Fracking is a relatively new form of crude oil and natural gas production that’s dramatically revived US hydrocarbon output.
The process involves blasting chemical-laced water below ground to fracture rock formation and withdraw oil or natural gas, opening up previously inaccessible reserves. But excess water is seeping out into dormant faults, and is thought to be causing them to slip, resulting in earthquakes.
Most existing earthquake-detection methods are designed to detect moderate-to-large events. As a consequence, they miss many low-magnitude earthquakes that get masked by background seismic noise.
But picking up the smaller quakes allows researchers to paint a more precise picture of all the earthquake activity in a place like Oklahoma, yielding a better understanding of the location of the quakes, whether they might be shifting, and whether the frequency is rising or falling. The extra data could eventually yield insight into whether a big one is coming, Perol said.
RELATED: Watch Swarms of Earthquakes Sweep Across Oklahoma
That’s because the art of predicting earthquakes remains essentially one of modeling likely future risk based on the patterns that have come before. In spite of some promising new research in the field of earthquake forecasting, the state-of-the-art is still limited, essentially, to an understanding of how many quakes have come before, and how often.
Existing platforms for detecting earthquakes use three stations to triangulate the source of the rumbling. The new method isn’t just more sensitive, but requires only one detection location.
To develop their system, Perol and his colleagues used a machine learning technique that funneled in both signal data and background noise then tweaked the algorithm until it could tell the difference. The data from real, recorded earthquakes were seeded into the system against backdrops of both real, field-recorded underground geological noise and synthetic blasts of artificially generated fuzz.
Using that method, “we trained the algorithm to detect, in real time, whether it’s noise or an earthquake,” Perol said.