Real-Time Estimation of Fault Rupture Extent Using Near-Source versus Far-Source Classification

Yamada, Masumi; Heaton, Thomas H.; Beck, James L.

doi:10.1785/0120060243

Cited by 44 publications

(23 citation statements)

References 40 publications

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“…Unlike PGD, which can be related to slip on the fault, it is difficult to use a fundamental, physical understanding of faulting and wave propagation to predict PGA. Yamada et al (2007) show that the distribution for near-source PGA seems to be compatible with the hypothesis that the total radiation of high-frequency energy scales with the rupture area. This hypothesis is consistent with the observation that the PGA weakly depends on the slip on the fault.…”

supporting

confidence: 80%

“…The density of sites per near-source area is not constant for all events, and the sites do not evenly represent all near-source regions. For nine of the ten earthquakes used in this study, Yamada et al (2007) showed the sites in comparison to the fault rupture surface Moment magnitude (M w ) and focal depth are cited from the Harvard Centroid Moment Tensor solution. The preliminary determination of the epicenter is used for the focal depth.…”

Section: Datamentioning

confidence: 99%

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Statistical Features of Short-Period and Long-Period Near-Source Ground Motions

Yamada

Olsen

Heaton

2009

Bulletin of the Seismological Society of America

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This study collects recorded ground motions from the near-source region of large earthquakes and considers to what extent this historic record can inform expectations of future ground motions at similar sites. The distribution of observed peak ground acceleration (PGA) is well approximated by the lognormal distribution, and we expect the observed distribution to remain unchanged with the addition of data from future earthquakes. However, the distribution of peak ground displacements (PGD) will likely change after a well-recorded large earthquake. Specifically we expect future observations of PGD greater than those previously recorded. We use seismic scaling relations to motivate the expected distribution of PGD as uniform on the logarithmic scale, or at least fat-tailed. Because PGA does not scale with fault rupture area or slip on the fault, there are no such scaling relations to predict the observed distribution of PGA. The observed records show that there is essentially no correlation between PGD and PGA for near-source ground motions from large events. The large uncertainty in a future value of PGD in the near-source region of a large earthquake exists despite the ability of Earth scientists to accurately model long-period ground motions. In contrast, the relative certainty in a future value of PGA exists despite the inability to model short-period ground motions reliably. The stability of the observed distribution of PGA with respect to new ground-motion records enables us to predict the distribution of future PGA and to calculate the probability of exceeding the largest recorded PGA.

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supporting

confidence: 80%

Section: Datamentioning

confidence: 99%

Statistical Features of Short-Period and Long-Period Near-Source Ground Motions

Yamada

Olsen

Heaton

2009

Bulletin of the Seismological Society of America

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“…The latter is particularly needed for the real-time estimation of fault rupture length during large earthquakes (M > 6:5; e.g., Yamada et al, 2007). We define the following classification: an output of 1 is assigned if the pick was produced by noise; an output of 1 is assigned if the detected event is an earthquake with epicentral distance of Δ km ≤ 15M; an output of 0 is assigned if the detected event is an earthquake with Δ km ≤ 15M.…”

Section: Methodsmentioning

confidence: 99%

Rapid Estimation of Earthquake Source and Ground-Motion Parameters for Earthquake Early Warning Using Data from a Single Three-Component Broadband or Strong-Motion Sensor

Böse¹,

Heaton²,

Hauksson³

2012

Bulletin of the Seismological Society of America

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We propose a new algorithm to rapidly determine earthquake source and ground-motion parameters for earthquake early warning (EEW). This algorithm uses the acceleration, velocity, and displacement waveforms of a single three-component broadband (BB) or strong-motion (SM) sensor to perform real-time earthquake/noise discrimination and near/far source classification. When an earthquake is detected, the algorithm estimates the moment magnitude M, epicentral distance Δ, and peak ground velocity (PGV) at the site of observation. The algorithm was constructed by using an artificial neural network (ANN) approach. Our training and test datasets consist of 2431 three-component SM and BB records of 161 crustal earthquakes in California, Japan, and Taiwan with 3:1 ≤ M ≤ 7:6 at Δ≤ 115 km. First estimates become available at t 0 0:25 s after the P pick and are regularly updated. We find that displacement and velocity waveforms are most relevant for the estimation of M and PGV, while acceleration is important for earthquake/noise discrimination. Including site corrections reduces the errors up to 10%. The estimates improve by an additional 10% if we use both the vertical and horizontal components of recorded ground motions. The uncertainties of the predicted parameters decrease with increasing time window length t 0 ; larger magnitude events show a slower decay of these uncertainties than small earthquakes. We compare our approach with the τ c algorithm and find that our prediction errors are around 60% smaller. However, in general there is a limitation to the prediction accuracy an EEW system can provide if based on single-sensor observations.

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“…It may be sufficient to know that an event is M 6.5 or larger and broadcast a warning. However, methodologies to map the finite ruptures of these large (M > 7) earthquakes could also be developed and would enhance system performance (Yamada et al 2007;Yamada and Heaton 2008;Zollo et al 2009). …”

Section: Using the P Wavementioning

confidence: 99%