On the Feasibility of Using the Dense MyShake Smartphone Array for Earthquake Location

Abstract

MyShake is a growing smartphone-based network for seismological research applications. We study how dense array analysis of the seismic wavefield recorded by smartphones may enhance microearthquake monitoring in urban environments. In such areas, the microearthquake signal-to-noise ratio on smartphones is not well constrained. We address this issue by compiling a seismic noise model for the Los Angeles (LA) metropolitan area using over 500,000 seismograms recorded by stationary phones running MyShake. We confirm that smartphone noise level is reduced during nighttime, and identify strong noise sources such as major traffic highways, the LA airport, and the Long Beach seaport. The noise analysis shows that stationary smartphones are sensitive to human-induced ground motions, and therefore smartphone-derived seismograms may be used to infer the elastic properties of the shallow subsurface. We employ array backprojection analysis on synthetic data to estimate what fraction of LA's smartphone user population is required to install MyShake to enable the location of events whose induced ground motions are below the smartphone noise level. We find that having 0.5% of LA's population download the MyShake app would be sufficient to accurately locate M > 1 events recorded during nighttime by stationary phones located at epicentral distances < 5 km. Currently, the MyShake user coverage in LA is approaching a value that will allow us to locate events whose magnitude is near the regional catalog's magnitude of completeness.

Big Data Seismology

“…While EEW is simple in concept, the implementation of an effective system is surprisingly difficult in practice (Chung et al., 2020; Kohler et al., 2020; McGuire et al., 2021; Meier et al., 2020; Minson et al., 2018; Stubailo et al., 2020; Trugman et al., 2019; Wald, 2020), requiring an upfront investment in network densification, novel algorithms to analyze real‐time data streams to rapidly and accurately characterize earthquakes, telecommunication and engineering infrastructure to broadcast alerts to citizens in danger, and buy‐in from the community to prepare for and follow alert messages at a moment's notice. Many of the themes that pervade this article are essential to the EEW problem, from the application of dense arrays, to the adaptation of new technologies like smartphone‐based sensor networks (Brooks et al., 2021; Inbal et al., 2019; Kong, Inbal, et al., 2019; Kong et al., 2020a, 2020b), to the fusion of distinct types of data such as seismic and geodetic measurements (Murray et al., 2018). EEW and Big Data Seismology are inherently intertwined, and it is likely that the two disciplines will co‐evolve in tandem in the years to come.…”

On the feasibility of IoT-based smart meters for earthquake early warning

“…The MyShake platform (Inbal et al, 2019), which uses the MEMS accelerometers in smartphones, is a low-cost observation network with tens of thousands of stations. The Chinese MEMS-based network strong-motion seismograph (Fu et al, 2019) is another example of a low-cost technology for early warning systems, in addition to the P-alert network in Taiwan (Wu et al, 2019).…”

The MyShake Platform: A Global Vision for Earthquake Early Warning

“…Finally, phones can be remotely triggered to record for short periods of time, which allows the phones to be used as an array. Initial evaluation of the capability suggests that the MyShake network could detect and locate earthquakes as small as M1 using this approach (Inbal et al 2019). This is smaller than the events that traditional regional seismic networks can typically detect and could assist in the Vol.…”