Earthquake early warning (EEW) strives to detect an earthquake’s initiation, estimate its location and magnitude, and alert people and automated systems to imminent shaking (1, 2). EEW has had encouraging initial results (3), although two issues impede performance and wider implementation. First, magnitude estimates are unreliable for larger earthquakes [moment magnitude (Mw) >7] when based solely on brief observations of the earliest seismic waves (4). This can be overcome by using observations from Global Navigation Satellite Systems (GNSS) such as the Global Positioning System (GPS) (5). Second, it is expensive to install and operate the required dense seismic and GNSS networks. Nevertheless, even well-monitored regions such as California, Oregon, and Washington require extensive expansion and upgrade of existing instrumentation, including installing hundreds of new instruments, to implement EEW (6). Consequently, seismic EEW is operational in a handful of regions, and only a few of those (Japan, Mexico, and the United States) are incorporating GNSS data into their systems (Fig. 1) (7). Much of the global population exposed to high seismic risk, especially in poorer countries, does not benefit from EEW.

Commercial demand for personal mobile navigation has led to a proliferation of devices that use the same, albeit lower-quality, GNSS and Inertial Navigation Systems (INS) sensors used for EEW (8). Smartphones alone currently number 1 billion worldwide and will increase to ~5.9 billion by 2019 (9). The ubiquity of consumer devices raises the possibility that operational EEW could be achieved via crowdsourcing (10–12). For a global population exposed to ever-increasing earthquake risk (13), the significantly reduced costs associated with crowdsourcing could facilitate widespread EEW implementation and substantially reduce the impact of future earthquakes.

Although there has already been exploration of the potential of scientific seismic and GPS data (14), as well as consumer-quality accelerometers (15), for EEW and tsunami early warning, the potential of consumer-quality GNSS receivers remains untapped (16–18). As we will show, these data are rapidly improving in quality and are much less noisy than consumer-quality accelerometers, at only a fraction of the cost of scientific-quality instruments. Furthermore, GNSS data, which are displacement observations, are particularly well suited to monitoring large earthquakes and to produce magnitudes that do not saturate. Finally, we present a conceptual strategy for a crowdsourced EEW system that includes device use, data processing and quality control, earthquake detection, false alarm suppression, earthquake location, and magnitude determination that could be used to extend the benefits of EEW worldwide.

We assess the potential performance of smartphones and a crowdsourced EEW system in three ways. First, we perform controlled tests on a variety of consumer devices to determine their noise character and displacement detection capability. Second, for a scenario Mw 7 rupture on northern California’s Hayward fault, we generate synthetic smartphone accelerometer and GNSS time series for different data types we might obtain from crowdsourcing at random locations based on census population data. Finally, we consider GPS position time series of an actual earthquake, the Mw 9.0 Tohoku-Oki event, obtained using positioning data of the type found on consumer devices.

To obtain surface displacement observations for EEW, it is the instrument’s change in position that must be measured accurately; absolute position is used only for spatial reference. Consumer devices typically use single-frequency, C/A (coarse acquisition) code methods for GNSS positioning rather than the more precise and accurate locations derived from dual-frequency, carrier phase–based algorithms used in scientific applications (19). These C/A code positions can be substantially improved by using differential corrections via satellite-based augmentation systems (SBAS) (19), tracking the more precise GNSS carrier phase and using it to filter the C/A code data (“phase smoothing”) (20), or by combination with independent INS data in a Kalman filter (21). Today’s smartphones have some or all of these capabilities.

Two data types represent the range of data most likely for crowdsourced EEW. First, we investigated the least-precise data recorded by consumer devices: raw C/A code positions and low-quality accelerometer time series. These data can be used alone or in combination using a Kalman filtering approach (21). The Kalman filter takes advantage of the fact that the accelerometer is recording the second derivative of the same displacement time series that the GNSS receiver is recording, and from these two data sources produces a unified estimate of displacement with much less noise and bias than either of the original time series. Second, we considered the most sophisticated GNSS receiver commonly found in consumer devices: one capable of recording raw C/A data as well as real-time SBAS differential corrections while applying phase smoothing. To represent these scenarios (a poor GNSS receiver supplemented by a poor accelerometer, and a good GNSS receiver), we studied a latest-generation smartphone (Google Nexus 5) that contains a C/A code receiver and accelerometer, and a u-blox consumer GNSS receiver that is capable of recording SBAS and performing phase smoothing. We then subjected these two devices, along with a scientific-grade INS, to a series of displacements ranging from ~0.1 to 2.0 m (Fig. 2). The displacement time series from the scientific-grade INS system may be considered to be the true motion. Figure 2A shows the response of these two data types, as well as the twice-integrated acceleration and C/A code time series input to the Kalman filter, to a simple time series of motion. Both data types reproduce the time history of displacement with very high fidelity.