Communities around the world have adapted to live on the year-round frozen soil of frigid environments, such as in the Arctic. However, rising temperatures have introduced a new challenge: What happens when the ground under houses and roads begins to melt?
To better understand how these environments are changing, an interdisciplinary team of engineers and geoscientists led by researchers at Penn State has developed a computational framework that can use real-time measurements and artificial intelligence (AI) to predict the physical properties of the frozen soil, called permafrost. In one of the first studies of its kind, published in JGR Earth Surface and featured in Eos Magazine, the team applied their framework to a specific road embankment in Utqiaġvik, Alaska - the northernmost city in the United States. They recreated the permafrost's thermal properties with chilling accuracy, opening a pathway to more accurate predictions of how climate change may impact permafrost around the world.
According to Ming Xiao, professor of civil engineering at Penn State and corresponding author on the study, rising temperatures on Earth are causing permafrost in many areas of the world to rapidly thaw, with ground temperatures increasing by up to almost two degrees Fahrenheit per decade. This thawing can release dormant strains of bacteria or large emissions of carbon dioxide into the atmosphere, further accelerating global warming. Additionally, Arctic communities and governments could see billions of dollars in infrastructure damage over the coming decades if this trend continues.
"What makes permafrost unique is that it largely consists of ice," Xiao said. "As this ice melts, it turns to water. This makes thawed permafrost a very weak soil - it becomes very muddy, which can compromise the stability of infrastructure like roads, buildings or pipelines built in or on it."
Standard methods of predicting permafrost degradation require a ton of computational power and existing data. AI-powered modeling has proven more efficient, but Xiao explained that these models generally perform poorly when applied beyond their initial training data.
However, through what Tieyuan Zhu, associate professor of geosciences at Penn State and co-author on the study, described as an "accidental conversation" at a work party, Xiao's team realized that there was other research underway at Penn State that could help strike a balance between computational cost and prediction quality.
"My team's research uses advanced seismic temperature sensors built into fiber-optic cable to better study geology," Zhu said. "Over a conversation at a barbecue, Dr. Xiao recognized how our research could help build an accurate, physics-informed method of understanding and predicting how permafrost is changing in the Arctic."
This framework is known as a digital twin, which processes terabytes of data to create an extremely accurate, real-time simulation of an area or object. These simulations have become widespread in other fields such as mechanical or biomedical engineering, but Xiao said applying this concept to permafrost monitoring hadn't been thought of before. For this study, the team buried a pair of one-kilometer-long - about two-thirds of a mile or roughly 10 football fields - fiber-optic cables capable of collecting thermal and seismic data from the ground. A short section of these cables is along a road embankment, which collected temperature and seismic data from September 2021 to June 2024. The researchers then used some of the data as a foundation to build the twin.
