Local and Moment Magnitude Analysis in the Ridgecrest Region, California: Impact on Interevent Ground-Motion Variability

Abstract

We investigate the dependence of event-specific ground-motion residuals in the Ridgecrest region, California. We focus on the impact of using either local (ML) or moment (Mw) magnitude, for describing the source scaling of a regional ground-motion model. To analyze homogeneous Mw, we compute the source spectra of about 2000 earthquakes in the magnitude range 2.5–7.1, by performing a nonparametric spectral decomposition. Seismic moments and corner frequencies are derived from the best-fit ω−2 source models, and stress drop is computed assuming standard circular rupture model. The Brune stress drop varies between 0.62 and 24.63 MPa (with median equal to 3.0 MPa), and values for Mw>5 are mostly distributed above the 90th percentile. The median scaled energy for Mw<5 is −4.57, and the low values obtained for the Mw 6.4 and 7.1 mainshocks (−5 and −5.2, respectively) agree with previous studies. We calibrate an ad hoc nonparametric ML scale for the Ridgecrest region. The main differences with the standard ML scale for California are observed at distances between 30 and 100 km, in which differences up to 0.4 magnitude units are obtained. Finally, we calibrate ground-motion models for the Fourier amplitude spectra, considering the ML and Mw scales derived in this study and the magnitudes extracted from Comprehensive Earthquake Catalog. The analysis of the residuals shows that ML better describes the interevent variability above 2 Hz. At intermediate frequencies (between about 3 and 8 Hz), the interevent residuals for the model based on Mw show a correlation with stress drop: this correlation disappears, when ML is used. The choice of the magnitude scale has an impact also on the statistical uncertainty of the median model: for any fixed magnitude value, the epistemic uncertainty is larger for ML below 1.5 Hz and larger for Mw above 1.5 Hz.

Resolving Differences in the Rupture Properties of M5 Earthquakes in California Using Bayesian Source Spectral Analysis

“…Priors for the corner frequency f c of each event depend on the event size but are assumed to follow a lognormal distribution derived from a corresponding stress drop prior with a median stress drop of 5 MPa and standard deviation of 0.4 in log10 units. These values are in line with recent work(Baltay et al, 2013;Bindi, Zaccarelli, & Kotha, 2020;…”

Does Earthquake Stress Drop Increase With Depth in the Crust?

“…They too should be considered potentially biased in the same manner as the spectral decomposition analyses. For example, at Ridgecrest Bindi, Zaccarelli, et al (2020) obtained a similar apparent depth dependence to stress drop as Trugman (2020) and also did not explicitly consider an increase in Q with source depth. Bindi et al (2021) performed a subsequent study and identified a combination of depth and distance varying attenuation and stress drop increasing with depth that were able to fit the observed spectra.…”

“…Parker et al (2020) investigated the performance of ground motion prediction equations on the aftershocks of the 2019 Ridgecrest earthquake and observed that the deeper earthquakes exhibited higher ground acceleration, consistent with their having higher stress drop. Like the studies by Trugman (2020) and Bindi, Zaccarelli, et al (2020) of the same sequence, they did not include any dependence of attenuation on source depth in their analysis. In empirical ground motion prediction, it is not essential to distinguish whether source or path effects cause a trend when interpolating within a data set, but it is required to extrapolate predictions to different regions, or larger magnitude earthquakes (e.g., Bommer et al, 2007).…”

Spatiotemporal Variability of Earthquake Source Parameters at Parkfield, California, and Their Relationship With the 2004 M6 Earthquake

“…They also showed that the frequency range of the Southern California regional seismic network data is insufficient to resolve source scaling and absolute values of stress drop, and their variation, independently. This limitation is supported by the contrasting results of Trugman (2020), Bindi, Zaccarelli, and Kotha (2020), Bindi et al. (2021), and Abercrombie et al.…”

“…They also showed that the frequency range of the Southern California regional seismic network data is insufficient to resolve source scaling and absolute values of stress drop, and their variation, independently. This limitation is supported by the contrasting results of Trugman (2020), Bindi, Zaccarelli, and Kotha (2020), Bindi et al (2021), and Abercrombie et al (2021) for aftershocks of the 2019 Ridgecrest, California earthquake. Shearer and Abercrombie (2021) combined surface network with high-frequency deep borehole recordings to resolve the ambiguity near Cajon Pass.…”