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Static and Dynamic Triggering of Fault Creep on Strike-Slip Faults

Static and Dynamic Triggering of Fault Creep on Strike-Slip Faults
走滑断层上断层蠕变的静态和动态触发
批准号:
1411704
负责人:
Meng Wei
金额:
$5.77万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2013
资助国家:
美国
项目状态:
已结题
起止时间:
2013-11-17 至 2015-12-31

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中文摘要
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英文摘要
The 2010 El Mayor-Cucapah Mw 7.2 earthquake was the largest earthquake to strike southern California in the last 18 years. It triggered shallow creep on many faults in Salton Trough, Southern California, making it at least the 8th time in the last 42 years that a local or regional earthquake has done so. However, the triggering mechanism of fault creep and its implications for seismic hazard and fault mechanics remain poorly understood. The main goal of this project is to develop an improved view of fault creep by analyzing geodetic data and modeling the triggering process, both static and dynamic, in the framework of rate-and-state friction. The investigators will first construct an improved fault creep dataset by developing an automatic creep detector that can process thousands of InSAR images, whose results will later be integrated with the existing creepmeter and field survey datasets. Second, they will use realistic static and dynamic shaking due to nearby earthquakes as stress perturbations to a 2D planar fault model with rate-and-state frictional property variations both in depth and along strike. These parameters can eventually be used in the calculation of seismic hazard. By developing both state of the art models and the most comprehensive dataset of creep events for a large fault system, they hope to transition from a conceptual understanding of fault creep towards a quantitative, predictive understanding of the conditions that create creep events on particular faults. This project will improve our understanding of the mechanics of the shallow portions of faults, which has significant implications for the shaking/hazard levels expected from future earthquakes in the Salton Trough, an agriculturally and economically important area. The automatic transient detector is one of the first of its kind and will inspire new detectors applying pattern recognition on geodetic data.
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Using machine learning method to detect slow slip events in ocean bottom pressure data
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  • 项目类别:
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  • 资助金额:
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  • 财政年份:
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  • 项目类别:
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国内基金
海外基金
Dynamic Credit Rating with Feedback Effects
  • 批准号:
    --
  • 项目类别:
    外国学者研究基金项目
  • 资助金额:
    --
  • 批准年份:
    2024
  • 负责人:
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  • 依托单位: