A Unified Probabilistic Framework for Volcanic Hazard and Eruption Forecasting
A Unified Probabilistic Framework for Volcanic Hazard and Eruption Forecasting
复制标题
火山灾害和喷发预测的统一概率框架
DOI：
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发表时间：
2021
影响因子：
4.6
通讯作者：
T. Jordan
中科院分区：
文献类型：
--
作者：
W. Marzocchi;J. Selva;T. Jordan
抽象的。本文的主要目的是强调澄清火山灾害和喷发预测所采用的概率框架的重要性。喷发预测和火山灾害分析力求量化喷发前、喷发中和喷发后过程建模中普遍存在的深层不确定性。这些不确定性可分为三种基本类型：（1）火山系统的自然变异性，通常表示为具有参数化分布的随机过程（偶然的变化）;（2）我们对火山系统如何运作和演变的知识的不确定性，通常表现为基于专家意见的主观概率（认识上的不确定性）;（3）由于我们完全不知道的火山过程的行为，我们的预测是错误的可能性，因此，不能用概率来量化（本体论错误）。在这里，我们提出了Marzocchi & Jordan（2014）最近提出的风险分析的概率框架，它统一了所有三种类型的不确定性的处理。在这个框架内，一个火山爆发预测或火山灾害模型被认为是完整的，只有当它（a）充分表征了模型的偶然变化的表示和（B）可以无条件地测试（原则上）对观察，以确定本体论错误的认识不确定性。无条件的可测试性，这是模型验证的关键，取决于一个实验概念，其特征在于危险事件的可交换的数据序列与明确定义的频率。我们说明了这个统一的概率框架的应用，通过描述实验概念的火山灰下降Campi Flegrei的预测。最后，这个例子可以作为一个指南，将同样的概率框架应用于其他自然灾害。
Abstract. The main purpose of this article is to emphasize the importance of clarifying the probabilistic framework adopted for volcanic hazard and eruption forecasting. Eruption forecasting and volcanic hazard analysis seeks to quantify the deep uncertainties that pervade the modeling of pre-, sin- and post-eruptive processes. These uncertainties can be differentiated into three fundamental types: (1) the natural variability of volcanic systems, usually represented as stochastic processes with parameterized distributions (aleatory variability); (2) the uncertainty in our knowledge of how volcanic systems operate and evolve, often represented as subjective probabilities based on expert opinion (epistemic uncertainty); and (3) the possibility that our forecasts are wrong owing to behaviors of volcanic processes about which we are completely ignorant and, hence, cannot quantify in terms of probabilities (ontological error). Here we put forward a probabilistic framework for hazard analysis recently proposed by Marzocchi & Jordan (2014), which unifies the treatment of all three types of uncertainty. Within this framework, an eruption forecasting or a volcanic hazard model is said to be complete only if it (a) fully characterizes the epistemic uncertainties in the model's representation of aleatory variability and (b) can be unconditionally tested (in principle) against observations to identify ontological errors. Unconditional testability, which is the key to model validation, hinges on an experimental concept that characterizes hazard events in terms of exchangeable data sequences with well-defined frequencies. We illustrate the application of this unified probabilistic framework by describing experimental concepts for the forecasting of tephra fall from Campi Flegrei. Eventually, this example may serve as a guide for the application of the same probabilistic framework to other natural hazards.
DOI：
10.1111/j.2044-8317.2011.02037.x
发表时间：
2013-02
期刊：
The British journal of mathematical and statistical psychology
影响因子：
--
作者：
Gelman A;Shalizi CR
通讯作者：
Shalizi CR
火山：一种客观、结构化和可重复的方法，用于识别模拟火山组
影响因子：
3.5
作者：
Tierz P
通讯作者：
Tierz P
相互依赖的基础设施的不确定性来源及其影响
影响因子：
8.1
作者：
Reilly, Allison C.;Baroud, Hiba;Flage, Roger;Gerst, Michael D.
通讯作者：
Gerst, Michael D.