A new generation of earthquake source models- Past, present and future

新一代震源模型——过去、现在和未来

• 批准号: 1447107
• 负责人: Mark Simons
• 金额: $ 45万
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1447107&HistoricalAwards=false
• 关键词: new; generation; earthquake; source; models

This project focuses on theoretical and computational developments needed for the next generation of seismic models. This effort concentrates quantifying the impact of uncertainties in models and to rigorously incorporate uncertainties into the algorithms behind the models. The last decade has seen considerable improvements in modeling capability and a substantial expansion of geophysical observations including data from dense seismic networks, networks of permanent GPS installations and spatially synoptic geodetic imaging data from orbiting radar and optical satellites. Despite these efforts, perhaps the biggest obstacle to significant progress in observational earthquake source modeling arises from imperfect predictions of observations due to uncertainties in Earth structure - the impact of which are generally overlooked. Indeed, for large earthquakes, our ability to measure ground motions far exceeds our ability to model them. The expected benefits of this approach include improving the sharpness of our images of seismic and aseismic fault slip processes -- thereby directly impacting both our models for fault mechanics and inferences of seismic hazard. The algorithms developed here are directly applicable to other problems such as models of volcano deformation. All tools developed here will be documented and openly available to the geophysical community as open source. This project develops the concept of the misfit covariance as used in inversions for the distribution of slip on subsurface faults. The misfit covariance is a combination of covariances in observations (often assumed independent) and covariances associated with inaccurate model predictions (often entirely ignored). Yet, this prediction error can dwarf the observation error and can induce important covariances between observations. These covariances dictate the relative weighting between disparate data types as well as the information content found in observations from dense networks. The proposed approach to estimating the full model prediction error relies on developing computationally tractable methods for estimating the sensitivity of geodetic and seismic observations to perturbations in assumed material properties. This approach exploits recent advances in Bayesian earthquake source modeling and new massively parallel computational approaches using GPUs.

该项目侧重于下一代地震模型所需的理论和计算方面的发展。这项工作集中于量化模型中不确定性的影响，并将不确定性严格纳入模型背后的算法。在过去十年中，建模能力有了很大提高，地球物理观测大幅扩大，包括来自密集地震台网的数据、永久全球定位系统安装网络的数据以及轨道雷达和光学卫星的空间天气大地测量成像数据。尽管作出了这些努力，但在观测地震震源模拟方面取得重大进展的最大障碍可能是由于地球结构的不确定性而造成的观测预测不完善--其影响通常被忽视。事实上，对于大地震，我们测量地面运动的能力远远超过了我们对它们进行建模的能力。这种方法的预期好处包括提高我们的地震和非地震断层滑动过程图像的清晰度--从而直接影响我们的断层力学模型和地震危险的推断。本文开发的算法可直接应用于其他问题，如火山变形模型。这里开发的所有工具都将被记录在案，并作为开放源码向地球物理界公开提供。这个项目发展了失配协方差的概念，用于地下断层上滑动分布的反演。失配协方差是观测值中的协方差(通常被认为是独立的)和与不准确的模型预测相关的协方差(通常被完全忽略)的组合。然而，这种预测误差可以使观测误差相形见绌，并可以在观测之间引起重要的协方差。这些协方差决定了不同数据类型之间的相对权重，以及从密集网络中观察到的信息内容。所提出的估计整个模型预测误差的方法依赖于开发计算上容易处理的方法来估计大地测量和地震观测对假定材料性质的扰动的敏感性。这种方法利用了贝叶斯震源模拟的最新进展和使用GPU的新的大规模并行计算方法。