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

机译:基于实验室地震的走向滑动断层随机震源生成算法及其在南部圣安德烈亚斯断层中的应用

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摘要

There is a sparse number of credible source models available from large‐magnitude 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 ground‐motion 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 three‐component 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.
机译:过去发生的大震级提供的可信源模型数量很少。因此,对于使用情景地震进行可靠的风险量化而言,随机源模型生成算法变得很有必要。我们提出了一种算法,该算法将实验室地震中成像的断层破裂的物理性质与现场观测所约束​​的断层上的应力估计值结合起来,以生成大震级(M_w 6.0–8.0)走滑地震的随​​机震源模型。该算法通过对来自随机产生的震源模型的合成地面运动历史记录进行统计比较进行验证,该震源模型针对的是7.90级地震,并且在几何相似的断层上,经过地震后的等效震级进行了运动学有限源反演。综合数据集包括在加利福尼亚南部的636个地点计算的三分量地震动波形,用于圣安德烈亚斯南部断层的10个假想破裂场景(五个震源,每个都有两个破裂方向)。对于6.0级地震(该算法的下限),进行了类似的验证练习。此外,将M_w 7.9地震模拟中的地震动与Campbell-Bozorgnia下一代衰减关系以及ShakeOut情景地震的预测进行了比较。然后将该算法应用于为假设的7.9级地震(起源于加利福尼亚州帕克菲尔德)生成50个震源模型,其破裂从北向南(向Wrightwood传播),类似于1857年的特洪堡地震。使用频谱元素方法,针对每种情况地震,在洛杉矶盆地中计算出三分量地震动波形,以及地震动强度对震源参数(例如,相对于断层区域的粗糙区域的百分比,研究破裂速度和上升时间。

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