Applying machine learning tools to earthquake data offers new insights: Algorithms pick out hidden signals that could boost geothermal energy production

In a new study in Science Advances, researchers at Columbia University show that machine learning algorithms could pick out different types of earthquakes from three years of earthquake recordings at The Geysers in California, one of the world's oldest and largest geothermal reservoirs. The repeating patterns of earthquakes appear to match the seasonal rise and fall of water-injection flows into the hot rocks below, suggesting a link to the mechanical processes that cause rocks to slip or crack, triggering an earthquake. "It's a totally new way of studying earthquakes," said study coauthor Benjamin Holtzman, a geophysicist at Columbia's Lamont-Doherty Earth Observatory. "These machine learning methods pick out very subtle differences in the raw data that we're just learning to interpret." The approach is novel in several ways.