ABSTRACT Stress drop is a fundamental parameter in ground-motion modeling and seismic hazard assessment, but spectral estimates are subject to considerable uncertainties. A variety of factors cause different methods to yield different results, including the complexity of the seismic source, the assumptions inherent in the models used, the limited range of frequencies available, and the inherent difficulty in removing the propagation effects along the wave path. A primary challenge is determining whether the observed variations in spectral stress-drop estimates represent characteristics of the seismic source or the propagation path. We compare the performance of two methods applied to the 2019 Ridgecrest, California, earthquake sequence, each of which addresses the trade-offs between propagation and source in different ways. The first method, referred to as the spectral-fitting approach, operates on the hypothesis that the path effects remain constant across the spatial and temporal range of the sources under investigation. This approach assumes a level of uniformity in the propagation effects that simplifies the analysis. The second method, referred to as the spectral ratio approach, is based on the hypothesis that a small, collocated event will experience identical propagation effects to the earthquake of interest, potentially accounting for smaller scale variation in propagation effects. Our comparison reveals that the choice of method is not only influenced by the specifics of the data and the seismic events but also significantly constrained by the geological heterogeneity and consequent spatial variability of site and propagation effects in the study area. If an approach involves assuming a homogeneous attenuation structure, any spatial variation in attenuation structure will lead to this variation being incorrectly mapped into apparent source stress-drop variations. Understanding the local geology and structural heterogeneity, combined with using methods with contrasting underlying assumptions are good approaches to improving the reliability of estimated spectral stress drops.
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