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ABSTRACT Stress drop is a fundamental parameter related to earthquake source physics, but is hard to measure accurately. To better understand how different factors influence stress-drop measurements, we compare two different methods using the Ridgecrest stress-drop validation data set: spectral decomposition (SD) and spectral ratio (SR), each with different processing options. We also examine the influence of spectral complexity on source parameter measurement. Applying the SD method, we find that frequency bandwidth and time-window length could influence spectral magnitude calibration, while depth-dependent attenuation is important to correctly map stress-drop variations. For the SR method, we find that the selected source model has limited influence on the measurements; however, the Boatwright model tends to produce smaller standard deviation and larger magnitude dependence than the Brune model. Variance reduction threshold, frequency bandwidth, and time-window length, if chosen within an appropriate parameter range, have limited influence on source parameter measurement. For both methods, wave type, attenuation correction, and spectral complexity strongly influence the result. The scale factor that quantifies the magnitude dependence of stress drop show large variations with different processing options, and earthquakes with complex source spectra deviating from the Brune-type source models tend to have larger scale factor than earthquakes without complexity. Based on these detailed comparisons, we make a few specific suggestions for data processing workflows that could help future studies of source parameters and interpretations. 

ABSTRACT The recorded seismic waveform is a convolution of event source term, path term, and station term. Removing high-frequency attenuation due to path effect is a challenging problem. Empirical Green’s function (EGF) method uses nearly collocated small earthquakes to correct the path and station terms for larger events recorded at the same station. However, this method is subject to variability due to many factors. We focus on three events that were well recorded by the seismic network and a rapid response distributed acoustic sensing (DAS) array. Using a suite of high-quality EGF events, we assess the influence of time window, spectral measurement options, and types of data on the spectral ratio and relative source time function (RSTF) results. Increased number of tapers (from 2 to 16) tends to increase the measured corner frequency and reduce the source complexity. Extended long time window (e.g., 30 s) tends to produce larger variability of corner frequency. The multitaper algorithm that simultaneously optimizes both target and EGF spectra produces the most stable corner-frequency measurements. The stacked spectral ratio and RSTF from the DAS array are more stable than two nearby seismic stations, and are comparable to stacked results from the seismic network, suggesting that DAS array has strong potential in source characterization. 

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. 

SUMMARY In this study, I demonstrate that distributed acoustic sensing (DAS) raw strain rate data can directly be used to estimate spectral source parameters through an Empirical Green's Function (EGF) deconvolution analysis. Previously, DAS had been widely used in passive seismology to image the subsurface and analyze ground motion variations by converting strain or strain rate to particle velocity or acceleration prior to analysis. In this study, spectral analysis is applied to the PoroTomo joint DAS and seismic Nodal array in the Brady Hot Springs geothermal field to obtain source parameters for two M4 earthquakes via EGF analysis, where nearly collocated smaller events are used as an EGF to remove path and site effects. The EGF workflow is applied to raw DAS strain rate data without conversion to particle velocities and raw Nodal seismic data. The DAS and Nodal results are very consistent with similar features of spectral ratios, corner frequencies and moment ratios for the same event pairs. The uncertainty due to stacked spectral measurement is much lower on the DAS array, suggesting better stability of spectral shape measurement, possibly due to the much denser spatial sampling. The uncertainty due to model fitting is similar between DAS and Nodal arrays with slightly lower uncertainty on the DAS array. These observations demonstrate potential for directly using the strain rate measurements from DAS arrays for earthquake source characterizations. 

Abstract High-resolution passive seismic imaging of shallow subsurface structures is often challenged by the scarcity of coherent body-wave energy in ambient noise recorded at surface stations. We show that the autocorrelation (AC) of teleseismic P-wave coda extracted from just one month of continuous recording at 5 Hz geophones can overcome this limitation. We apply this method to investigate the longitudinal subsurface bedrock structure of Unaweep Canyon—a paleovalley in western Colorado (United States) with complex evolution. Both fluvial and glacial processes have been proposed to explain the canyon’s genesis and morphology. The teleseismic P-wave coda AC retrieves zero-offset reflections from the shallow (200–500 m depth) basement interface at 120 stations along a 5 km long profile. In addition, we invert interferometrically retrieved surface-wave dispersion for the shear-wave structure of the sedimentary fill. Combined interpretation of these results and other geophysical and well data suggests an overdeepened basement geometry most consistent with glacial processes. 

SUMMARY It is well known that large earthquakes often exhibit significant rupture complexity such as well separated subevents. With improved recording and data processing techniques, small earthquakes have been found to exhibit rupture complexity as well. Studying these small earthquakes offers the opportunity to better understand the possible causes of rupture complexities. Specifically, if they are random or are related to fault properties. We examine microearthquakes (M < 3) in the Parkfield, California, area that are recorded by a high-resolution borehole network. We quantify earthquake complexity by the deviation of source time functions and source spectra from simple circular (omega-square) source models. We establish thresholds to declare complexity, and find that it can be detected in earthquakes larger than magnitude 2, with the best resolution above M2.5. Comparison between the two approaches reveals good agreement (>90 per cent), implying both methods are characterizing the same source complexity. For the two methods, 60–80 per cent (M 2.6–3) of the resolved events are complex depending on the method. The complex events we observe tend to cluster in areas of previously identified structural complexity; a larger fraction of the earthquakes exhibit complexity in the days following the Mw 6 2004 Parkfield earthquake. Ignoring the complexity of these small events can introduce artefacts or add uncertainty to stress drop measurements. Focusing only on simple events however could lead to systematic bias, scaling artefacts and the lack of measurements of stress in structurally complex regions.