Fig. 3. Hayward fault earthquake scenario.

(A) Representative displacement time series from Hayward fault rupture scenario. Black line: true displacement. Blue line: simulated smartphone C/A code GNSS. Green line: simulated smartphone accelerometer, twice integrated. Red line: Kalman filter combining GNSS and accelerometer. The red line is representative of the data we expect to observe with the least sophisticated consumer devices, yet it still does a good job of recovering the true ground motion shown in black. (B) Diamonds showing estimated epicentral location colored by time after origin. As soon as the earthquake is detected (at 5 s after origin), its epicenter can be estimated with an error of less than 5 km using consumer-quality data. Contour: S wave position when detection criterion is satisfied. Yellow text denotes major cities: SF, San Francisco; SJ, San Jose; OK, Oakland. Blue dots denote observer locations assuming 0.2% of the population within the blue box contribute data. (C) Number of observers who have detected a potential earthquake trigger as a function of time. The higher the density of observations, the sooner the detection criteria of a hundred triggers is reached. With just 0.2% of the population contributing data, the earthquake can be detected in 5 s. (D) Epicenter location error as a function of time. The error on the epicenter location is always <5 km even with very small percentages of the population contributing observations. (E) Estimated moment magnitude as a function of time for different participation levels. Black line: true magnitude. The estimated magnitude release almost perfectly reproduced the actual time history of moment release with very little latency, even for very low participation rates. The accuracy and low latency of the detection, location, and magnitude estimate of the earthquake based on very small numbers of consumer-quality observations suggest that a crowdsourced EEW system is feasible.