 people in Seattle and found that respondents of all cycling experience levels ranked the threat of cars as the factor which most discouraged them from cycling. They also said they’d be very likely to use a map that helps navigate for safety.

To create ProxiCycle, the team built a small sensor system that plugs into a bike’s left handlebar. The system consists of a 3D printed plastic casing that houses a pair of sensors and a Bluetooth antenna. The antenna transmits data to the rider’s phone, where the team’s algorithm susses out what’s a passing car rather than a person, or another cyclist, or a tree.

The team validated the system both by testing it in a parking lot, with a car passing at different distances, and with seven cyclists riding through Seattle with GoPro cameras on their handlebars. Researchers watched the footage from these rides and compared this to the sensor output.

In the future, researchers hope to scale the study up and potentially account for other risk factors, such as cyclists being hit by opening car doors, and deploy ProxiCylce in other cities. With enough data, existing bike mapping apps might include safer route suggestions for cyclists.

“One study participant found out that there’s a great bike lane on a quieter street just one block north,” said Breda. “It’s these minor adjustments that can make a big difference in safety.”

In a groundbreaking innovation, researchers have designed a light-powered soft robot that can carry loads through the air along established tracks, similar to cable cars or aerial trams. This autonomous soft robot, made of liquid crystal elastomers, can climb slopes at angles of up to 80 degrees and carry loads up to 12 times its weight.

According to Jie Yin, corresponding author of a paper on the work and an associate professor of mechanical and aerospace engineering at North Carolina State University, “We’ve previously created soft robots that can move quickly through water and across solid ground. Now, we’ve demonstrated that it’s possible to carry objects through the air across open space by following established tracks.”

The soft robot operates by being suspended on a track, which can be as thin as a human hair or as thick as a drinking straw. When exposed to infrared light perpendicular to the track, the portion of the ribbon absorbing the most light contracts, inducing a rolling motion that pulls the “cooler” part into the light. This cycle repeats itself over and over again, causing the soft ring robot to roll and twist on itself as it moves along the track.

“We’ve also shown that our soft ring robot can overcome obstacles on the track, such as knots or bulges,” says Fangjie Qi, first author of the paper and a Ph.D. student at NC State. “It can travel up or down a slope and carry loads more than 12 times its weight.”

In addition to navigating tracks of varying thickness, the researchers demonstrated that the soft robot can follow complex routes, including curved lines, circles, three-dimensional spirals, and so on, in a controlled way.

The team is now exploring specific applications for this technology, as well as adapting the soft robots to respond to inputs other than infrared light. “For example,” says Yin, “we’re thinking about developing a soft ring robot that operates in sunlight or responds to other external energy sources.”