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A STOCHASTIC EARTHQUAKE RECURRENCE MODEL WITH TEMPORAL AND SPATIAL DEPENDENCE (SEISMIC HAZARD, RENEWAL PROCESS, OCCURRENCE).

机译:具有时间和空间依赖性(地震危险,更新过程,发生)的随机地震递归模型。

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摘要

Consideration of the dependence between the occurrences of earthquakes is an important aspect of seismic hazard analysis which has drawn much attention. Currently used techniques for hazard evaluation are based on Poisson assumptions for earthquake occurrences, such as spatial and temporal independence between events. While this is an adequate description for some cases, it is not representative of all data. In addition it is not consistent with any geophysical description of the earthquake-generating process. A model which permits better estimates of the probabilities of earthquake occurrence is desirable in seismic hazard analysis. Assumptions of independence between earthquakes in space and time have been shown to lead to the overestimation of seismic design parameters. Modeling of dependence can lead to more realistic policy decisions and cost-effective design.;The model is tested against observed data and comparisons are made with other existing models. Examples are run for sections of the Calaveras fault zone and the San Andreas fault zone. These study zones were chosen because historical data for these areas suggest that the time-predictable hypothesis is a good description of the pattern of earthquake occurrences. Comparisons with observed data on the Calaveras fault show that the cumulative slip and the cumulative number of events in a fixed interval generated from the model are a good match to the data. Comparisons with the Poisson model suggest that the Poisson model may underestimate occurrence probabilities when a long gap has elapsed since the last earthquake or when the last earthquake was not very large.;Recent studies have indicated that a positive correlation exists between an earthquake recurrence interval and the size of the preceding event. This correlation is used to develop a stochastic model of earthquake occurrence for use in seismic hazard evaluation. Temporal and spatial dependence are included in the model. The model is based on the Time-Predictable Recurrence Model of Shimazaki and Nakata (1980) and their basic assumptions are adopted in the formulation of the stochastic model of seismic events. Stress is accumulated at a constant rate along a fault. Earthquakes occur when the accumulated stress reaches a threshold value at some location on the fault. The size of an earthquake is measured by the change in stress level. Larger events correspond to larger stress releases. After an earthquake, the amount of time required for stress to build up to the threshold state determines the time to the next earthquake. This time is related to the size of the recent earthquake and the rate of stress accumulation. The size of the stress releases are assumed to be independent identically distributed random variables. Spatial dependence is included by introducing a probability distribution for the location of a future earthquake conditional on the location of the epicenter of the previous event.
机译:考虑地震发生之间的相关性是地震危险性分析的重要方面,引起了人们的广泛关注。当前使用的危害评估技术基于地震发生的泊松假设,例如事件之间的时空独立性。尽管这在某些情况下是足够的描述,但并不代表所有数据。另外,它与地震发生过程的任何地球物理描述都不一致。在地震灾害分析中,需要一种能够更好地估计地震发生概率的模型。时空地震之间独立性的假设已被证明导致对地震设计参数的高估。依赖关系的建模可以导致更现实的政策决策和具有成本效益的设计。该模型根据观察到的数据进行了测试,并与其他现有模型进行了比较。卡拉维拉斯断层带和圣安地列斯断层带的部分实例。选择这些研究区是因为这些地区的历史数据表明,时间可预测的假设很好地描述了地震发生的模式。与有关Calaveras断层的观测数据的比较表明,从模型生成的固定时间间隔内的累积滑动和累积事件数与该数据非常匹配。与泊松模型的比较表明,泊松模型可能会低估自上次地震以来已发生长时间的间隔或上次地震发生的时间不是很大时的发生概率。;最近的研究表明,地震复发间隔与地震间隔之间存在正相关关系。前一个事件的大小。该相关性用于开发地震发生的随机模型,用于地震危险性评估。时间和空间依赖性包括在模型中。该模型基于Shimazaki和Nakata(1980)的时间可预测递归模型,并在地震事件随机模型的制定中采用了它们的基本假设。应力沿断层以恒定速率累积。当累积应力在断层的某个位置达到阈值时,就会发生地震。地震的大小通过应力水平的变化来衡量。较大的事件对应较大的压力释放。地震后,应力达到阈值状态所需的时间决定了下一次地震的时间。这段时间与最近的地震规模和应力积累速率有关。应力释放的大小假定为独立的,均匀分布的随机变量。通过引入针对前次地震震中位置的未来地震位置的概率分布来包括空间依赖性。

著录项

  • 作者

    ANAGNOS, THALIA.;

  • 作者单位

    Stanford University.;

  • 授予单位 Stanford University.;
  • 学科 Engineering Civil.
  • 学位 Ph.D.
  • 年度 1985
  • 页码 149 p.
  • 总页数 149
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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