When applied to the data sets taken from the Long Beach area, the algorithms detected substantially more earthquakes and made it easier to work out how and where they started. And when applied to data from a 2014 earthquake in La Habra, also in California, the team observed four times more seismic detections in the "denoised" data compared with the officially recorded number. Researchers from Penn State have been training deep-learning algorithms to accurately predict how changes in measurements could indicate forthcoming earthquakes--a task that has confounded experts for centuries. And members of the Stanford team previously trained models for phase picking, or measuring the arrival times of seismic waves within an earthquake signal, which can be used to estimate the quake's location. Deep-learning algorithms are particularly useful for earthquake monitoring because they can take the burden off human seismologists, says Paula Koelemeijer, a seismologist at Royal Holloway University of London, who was not involved in this study.

Safehub, whose platform enables businesses to monitor their buildings for signs of earthquakes, today closed a $5 million seed round. The company says it will use the capital to accelerate deployment to Fortune 500 customers as it expands its engineering team. A recent FEMA study pegged U.S. losses from earthquakes at $4.4 billion per year. In spite of the risk, more than 60% of U.S. small businesses don't have a formal emergency-response plan and fail to back up their sensitive data offsite. Safehub aims to close the gap with a real-time, building-specific earthquake damage data-gathering solution.

As earthquakes grow more frequent in the central United States--driven at least in part by the fracking boom--researchers have been working on sophisticated new tools, including satellites, underwater seismic sensors, and software to detect temblors and hopefully even predict them.