Earth News

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Machine learning predicts new details of geothermal heat flux beneath the Greenland Ice Sheet A paper appearing in Geophysical Research Letters uses machine learning to craft an improved model for understanding geothermal heat flux—heat emanating from the Earth's interior—below the Greenland Ice Sheet. It's a research approach new to glaciology that could lead to more accurate predictions for ice-mass loss and global sea-level rise. Among the key findings: Greenland has an anomalously high heat flux in a relatively large northern region spreading from the interior to the east and west. Southern Greenland has relatively low geothermal heat flux, corresponding with the extent of the North Atlantic Craton, a stable portion of one of the oldest extant continental crusts on the planet. The research model predicts slightly elevated heat flux upstream of several fast-flowing glaciers in Greenland, including Jakobshavn Isbræ in the central-west, the fastest moving glacier on Earth. "Heat that comes up from the interior of the Earth contributes to the amount of melt on the bottom of the ice sheet—so it's extremely important to understand the pattern of that heat and how it's distributed at the bottom of the ice sheet," said Soroush Rezvanbehbahani, a doctoral student in geology at the University of Kansas who spearheaded the research. "When we walk on a slope that's wet, we're more likely to slip. It's the same idea with ice—when it isn't frozen, it's more likely to slide into the ocean. But we don't have an easy way to measure geothermal heat flux except for extremely expensive field campaigns that drill through the ice sheet. Instead of expensive field surveys, we try to do this through statistical methods. "Rezvanbehbahani and his colleagues have adopted machine learning—a type of artificial intelligence using statistical techniques and computer algorithms—to predict heat flux values that would be daunting to obtain in the same detail via conventional ice cores. Using all available geologic, tectonic and geothermal heat flux data for Greenland—along with geothermal heat flux data from around the globe—the team deployed a machine learning approach that predicts geothermal heat flux values under the ice sheet throughout Greenland based on 22 geologic variables such as bedrock topography, crustal thickness, magnetic anomalies, rock types and proximity to features like trenches, ridges, young rifts, volcanoes and hot spots.

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