Gerstenberger Matthew C, Wiemer Stefan, Jones Lucile M, Reasenberg Paul A
US Geological Survey, 525 S. Wilson Ave, Pasadena, California 91106, USA.
Nature. 2005 May 19;435(7040):328-31. doi: 10.1038/nature03622.
Despite a lack of reliable deterministic earthquake precursors, seismologists have significant predictive information about earthquake activity from an increasingly accurate understanding of the clustering properties of earthquakes. In the past 15 years, time-dependent earthquake probabilities based on a generic short-term clustering model have been made publicly available in near-real time during major earthquake sequences. These forecasts describe the probability and number of events that are, on average, likely to occur following a mainshock of a given magnitude, but are not tailored to the particular sequence at hand and contain no information about the likely locations of the aftershocks. Our model builds upon the basic principles of this generic forecast model in two ways: it recasts the forecast in terms of the probability of strong ground shaking, and it combines an existing time-independent earthquake occurrence model based on fault data and historical earthquakes with increasingly complex models describing the local time-dependent earthquake clustering. The result is a time-dependent map showing the probability of strong shaking anywhere in California within the next 24 hours. The seismic hazard modelling approach we describe provides a better understanding of time-dependent earthquake hazard, and increases its usefulness for the public, emergency planners and the media.
尽管缺乏可靠的确定性地震前兆，但通过对地震聚类特性的日益精确理解，地震学家拥有了有关地震活动的重要预测信息。在过去15年中，基于一般短期聚类模型的随时间变化的地震概率在重大地震序列期间已近乎实时地公开提供。这些预测描述了在给定震级的主震之后平均可能发生的事件概率和数量，但并非针对手头的特定序列量身定制，且不包含余震可能发生地点的信息。我们的模型在两个方面基于这种一般预测模型的基本原理进行构建：它根据强烈地面震动的概率重新进行预测，并且将基于断层数据和历史地震的现有与时间无关的地震发生模型与描述局部随时间变化的地震聚类的日益复杂的模型相结合。结果是一幅随时间变化的地图，显示了未来24小时内加利福尼亚州任何地方发生强烈震动的概率。我们所描述的地震灾害建模方法能更好地理解随时间变化的地震灾害，并提高其对公众、应急规划者和媒体的实用性。
