Ground motion model (GMM) selection and weighting introduces a significant source of uncertainty in United States Geological Survey (USGS) seismic hazard models. The increase in moderate moment magnitude induced earthquakes (Mw 4 to 5.8) in Oklahoma and Kansas since 2009, due to increased wastewater injection related to oil and gas production (Keranen et al., 2013; 2014; Weingarten et al., 2015; McNamara et al., 2015a), provides useful near-source (< 40 km) instrumental ground-motion observations for comparisons between central and eastern US (CEUS) induced (Rennolet et al., 2017) and tectonic (Goulet et al., 2014) earthquakes. In this study, we evaluate over 50 GMMs using two well-established probabilistic scoring methods: log likelihood (LLH) (Scherbaum et al., 2004; 2009) and multivariate LLH (MLLH) (Mak et al., 2017). The LLH approach compares the mean and standard deviation (σ) of the observed and modeled ground motions. The MLLH approach advances the LLH method by considering the variability (φ,τ) of multiple correlated variables namely intra- (within) and inter- (between) event residuals.
For the probabilistic scoring GMM evaluation methods (LLH, MLLH), we compute horizontal component peak ground acceleration (PGA) and 1s period pseudo spectral acceleration (PSA1.0) total residuals using GMM software (nshmp-haz) recently implemented by the USGS National Seismic Hazard Model Project (NSHMP). We observe from LLH and MLLH scores that: 1) newer GMMs with lower standard deviations (σ,φ,τ) score better than older GMMs with higher published uncertainty; 2) 2014 CEUS GMMs score better for CEUS tectonic earthquakes than induced earthquakes; 3) NGA-West2, G17 and A15 GMMs score well for CEUS induced earthquake ground motions; and 4) NGA-East GMMs score well for CEUS tectonic earthquake ground motions. We also use the LLH and MLLH scores to evaluate GMM weights applied in past USGS seismic hazard forecasts and to inform weighting of GMMs in future seismic hazard forecasts.