Ground-motion modeling using MyShake smartphone peak acceleration data

In the field of ground-motion modeling, the availability of densely sampled ground-motion data is becoming key to mapping repeatable source, path, and site effects to enable ground-motion models (GMMs) to more accurately predict shaking from future earthquakes. This is particularly important because the field is moving toward nonergodic GMMs with spatially variable coefficients. To achieve the level of sampling required, the addition of non-instrumental data collected at very high spatial resolution, like felt intensity data or smartphone data, could prove essential. The predictive power of this nontraditional data for free-field ground motion needs to be tested before these data are used. In this work, we present a new database of over 1600 ground-shaking waveforms collected between 2019 and 2023 by the MyShake smartphone app, which delivers earthquake early warning messages to users on the U.S. West Coast. We develop a GMM, MyShake GMM, for peak smartphone-recorded accelerations in 3< or =M< or =5.5 earthquakes recorded at short (<50 km) distances. We compare our model with free-field GMMs and show a similar geometric decay and a close match in predicted amplitudes for short-period spectral accelerations (SAs). We use residual correlation analysis to show that MyShake GMM residuals have a positive correlation with free-field residuals, with correlation coefficients of around 0.4 for peak ground acceleration, velocity, and short-period SA, similar to correlations previously reported between felt intensity and free-field data. This illustrates the potential that densely sampled smartphone ground-shaking data has in identifying repeatable free-field ground-motion effects for various ground-motion modeling applications. These could potentially include highly location-specific assessments of site response, ground-motion interpolation schemes like ShakeMap, or validating outputs from nonergodic, spatially variable coefficient GMMs.