Machine Fault - Eos

On a sturdy workbench in seismologist Chris Marone's lab on the fifth floor of the geosciences building at Pennsylvania State University (Penn State) sits a large steel-framed machine with thick hydraulic pistons that force metal blocks and plates to grind past each other under extreme pressure. When the device is running, Marone sometimes closes the door to the lab so the loud bangs of "laboratory earthquakes" do not disrupt people across the hall. Lately, however, it has been the quieter sounds emanating from the machine that have caused a disruption in the field of seismology. In a recent spate of studies, researchers applied machine learning to acoustic emission data from Marone's earthquake machine, as well as from natural faults. The work led to the discovery of a new relationship between a fault's acoustic emissions and its physical characteristics, including its frictional state, its displacement rate, and the timing and magnitude of its next failure.