Using machine learning, researchers at Penn State have tied low-magnitude microearthquakes to the permeability of subsurface rocks beneath the Earth, a discovery that could have implications for improving geothermal energy transfer.
Using machine learning, researchers at Penn State have tied low-magnitude microearthquakes to the permeability of subsurface rocks beneath the Earth, a discovery that could have implications for improving geothermal energy transfer.
Generating geothermal energy requires a permeable subsurface to efficiently release heat when cold fluids are forced into the rock. This research reveals the optimum times for efficient energy transfer by exposing the link to microearthquakes, which are monitored on the surface through seismometers. The team published their findings in Nature Communications.
Using funding from the U.S. Department of Energy (DOE) and two datasets from the EGS Collab and Utah FORGE demonstration projects, researchers used machine learning to extract the “noise” found in the data that obscured the link. Researchers then used machine learning to create a model from one site and successfully applied it to the other — a process called transfer learning — suggesting that the link was formed based on general physics of subsurface rocks. That means it’s likely to be universally true for all geothermal energy sites, the researchers said.
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