Collaborative Research: CMG: Cellular Automata, Directed Graphs, and the Modeling of Earthquake and Landforms

合作研究：CMG：元胞自动机、有向图以及地震和地貌建模

• 批准号: 0327598
• 负责人: Andrei Gabrielov
• 金额: $ 12.89万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-10-01 至 2006-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0327598&HistoricalAwards=false
• 关键词: Collaborative; Research; CMG; Cellular; Automata

This project examines some of the mathematical foundations of self-organization in complex systems that occur in geophysics. The approach is based on studying the properties of a number of discrete phenomenological models of geophysical and environmental phenomena including: the spread of forest fires, earthquakes, and landform evolution. One goal is to identify the universal aspects of the behavior of such nonlinear systems, extending results on universality already seen in studies drainage networks, diffusion-limited aggregation, and forest fire models. Questions to be addressed include the dependence of behavior on the representation of interactions between elements, the effect of representing unresolved physical processes as random noise, their forecastability, and whether continuum analogues can be found. The investigators hope that the understanding gained may have practical applications in the prediction and risk assessment of natural hazards, including better predictive models of earthquakes, the assessment of land-slide hazards, and the assessment of forest fire hazards.

这个项目探讨了一些数学基础的自组织在复杂的系统中发生的物理学。 该方法是基于研究地球物理和环境现象的离散唯象模型的属性，包括：森林火灾，地震和地形演变的蔓延。 一个目标是确定这样的非线性系统的行为的普遍性方面，扩展已经看到的研究排水网络，扩散限制聚合，和森林火灾模型的普遍性的结果。 需要解决的问题包括行为对元素之间相互作用的表示的依赖性，将未解决的物理过程表示为随机噪声的效果，它们的可预测性，以及是否可以找到连续类似物。研究人员希望所获得的理解可能在自然灾害的预测和风险评估中具有实际应用，包括更好的地震预测模型，滑坡危险性评估和森林火灾危险性评估。