.png){kind=icon}
返回
Pattern dynamics, pattern hierarchies, and forecasting in complex multi-scale earth systems
.png){kind=icon}
OA
AI
DOI：10.5194/hess-10-789-2006.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Catastrophic disasters afflicting human society are often triggered by tsunamis, earthquakes, widespread flooding, and weather and climate events. As human populations increasingly move into geographic areas affected by these earth system hazards, forecasting the onset of these large and damaging events has become increasingly urgent. In this paper we consider the fundamental problem of forecasting in complex multi-scale earth systems when the basic dynamical variables are either unobservable or incompletely observed. In such cases, the forecaster must rely on incompletely determined, but tunable models to interpret observable space-time patterns of events. The sequence of observable patterns constitute an apparent pattern dynamics, which is related to the underlying but hidden dynamics by a complex dimensional reduction process. As an example, we examine the problem of earthquakes, which must utilize current and past observations of observables such as seismicity and surface strain to produce forecasts of future activity. We show that numerical simulations of earthquake fault systems are needed in order to relate the fundamentally unobservable nonlinear dynamics to the readily observable pattern dynamics. We also show that the space-time patterns produced by the simulations lead to a scale-invariant hierarchy of patterns, similar to other nonlinear systems. We point out that a similar program of simulations has been very successful in weather forecasting, in which current and past observations of weather patterns are routinely extrapolated forward in time via numerical simulations in order to forecast future weather patterns.
Keyword:
SYNTHETIC SEISMICITY MODEL
SOUTHERN-CALIFORNIA
THRESHOLD SYSTEMS
PHYSICAL MODEL
FAULT SYSTEMS
EARTHQUAKES
SIMULATIONS
PREDICTION
HAZARD
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.8
论文数:
6.0K
被引数:
2.8W
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏
查看更多内容