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ABSTRACT Directivity, or the focusing of energy along the direction of an earthquake rupture, is a common property of earthquakes of all sizes and can cause increased hazard due to azimuthally dependent ground-motion amplification. For small earthquakes, the effects of directivity are generally less pronounced due to reduced rupture size, yet the directivity in small events can bias source property estimates and provide important insights into general regional faulting patterns. However, due to observational limitations, directivity is usually only measured and modeled for large events. As such, many studies of small earthquakes either ignore directivity altogether or assume a constant rupture direction for all events in a cluster. In our study, we apply a refined directivity fitting method constrained with two separate methods of source deconvolution to the dataset of aftershocks of the 2019 Ridgecrest earthquakes, which contain a large number of well-recorded small-to-mid sized earthquakes occurring in close proximity to each other. The revealed directivity of 100+ small (M 2.4–5) earthquakes is highly heterogeneous and primarily oblique to and away from the main fault strike, suggesting a complex postseismic stress redistribution. In addition, the energy focusing effect of directivity appears to bias the selection of high-quality data from stations in the direction of rupture, leading to average stress-drop increases of 50% if directivity is not accounted for. 

ABSTRACT Earthquake stress drop—a key parameter for describing the energetics of earthquake rupture—can be estimated in several different, but theoretically equivalent, ways. However, independent estimates for the same earthquakes sometimes differ significantly. We find that earthquake source complexity plays a significant role in why theoretically (for simple rupture models) equivalent methods produce different estimates. We apply time- and frequency-domain methods to estimate stress drops for real earthquakes in the SCARDEC (Seismic source ChAracteristics Retrieved from DEConvolving teleseismic body waves, Vallée and Douet, 2016) source time function (STF) database and analyze how rupture complexity drives stress-drop estimate discrepancies. Specifically, we identify two complexity metrics—Brune relative energy (BRE) and spectral decay—that parameterize an earthquake’s complexity relative to the standard Brune model and strongly correlate with the estimate discrepancies. We find that the observed systematic magnitude–stress-drop trends may reflect underlying changes in STF complexity, not necessarily trends in actual stress drop. Both the decay and BRE parameters vary systematically with magnitude, but whether this magnitude–complexity relationship is real remains unresolved. 

ABSTRACT We present initial findings from the ongoing Community Stress Drop Validation Study to compare spectral stress-drop estimates for earthquakes in the 2019 Ridgecrest, California, sequence. This study uses a unified dataset to independently estimate earthquake source parameters through various methods. Stress drop, which denotes the change in average shear stress along a fault during earthquake rupture, is a critical parameter in earthquake science, impacting ground motion, rupture simulation, and source physics. Spectral stress drop is commonly derived by fitting the amplitude-spectrum shape, but estimates can vary substantially across studies for individual earthquakes. Sponsored jointly by the U.S. Geological Survey and the Statewide (previously, Southern) California Earthquake Center our community study aims to elucidate sources of variability and uncertainty in earthquake spectral stress-drop estimates through quantitative comparison of submitted results from independent analyses. The dataset includes nearly 13,000 earthquakes ranging from M 1 to 7 during a two-week period of the 2019 Ridgecrest sequence, recorded within a 1° radius. In this article, we report on 56 unique submissions received from 20 different groups, detailing spectral corner frequencies (or source durations), moment magnitudes, and estimated spectral stress drops. Methods employed encompass spectral ratio analysis, spectral decomposition and inversion, finite-fault modeling, ground-motion-based approaches, and combined methods. Initial analysis reveals significant scatter across submitted spectral stress drops spanning over six orders of magnitude. However, we can identify between-method trends and offsets within the data to mitigate this variability. Averaging submissions for a prioritized subset of 56 events shows reduced variability of spectral stress drop, indicating overall consistency in recovered spectral stress-drop values. 

Abstract Understanding and modeling variability of ground motion is essential for building accurate and precise ground motion prediction equations, which can net site‐specific characterization and reduced hazard levels. Here, we explore the spatial variability in peak ground velocity (PGV) at Sage Brush Flats along the San Jacinto Fault in Southern California. We use data from a dense array (0.6 × 0.6 km², 1,108 geophones, station spacings 10–30 m) deployed in 2014 for ~1 month. These data offer an opportunity to study small‐scale variability in this region. We examine 38 earthquakes (2 ≤ M_L ≤ 4.2) within 200 km of the array. Fault strands and a small basin impact the ground motions, producing PGV variations up to 22% of the mean and a 40% reduction inPandSwave near‐surface velocities. We find along‐fault rupture directivity, source, and path effects can increase PGVs by 167%. Surface PGV measurements exceed the colocated borehole station (depth at 148 m) PGV by factors of 3–10, confirming the impact on PGV from near‐surface fault structures, basins, topography, and amplifications from soft sediments. Consistently, we find high PGVs within the basin structure. A pair of colocated GaM_L2.6 events produce repeatable PGV values with similar spatial patterns. The average corner frequencies of these two events are 11–16 Hz, and viable measurements of stress drop can differ by 6.45 MPa. Within this small array, the PGV values are variable implying spatial extrapolation of PGV to regions of known faults and basins, even across a small area, should be done with caution.