Earthquake Rupture Simulation: thermo-mechanical models and validation with strong motion data
Earthquake Rupture Simulation: thermo-mechanical models and validation with strong motion data
批准号:
0810271
负责人:
Steven Day
金额:
$29.0万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-02-01 至 2013-01-31
中文摘要
大地震是罕见的、复杂的事件,其震源是深埋的,无法直接观测。此外,对于非常大量级的事件,存在相对较少的近源强震记录,即,这些地震对生命安全和建筑环境构成最大的潜在威胁。 经过充分验证的计算机模拟提供了一种手段,预测强地面运动的大地震,最好的地震物理和波传播模型的基础上。模拟可以帮助满足对基于性能的工程和结构分析中使用的场地适当的地面震动估计的迫切需求,鉴于美国西部城市高层建筑的建设热潮,这一需求尤其迫切。计算机模拟对于推进我们对地震的基本科学理解也至关重要,因为它们揭示了大规模效应如何从难以通过实验或传统理论分析研究的小规模相互作用中产生。 该项目正在开发数字地震模型,这些模型代表断层作用的主要机械和热过程,包括岩石接触加热和孔隙流体加压导致摩擦阻力减弱。 这些计算机模型还包括已建立的断层粗糙度和地球应力状态的统计表示,以及地质材料的强度极限(其对由断层激发的应力波扰动的振幅设定上限)。模型必须在大型计算机集群上高效运行(即,具有数千个处理器内核的系统),因此它们可以联合收割机(i)精确模拟断层带过程所需的小尺度分辨率与(ii)模拟大地震源和计算工程感兴趣距离处的地面运动所需的大的总体物理体积。该项目吸引了地球和计算科学家以及学员来解决这些建模挑战。该项目小组正在测试计算机模型,方法是将来自大型模拟套件的地面运动预测与来自实际地震记录的可比汇编进行比较。例如,地震运动参数(如频谱加速度和峰值地面速度)的分布(包括中值和统计分布)是这些观测试验的目标,这些参数是事件震级、场地距离和其他变量的函数。这些计算机模型可以在上述意义上验证,然后用于(i)模拟未来大地震场景的地震震动,(ii)改进简化的地面震动建模方法(所谓的运动学方法),以及(iii)改进工程设计中常规使用的经验地面运动模型,通过提供超出其具有来自数据的显著支持的范围的基于物理学的外推(即,到大幅度和小的源到站点距离)。
英文摘要
Large earthquakes are rare, complex events and their sources are deep-seated and inaccessible to direct observation. Moreover, relatively few near-source strong motion recordings exist for events of very large magnitude, i.e., those earthquakes that pose the greatest potential threat to life safety and the built environment. Well-validated computer simulations provide a means to forecast strong ground motion from large earthquakes, based upon the best available earthquake physics and wave propagation models. Simulations can help fill the pressing need for site-appropriate ground-shaking estimates for use in performance-based engineering and structural analysis, a requirement that is especially acute in light of the construction boom in very tall buildings in western U.S. cities. Computer simulations are also essential for advancing our basic scientific understanding of earthquakes, as they reveal how large-scale effects emerge from smaller-scale interactions that are difficult to study experimentally or via traditional theoretical analysis. This project is developing numerical earthquake models that represent the principal mechanical and thermal processes of faulting, including the weakening of frictional resistance by the heating of rock contacts and the pressurization of pore fluids. These computer models also incorporate established statistical representations of fault roughness and of the stress state in the earth, as well as the strength limits of geologic materials (which place upper bounds on the amplitude of the stress wave disturbances excited by faulting). The models must run efficiently on large computer clusters (i.e., those with thousands of processor cores), so that they can combine (i) the small-scale resolution required to accurately simulate fault-zone processes with (ii) the large overall physical volume required to simulate large earthquake sources and compute surface ground motion at distances of engineering interest. The project engages both earth and computational scientists and trainees to address these modeling challenges. The project team is testing the computer models by comparing the ground motion predictions from large suites of simulations with comparable compilations from actual earthquake recordings. For example, the distributions (including median and statistical spread) of ground motion parameters such as spectral acceleration and peak ground velocity, as functions of event magnitude, site distance, and other variables, are among the targets of these observational tests. Those computer models that can be validated in the above sense are then used to (i) simulate earthquake shaking from future large earthquake scenarios, (ii) develop improvements to simplified modeling methods for ground shaking (the so-called kinematic methods), and (iii) improve the empirical ground motion models that are conventionally used in engineering design, by providing a physics-based extrapolation beyond the range in which they have significant support from data (i.e., to large magnitude and small source-to-site distances).
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