A Model for Single-Station Standard Deviation Using Data from Various Tectonic Regions

The 2011 moment magnitude (M w ) 9.0 Tohoku-Oki Japan earthquake occurred in a region where giant megathrust earthquakes were not expected. This earthquake proved the difficulty in assessing seismic hazard by relying mainly on information from historical and instrumental seismicity. To help improve the seismic-hazard assessment for such rare events, we propose a methodology to estimate the slip distribution of future megathrust earthquakes based on a model of interseismic coupling distribution in subduction margins, as well as information of historical earthquakes, and apply the method to the central Peru region, Lima. The slip model obtained from geodetic data represents the large scale features of asperities within the megathrust, which is appropriate for simulation of long-period waves and tsunami modeling. For the simulation of a broadband strong ground motion, we add small scale heterogeneities to the source slip to be able to simulate high frequencies. To achieve this purpose, we propose broadband source models constructed by adding short-wavelength slip distributions obtained from a Von Karman power spectral density function, to the slip model inferred from interseismic geodetic data. Using these slip models and assuming several hypocenter locations, we calculate a set of strong ground motions for Lima and incorporate site effects obtained from microtremors surveys and geotechnical data. Our simulated average pseudospectral accelerations (period 0.3 s) are above 1:5g for wide areas in Lima, which may be critical in terms of damage of low-to midrise masonry and reinforced concrete buildings, which characterize the majority of buildings in Lima.

Section: Strong-motion Simulations In Lima Includingsupporting

confidence: 80%

Scenario Source Models and Strong Ground Motion for Future Mega‐earthquakes: Application to Lima, Central Peru

Pulido¹,

Aguilar²,

Tavera³

et al. 2015

“…Such analysis is motivated by the fact that the random variability associated to groundmotion predictions (sigma) strongly influences the seismichazard curve computation, in particular for long return periods (Bommer and Abrahamson, 2006). The understanding of the physical causes of ground-motion variability has not been explored yet, and topography is one of the suggested explanations (e.g., Rai et al, 2012;Rodriguez-Marek et al, 2013). Our simulations provide a unique opportunity to analyze the spatial variations of the ground-motion variability and discuss topography proxies that may help to identify sites where ground motions are more variable and highly dependent on source-site configurations.…”

Section: Introductionmentioning

confidence: 99%

Frequency‐Scaled Curvature as a Proxy for Topographic Site‐Effect Amplification and Ground‐Motion Variability

Maufroy¹,

Cruz‐Atienza²,

Cotton³

et al. 2014

We introduce a new methodology to predict the topographic site-effect amplification. Ground motions obtained from a large database of 3D earthquake simulations show that the curvature of the Earth's surface, defined as the second spatial derivative of the elevation map, is correlated with the topographic site amplification. The highest correlation between the frequency-dependent topographic amplification and the topographic curvature is reached when the curvature is smoothed over a characteristic length equal to the S wavelength divided by two (i.e., frequency-scaled curvature [FSC]). This implies the amplification is caused by topographic features for which horizontal dimensions are similar to half of the S wavelength. The largest ground-motion variabilities are found at sites located on slopes and on the largest summits, whereas intermediate variabilities occur over narrow ridges and a stable behavior in the bottom valleys. The FSC proxy allows the identification of topographic features with similar characteristic dimensions and probabilistic estimates of amplification values accounting on the variability of ground motions due to source-site interactions. Amplification estimates using the FSC proxy are robust and easily computed from digital elevation maps provided that reasonable values of S-wave velocities are available in the area of interest.

“…A very recent work by Rodriguez-Marek et al (2013) addresses the issue of the variation of single station sigma with respect to region, magnitude, and distance. First, it is remarkable from their work that the mean values of ϕ SS appear to be stable (average ϕ SS ≈ 0:45) across the different regions (California, Taiwan, Japan, Switzerland, and Turkey).…”

Section: Introductionmentioning

confidence: 99%

Is Ground‐Motion Variability Distance Dependent? Insight from Finite‐Source Rupture Simulations

Imtiaz¹,

Causse²,

Chaljub³

et al. 2015

The ground-motion variability sigma is a fundamental component in probabilistic seismic-hazard assessment because it controls the hazard level at very low probabilities of exceedance. So far, most of the analyses based on empirical ground-motion prediction equations do not consider any distance dependency of sigma. This study aims to analyze the potential distance dependency of ground-motion variability, especially in the near-field region, where the variability is poorly constrained due to the lack of available records. We, therefore, investigate the distance dependency of sigma by performing numerical simulations of ground motion for some strike-slip events. Synthetic velocity seismograms (up to 3 Hz) have been generated from a suite of finite-source rupture models of past earthquakes. Green's functions were calculated for a 1D velocity structure using a discrete wavenumber technique (Bouchon, 1981). The within-event component of the ground-motion variability was then evaluated from the synthetic data as a function of distance. The simulations reveal that the within-event component of the ground motion shows a distance dependency, subject to the rupture type. For bilateral ruptures, the variability tends to increase with distance. On the contrary, in case of unilateral events, the variability decreases with distance.