Nearly all aspects of earthquake rupture are controlled by the friction along
the fault that progressively increases with tectonic forcing, but in general
cannot be directly measured. We show that fault friction can be determined at
any time, from the continuous seismic signal. In a classic laboratory
experiment of repeating earthquakes, we find that the seismic signal follows a
specific pattern with respect to fault friction, allowing us to determine the
fault's position within its failure cycle. Using machine learning, we show that
instantaneous statistical characteristics of the seismic signal are a
fingerprint of the fault zone shear stress and frictional state. Further
analysis of this fingerprint leads to a simple equation of state quantitatively
relating the seismic signal power and the friction on the fault. These results
show that fault zone frictional characteristics and the state of stress in the
surroundings of the fault can be inferred from seismic waves, at least in the
laboratory.Comment: 12 pages, 4 figures. Supplementary material not include
