Fig. 4. Tohoku-oki earthquake example.

(A) Representative displacement time series observed for Tohoku-oki earthquake. Black line: scientific-grade GPS. Red line: consumer-grade (C/A code) GPS. C/A code GPS positions are the worst type of data we expect to obtain from consumer devices. However, even these data do a good job of recovering the actual displacement time series as shown by the scientific-grade GPS data. (B) Diamonds showing estimated epicentral location colored by time after origin. Waves indicate tsunami arrival times (28). Blue contour: S wave position when detection criterion is satisfied. Cyan contour: S wave position when S wave reaches Tokyo. Although there is higher latency in this example than the Hayward fault example due to the offshore location of the earthquake and the noisier data used, the proposed crowdsourcing approach could detect and locate the Tohoku earthquake before strong shaking reaches Tokyo and before the tsunami makes landfall. (C) Number of potential earthquake triggers versus time. We looked at the time series of C/A code positions before the earthquake to determine the frequency with which a trigger might be observed due to noise. We then expressed the number of triggers as SDs from that background triggering rate and then, to be very conservative, do not issue a warning until the number of observed triggers exceeds 5σ of the background triggering rate. (D) Red: location error of our estimated epicenter relative to the epicenter of (29). Purple: error associated with locations reported by Japan Meteorological Agency (JMA) EEW system. Brown: first location available from global monitoring (30). Although significantly slower than scientific-quality EEW (which includes offshore near-source observations from ocean-bottom seismometers), the consumer-quality data are capable of determining the earthquake’s location just as accurately as the scientific-quality JMA EEW system and do so significantly faster than an epicenter could be obtained from global scientific seismic data. (E) Red: estimated magnitude release as a function of time. Purple: M_j values reported by JMA’s EEW system. Brown: first M_w estimate available from global monitoring (30). Black: true magnitude from independent kinematic rupture model (24). Again, although there is more latency in the magnitude estimated using only onshore consumer-quality data than offshore scientific-quality data, the proposed crowdsourced EEW system is significantly faster than the global response to the earthquake. Also, note that the consumer-quality magnitude, which is based on GNSS data, does not saturate like the seismic magnitudes estimated from scientific-quality seismic data.