 |
| Credit: Penn State |
The work aims to use machine learning both to better predict seismic activity during geothermal exploration and to optimize geothermal energy production.
Geothermal systems require the creation of fractures through hydraulic stimulation. This fracture formation and stimulation is associated with microearthquakes (MEQs) that can damage buildings and other surface structures. Chris Marone, professor of geosciences and Jing Yang, assistant professor of electrical engineering hope that by using Yang's machine learning (ML) algorithms they will be able to forecast and predict seismic events such as MEQs.
"We are very interested in whether certain precursors exist for microearthquakes so that we can predict when a major seismic activity is going to happen in the near future, upon which some immediate actions can be taken before anything destructive happens," said Yang.
