Seismic hazard modeling is a multidisciplinary science that aims to forecast earthquake occurrence and its resultant ground shaking. Such models consist of a probabilistic framework that quantifies uncertainty across a complex system; typically, this includes at least two model components developed from Earth science: seismic source and ground motion models. Although there is no scientific prescription for the forecast length, the most common probabilistic seismic hazard analyses consider forecasting windows of 30 to 50 years, which are typically an engineering demand for building code purposes. These types of analyses are the topic of this review paper. Although the core methods and assumptions of seismic hazard modeling have largely remained unchanged for more than 50 years, we review the most recent initiatives, which face the difficult task of meeting both the increasingly sophisticated demands of society and keeping pace with advances in scientific understanding. A need for more accurate and spatially precise hazard forecasting must be balanced with increased quantification of uncertainty and new challenges such as moving from time‐independent hazard to forecasts that are time dependent and specific to the time period of interest. Meeting these challenges requires the development of science‐driven models, which integrate all information available, the adoption of proper mathematical frameworks to quantify the different types of uncertainties in the hazard model, and the development of a proper testing phase of the model to quantify its consistency and skill. We review the state of the art of the National Seismic Hazard Modeling and how the most innovative approaches try to address future challenges.

中文翻译：

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区域和国家范围内的概率地震危险性分析：最新进展和未来挑战

地震危险性建模是一门多学科的科学，旨在预测地震的发生及其引起的地震动。这些模型由一个概率框架组成，该框架量化了整个复杂系统中的不确定性。通常，这至少包括从地球科学开发的两个模型组件：地震源模型和地震动模型。尽管没有关于预报长度的科学规定，但是最常见的概率地震灾害分析考虑的是30到50年的预报窗口，这通常是出于建筑规范目的的工程需求。这些类型的分析是本文的主题。尽管地震灾害建模的核心方法和假设在过去50多年中基本上没有变化，但我们回顾了最新的举措，他们面临着既要满足社会日益复杂的要求又要与科学认识同步发展的艰巨任务。必须通过增加不确定性的量化和新的挑战（例如，从与时间无关的危害转变为与时间有关并针对特定时间段的预测）来平衡对更准确和空间精确的危害预测的需求。应对这些挑战需要开发科学驱动的模型，该模型整合所有可用信息，采用适当的数学框架来量化危害模型中的不同类型的不确定性，并开发模型的适当测试阶段以量化其危害性。一致性和技巧。