Continuing Support for the Operation of the Los Angeles Strong Motion Array

继续支持洛杉矶强震动阵列的运行

• 批准号: 0301650
• 负责人: Maria Todorovska
• 金额: $ 8.19万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-05-15 至 2006-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0301650&HistoricalAwards=false
• 关键词: Continuing; Support; Operation; Los; Angeles

This action is to support a collaborative research project to study the generation and propagation of tsunamis in both the near and far fields, as generated by earthquakes or underwater landslides. The principal goal is to define features ("observables") that can be used to discriminate between an earthquake or a landslide as the origin of a tsunami.In the near field, hydrodynamic simulations are used to examine the influence of various source parameters on the expected maximum runup on a nearby beach, and on the lateral distribution along the beach. The strategy is to identify combinations of observables that would be largely invariant for one type of source, and thus could serve as source discriminants. In addition, this approach will be applied to the existing databases collected during the post- tsunami surveys conducted in the past decade, in order to determine in detail the source mechanisms that controlled the amplitude and extent of devastation. This research will result in a better understanding of the association between source parameters (for both types of sources) and the resulting runup at nearby beaches, and will improve the models used in estimating and mitigating the impact of future tsunamis on a regional scale.In the far field, the project considers the frequency dispersion and its effect on the evolution of tsunami waveshapes under a variety of source scenarios, again including both earthquakes and landslides. Comparing simulations performed with dispersive and non-dispersive codes should identify those source conditions that may enhance the effect of dispersion in the far field, and generalize to a full two-dimensional ocean earlier results obtained for one-dimensional propagation. The influence of source type on directivity will be addressed systematically. This is well understood for earthquake sources, but remains debatable for landslides. This research will establish the possible contribution of landslide sources to tsunami hazard in the far field, and clarify the question of the suitability of various approximations in hydrodynamic theories under a number of source scenarios. The results will be applied to the case of the 1946 Aleutian earthquake, which inflicted the greatest devastation by a tsunami on the United States in the past 100 years, but whose exact generating mechanism is still elusive. A number of historical tsunamis will also be studies, for which no exact description of source mechanism is available.The research will significantly improve the capability to predict inundation and runup during future tsunamis, and thus for mitigation of their effects. The principal investigators will continue their tradition of graduate and undergraduate student involvement in all aspects of the research, and active interaction with the news media. They will also compile and permanently archive the large dataset of videotaped interviews obtained during previous tsunami field surveys.

该行动是为了支持一个协作研究项目，以研究地震或水下滑坡生成的近距离和远处的海啸的产生和传播。 主要目标是定义可用于区分地震或滑坡的特征（“可观察到”）作为海啸的起源。在近场，流体动力模拟用于检查各种源参数对源参数的影响。预计在附近的海滩和海滩的侧向分布上预计最大值。 该策略是确定可观察到的组合，这些可观察到的物质在很大程度上是一种类型的来源，因此可以用作源歧视因素。 此外，该方法将应用于过去十年中海啸调查期间收集的现有数据库，以详细确定控制破坏幅度和程度的源机制。 这项研究将使人们更好地了解源参数（两种类型的来源）与在附近海滩的结果之间的关联，并将改善用于估算和减轻未来海啸对区域规模的影响的模型。远场，该项目考虑了频率分散及其对多种源场景下海啸波的影响的影响，再次包括地震和滑坡。 比较用分散性和非分散代码进行的模拟应确定那些可能增强远场分散作用的来源条件，并概括为一维繁殖获得的完整二维海洋早期结果。 源类型对方向性的影响将被系统地解决。 对于地震来源而言，这是对此众所周知的，但对于滑坡仍然值得商bat。 这项研究将确定滑坡源对远处海啸危害的可能贡献，并阐明在许多源场景下流体力学理论中各种近似值的适用性问题。 结果将应用于1946年的阿留申地震，在过去的100年中，海啸造成了海啸最大的破坏，但其确切的生成机制仍然难以捉摸。 许多历史海啸也将是研究，为此，没有确切的来源机制描述。该研究将显着提高预测未来海啸期间的淹没和奔跑的能力，从而减轻其效果。 首席调查人员将继续他们在研究各个方面的毕业生和本科生参与以及与新闻媒体的积极互动。 他们还将编译并永久存档在以前的海啸现场调查中获得的录像带访谈的大型数据集。