Collaborative Research: CMG--Stochastic Quantization for Modeling the Dynamics of Regional Seismicity

合作研究：CMG--用于模拟区域地震活动动力学的随机量化

• 批准号: 0620838
• 负责人: Ilya Zaliapin
• 金额: $ 24.79万
• 依托单位: Board of Regents, NSHE, obo University of Nevada, Reno
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0620838&HistoricalAwards=false
• 关键词: Collaborative; Research; CMG; Stochastic; Quantization

Predictive understanding of the dynamics of seismicity is one of the greatest challenges of modern geophysics. To accomplish such an understanding requires a combination of exploratory data analysis and relevant mathematical modeling. This project will provide a framework for modeling and analyzing regional seismicity based on the stochastic quantization approach and the use of non-linear filtering for marked point processes. The resulting description will allow one to represent regional fault networks, including details such as complex geometry, memory, interactions, and structural heterogeneities. The proposed research will expand upon ideas captured in existing geophysical models. In particular, one of the most important goals is to enhance Epidemic Type Aftershock Sequence (ETAS) approach to short-term earthquake modeling and forecasts and improve the model performance on larger spatial and temporal scales by incorporating known aspects of regional fault systems. The models constructed in the project will be tested on the observed seismicity of southern California and will leverage high-quality data from Southern California Earthquake Data Center (SCEDC) and the Southern California Earthquake Center Community Fault Model (SCEC CFM).Natural hazards pose a threat to society, and earthquakes are probably the greatest danger to the built environment in tectonically active areas. This project will contribute to the predictive understanding of the seismicity dynamics by merging the state-of-the-art mathematical methods and seismicity data. Ultimately, the proposed work will be used to formulate earthquake forecasting strategies, which will be tested within the international Collaboratory for the Study of Earthquake Predictability (CSEP), recently organized by SCEC. The collaborative and cross-disciplinary approach of this project makes it an ideal training ground for graduate students and young scientists.

对地震活动动力学的预测性理解是现代地球物理学最大的挑战之一。要实现这样的理解，需要结合探索性数据分析和相关的数学建模。本计画将提供一个以随机量化方法为基础，并使用非线性过滤法来分析区域地震活动的架构。由此产生的描述将允许一个代表区域断层网络，包括细节，如复杂的几何形状，记忆，相互作用和结构的不均匀性。拟议的研究将扩大现有地球物理模型中捕获的想法。特别是，最重要的目标之一是加强流行病型余震序列（ETAS）的方法，短期地震建模和预测，并提高模型的性能在更大的空间和时间尺度上，通过纳入已知的方面的区域断层系统。该项目中构建的模型将在观测到的南加州地震活动性上进行测试，并将利用来自南加州地震数据中心（SCEDC）和南加州地震中心社区断层模型（SCEC CFM）的高质量数据。自然灾害对社会构成威胁，地震可能是构造活跃地区建筑环境的最大危险。该项目将通过融合最先进的数学方法和地震活动性数据，有助于对地震活动性动力学的预测理解。最后，拟议的工作将用于制定地震预报战略，这些战略将在SCEC最近组织的地震可预报性研究国际合作实验室（CSEP）内进行测试。该项目的协作和跨学科方法使其成为研究生和年轻科学家的理想培训场所。