Ground-Motion Modeling of Hayward Fault Scenario Earthquakes, Part I: Construction of the Suite of Scenarios

Aagaard, Brad T.; Schwartz, David P.; Ponce, David A.; Graymer, R. W.

doi:10.1785/0120090324

Cited by 39 publications

(61 citation statements)

References 70 publications

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“…Pitarka (2004, 2010) modeled the effect of these depth-dependent properties by increasing slip rise time and decreasing rupture speed for fault rupture in the upper few kilometers of the crust. Similar depth-dependent effects were included in kinematic Hayward fault scenarios (Aagaard et al, 2010) and the original ShakeOut parameterization (Graves et al, 2011). More recent spontaneous dynamic calculations for a large San Andreas rupture have included the effects of off-fault plasticity, which suggest these near-fault nonlinear effects can significantly reduce ground shaking levels (Roten et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Subsurface Geometry of the San Andreas Fault in Southern California: Results from the Salton Seismic Imaging Project (SSIP) and Strong Ground Motion Expectations

Fuis¹,

Bauer²,

Goldman³

et al. 2017

Bulletin of the Seismological Society of America

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The San Andreas fault (SAF) is one of the most studied strike-slip faults in the world; yet its subsurface geometry is still uncertain in most locations. The Salton Seismic Imaging Project (SSIP) was undertaken to image the structure surrounding the SAF and also its subsurface geometry. We present SSIP studies at two locations in the Coachella Valley of the northern Salton trough. On our line 4, a fault-crossing profile just north of the Salton Sea, sedimentary basin depth reaches 4 km southwest of the SAF. On our line 6, a fault-crossing profile at the north end of the Coachella Valley, sedimentary basin depth is ∼2-3 km and centered on the central, most active trace of the SAF. Subsurface geometry of the SAF and nearby faults along these two lines is determined using a new method of seismic-reflection imaging, combined with potential-field studies and earthquakes. Below a 6-9 km depth range, the SAF dips ∼50°-60°NE, and above this depth range it dips more steeply. Nearby faults are also imaged in the upper 10 km, many of which dip steeply and project to mapped surface fault traces. These secondary faults may join the SAF at depths below about 10 km to form a flower-like structure. In Appendix D, we show that rupture on a northeast-dipping SAF, using a single plane that approximates the two dips seen in our study, produces shaking that differs from shaking calculated for the Great California ShakeOut, for which the southern SAF was modeled as vertical in most places: shorter-period (T < 1 s) shaking is increased locally by up to a factor of 2 on the hanging wall and is decreased locally by up to a factor of 2 on the footwall, compared to shaking calculated for a vertical fault.

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Section: Discussionmentioning

confidence: 99%

Subsurface Geometry of the San Andreas Fault in Southern California: Results from the Salton Seismic Imaging Project (SSIP) and Strong Ground Motion Expectations

Fuis¹,

Bauer²,

Goldman³

et al. 2017

Bulletin of the Seismological Society of America

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“…(This simulation is scenario hs_r02_hypoF_vr92_tr15 in Aagaard et al [2010].) The exponentially weighted moving average (using a variety of decay rates) of data from select stations for these earthquakes is shown in Figure 3.…”

Section: Datamentioning

confidence: 99%

Real‐time inversions for finite fault slip models and rupture geometry based on high‐rate GPS data

et al. 2014

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We present an inversion strategy capable of using real‐time high‐rate GPS data to simultaneously solve for a distributed slip model and fault geometry in real time as a rupture unfolds. We employ Bayesian inference to find the optimal fault geometry and the distribution of possible slip models for that geometry using a simple analytical solution. By adopting an analytical Bayesian approach, we can solve this complex inversion problem (including calculating the uncertainties on our results) in real time. Furthermore, since the joint inversion for distributed slip and fault geometry can be computed in real time, the time required to obtain a source model of the earthquake does not depend on the computational cost. Instead, the time required is controlled by the duration of the rupture and the time required for information to propagate from the source to the receivers. We apply our modeling approach, called Bayesian Evidence‐based Fault Orientation and Real‐time Earthquake Slip, to the 2011 Tohoku‐oki earthquake, 2003 Tokachi‐oki earthquake, and a simulated Hayward fault earthquake. In all three cases, the inversion recovers the magnitude, spatial distribution of slip, and fault geometry in real time. Since our inversion relies on static offsets estimated from real‐time high‐rate GPS data, we also present performance tests of various approaches to estimating quasi‐static offsets in real time. We find that the raw high‐rate time series are the best data to use for determining the moment magnitude of the event, but slightly smoothing the raw time series helps stabilize the inversion for fault geometry.

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“…Based in part on this evidence, several new models have been developed with an underlying principle that the secant rupture speed (the average rupture speed from the hypocenter to a given subfault location) or local rupture speed is correlated with slip on the fault (e.g., Guatteri et al, 2004;Liu et al, 2006;Aagaard, Graves, Rodgers, et al, 2010;Aagaard, Graves, Schwartz, et al, 2010;Graves and Pitarka, 2010;Song and Somerville, 2010;Song et al, 2014). Others correlate rupture initiation with slip (e.g., Graves and Pitarka, 2010).…”

Section: Rupture Speed Distributionmentioning

confidence: 99%

“…Brune (1970) proposed one of the earliest earthquake source models, in which near-and far-field displacement spectra are calculated from a fault dislocation model accelerated by an effective stress. Significant progress has been made in kinematic source modeling since then, with the help of data collected by modern seismic networks (Zeng et al, 1994;Hartzell et al, 1999;Somerville et al, 1999;Mai and Beroza, 2002;Nielsen and Madariaga, 2003;Guatteri et al, 2004;Tinti et al, 2005;Lavallée et al, 2006;Liu et al, 2006;Aagaard, Graves, Rodgers, et al, 2010;Aagaard, Graves, Schwartz, et al, 2010;Graves and Pitarka, 2010). We start with a brief description of current approaches to prescribing the three source parameters for each subevent.…”

Section: Introductionmentioning

confidence: 99%

A Laboratory Earthquake‐Based Stochastic Seismic Source Generation Algorithm for Strike‐Slip Faults and its Application to the Southern San Andreas Fault

Siriki¹,

Bhat²,

Lü³

et al. 2015

Bulletin of the Seismological Society of America

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There is a sparse number of credible source models available from largemagnitude past earthquakes. A stochastic source-model-generation algorithm thus becomes necessary for robust risk quantification using scenario earthquakes. We present an algorithm that combines the physics of fault ruptures as imaged in laboratory earthquakes with stress estimates on the fault constrained by field observations to generate stochastic source models for large-magnitude (M w 6.0-8.0) strike-slip earthquakes. The algorithm is validated through a statistical comparison of synthetic groundmotion histories from a stochastically generated source model for a magnitude 7.90 earthquake and a kinematic finite-source inversion of an equivalent magnitude past earthquake on a geometrically similar fault. The synthetic dataset comprises threecomponent ground-motion waveforms, computed at 636 sites in southern California, for 10 hypothetical rupture scenarios (five hypocenters, each with two rupture directions) on the southern San Andreas fault. A similar validation exercise is conducted for a magnitude 6.0 earthquake, the lower magnitude limit for the algorithm. Additionally, ground motions from the M w 7.9 earthquake simulations are compared against predictions by the Campbell-Bozorgnia Next Generation Attenuation relation, as well as the ShakeOut scenario earthquake. The algorithm is then applied to generate 50 source models for a hypothetical magnitude 7.9 earthquake originating at Parkfield, California, with rupture propagating from north to south (toward Wrightwood), similar to the 1857 Fort Tejon earthquake. Using the spectral element method, three-component ground-motion waveforms are computed in the Los Angeles basin for each scenario earthquake and the sensitivity of ground-shaking intensity to seismic source parameters (such as the percentage of asperity area relative to the fault area, rupture speed, and rise time) is studied.Online Material: Figures of source and simulated peak ground motions for an M w 6.05 scenario on the southern San Andreas fault, and table of V S30 and basin depth at stations.

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Ground-Motion Modeling of Hayward Fault Scenario Earthquakes, Part I: Construction of the Suite of Scenarios

Cited by 39 publications

References 70 publications

Subsurface Geometry of the San Andreas Fault in Southern California: Results from the Salton Seismic Imaging Project (SSIP) and Strong Ground Motion Expectations

Subsurface Geometry of the San Andreas Fault in Southern California: Results from the Salton Seismic Imaging Project (SSIP) and Strong Ground Motion Expectations

Real‐time inversions for finite fault slip models and rupture geometry based on high‐rate GPS data

A Laboratory Earthquake‐Based Stochastic Seismic Source Generation Algorithm for Strike‐Slip Faults and its Application to the Southern San Andreas Fault

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