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

Trugman, Daniel T.

doi:10.1029/2021jb023526

Cited by 14 publications

(8 citation statements)

References 156 publications

(273 reference statements)

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“…The spatial variations in median stress drop seen in Figures 12 and 14 are consistent with recent results for 14 M five earthquakes in southern California computed using an S-wave spectral ratio approach and Bayesian probability theory (Trugman, 2022). For example, three M 5.3-5.8 earthquakes in the Salton Trough region have estimated stress drops of 2.5, 3.6, and 11.1 MPa, whereas three M 5.2-5.4 earthquakes near the San Jacinto Fault have estimated stress drops of 30.6, 38.5, and 81.7 MPa.…”

Section: Comparison With Similar Prior Studiessupporting

confidence: 87%

Improved Stress Drop Estimates for M 1.5 to 4 Earthquakes in Southern California From 1996 to 2019

2022

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We estimate Brune‐type stress drops for over 70,000 southern California M 1.5–4 earthquakes from 1996 to 2019 using a P‐wave spectral decomposition approach. Based on our recent work documenting hard‐to‐resolve trade‐offs between absolute stress drop, stress drop scaling with moment, high‐frequency falloff rate, and empirical corrections for path and attenuation terms, we adopt a new approach in which the average corner frequency of the smallest earthquakes within a short distance from each target event is fixed to a constant value. This removes any true coherent spatial variations in stress drops among the smallest events but ensures that any spatial variations seen in larger event stress drops are real and not an artifact of inaccurate path corrections. Applying this approach across southern California, we document spatial variations in stress drop that agree with previous work, such as lower‐than‐average stress drops in the Salton Trough, as well as small‐scale stress drop variations along many faults and aftershock sequences. We observe an apparent increase in Brune‐type stress drop with moment for M 3–4 earthquakes, but their spectra can be fit equally well with self‐similar models with a high‐frequency falloff rate shallower than f−2.

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Section: Comparison With Similar Prior Studiessupporting

confidence: 87%

Improved Stress Drop Estimates for M 1.5 to 4 Earthquakes in Southern California From 1996 to 2019

2022

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“…The aftershock sequence is comprised of a mixture of normal and strike‐slip faulting events, with shallower events tending to produce lower stress drops than events occurring deeper along the fault interface. While this latter finding may be a signature of the normal faulting regime (where the maximum principal stress is oriented vertically), it is also broadly consistent with recent findings in transpressional settings in California (Bindi et al., 2021; Trugman, 2020, 2022). The mainshock rupture did not reach the surface, an observation that has fundamental importance both for the generation of strong ground motion and for the paleoseismic interpretation of event sequences.…”

Section: Discussionsupporting

confidence: 90%

“…Indeed, if we examine the posterior distribution of individual events (Figure S5 in Supporting Information ), we find that there is negligible tradeoff between corner frequency and attenuation, as the latter is constrained by a larger ensemble of events in any given cluster. There is no discernible directivity signature in the amplitude spectra of the mainshock (Figure S6 in Supporting Information ), which is consistent with the bilateral spatial pattern of aftershocks (Trugman, 2022). However, the azimuthal station coverage for the mainshock is not optimal for a frequency‐domain directivity analysis; it is possible for example, the mainshock is composed of multiple subevents with preferred rupture directions (e.g., Wang et al., 2023).…”

Section: Resultssupporting

confidence: 76%

“…To estimate these parameters, we use the Bayesian parameter estimation code PyMC (Salvatier et al., 2016), which uses an No U‐Turns formulation of the Hamiltonian Monte Carlo algorithm to efficiently sample high‐dimensional parameter spaces (Hoffman & Gelman, 2011). We define weakly informative priors for all source parameters that depend on the magnitude of the event, but also account for both the uncertainty in the magnitude itself as well as intrinsic variability in stress drop from event to event (Trugman, 2022). We also include a station term in the moment estimation to allow for variations in site amplification from site to site.…”

Section: Methodsmentioning

confidence: 99%

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The Rocks That Did Not Fall: A Multidisciplinary Analysis of Near‐Source Ground Motions From an Active Normal Fault

et al. 2023

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Earthquake seismology is an increasingly data-rich science, with tens of thousands of sensors recording the motions of the Earth in near-real time. Despite these rich new data sets and the opportunities they present (Arrowsmith et al., 2022), we still lack several key forms of observations needed to advance our understanding of earthquake rupture processes and the hazards they produce. One of the most acute challenges of this type relates to the paucity of high-quality, on-scale recordings close to large ruptures, especially within normal faulting sequences. Large earthquakes are thankfully rare, and few seismometers manage to capture the fine-scale details of the rupture process from a close vantage point (10 km or less). This observational deficit is more than just an academic concern and indeed has important implications for hazard, as the empirical ground motions models used in typical hazard applications must extrapolate beyond the support of their training data, while physics-based rupture models cannot be calibrated to match robust observations (Baker et al., 2021;Douglas & Edwards, 2016;Gerstenberger et al., 2020). A better resolution of near-source ground motions would be transformative for earthquake scientists, engineers, and communities near active fault systems.

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“…One of the most common techniques to capture directivity effects of smaller earthquakes is to measure the duration of the source pulse (called Apparent Source Time Function) at each site and then model it by a line source (e.g., Tomic et al., 2009; Folesky et al., 2016 among the others). Trugman (2022) has recently proposed an innovative Bayesian technique to isolate source spectra using a generalized spectral decomposition inversion and applied it to magnitude M5 in the Southern California to infer some source properties, including directivity effects.…”

Section: Introductionmentioning

confidence: 99%

Empirical Evidence of Frequency‐Dependent Directivity Effects From Small‐To‐Moderate Normal Fault Earthquakes in Central Italy

et al. 2022

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Directivity effect of an earthquake is the focusing of the radiated seismic wave energy due to the rupture propagation along the fault (Anderson, 2007;Ben-Menahem, 1961;Boatwright, 2007;Joyner, 1991). Earthquake directivity represents the analogue of the Doppler effect for sound and light waves (Douglas et al., 1988;Pacor, Gallovič, et al., 2016), which shifts the frequency of a moving oscillator to higher frequency when the oscillator moves toward an observer, and lower frequency when it moves away. This phenomenon, which represents one of the key factors in featuring the spatial distribution of the seismic shaking, produces azimuthal and spectral variations in the ground motion, that can be used to infer information on both the orientation of the fault plane and on the modes of rupture propagation (Abercrombie et al., 2017). Furthermore, the quantification of the directivity-induced amplifications has important consequences in seismic hazard assessment, in terms of ground-motion amplitude and associated variability (Chioccarelli & Iervolino, 2014;Spagnuolo et al., 2012). Although the importance of directivity is widely recognized for both seismological studies on earthquake sources and engineering applications, a clear picture of how strongly and how often it occurs is not yet available.

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

Abstract: Understanding how earthquakes nucleate, rupture, and arrest is central to earthquake science. It is well known that different earthquakes of a given size can exhibit a wide range of rupture properties, from radiated energy and source duration to rupture velocity and directivity (e.g.,

Cited by 14 publications

References 156 publications

Improved Stress Drop Estimates for M 1.5 to 4 Earthquakes in Southern California From 1996 to 2019

Improved Stress Drop Estimates for M 1.5 to 4 Earthquakes in Southern California From 1996 to 2019

The Rocks That Did Not Fall: A Multidisciplinary Analysis of Near‐Source Ground Motions From an Active Normal Fault

Empirical Evidence of Frequency‐Dependent Directivity Effects From Small‐To‐Moderate Normal Fault Earthquakes in Central Italy

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