To aid in setting scientific research priorities, we assess the potential value of removing each of the epistemic uncertainties currently represented in the US Geological Survey California seismic-hazard model, using average annual loss (AAL) as the risk metric of interest. Given all the uncertainties, represented with logic-tree branches, we find a mean AAL of $3.94 billion. The modal value is 17.5% lower than the mean, and there is a 78% chance that the true AAL value is more than 10% away from the mean, and a 5% chance that it is a factor 2.1 greater or lower than the mean. We quantify the extent to which resolving each uncertainty improves the AAL estimate. The most influential branch is one that adds additional epistemic uncertainty to ground motion models, but others are found to be influential as well, such as the rate of M ≥ 5 events throughout the region. We discuss the broader implications of our findings, and note that the time dependence caused by spatiotemporal clustering can be much more influential on AAL than the epistemic uncertainties explored here.

中文翻译：

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评估消除地震灾害相关认知不确定性的价值，以加利福尼亚州的平均年损失为例

为了帮助确定科学研究优先事项，我们使用平均年损失 (AAL) 作为感兴趣的风险指标，评估了消除美国地质调查局加利福尼亚地震灾害模型中当前代表的每个认知不确定性的潜在价值。考虑到所有的不确定性，用逻辑树分支表示，我们发现平均 AAL 为 39.4 亿美元。模态值比均值低 17.5%，真实 AAL 值与均值相差 10% 以上的可能性为 78%，比均值大或小 2.1 倍的可能性为 5% . 我们量化了解决每个不确定性改进 AAL 估计的程度。最有影响力的分支是为地面运动模型增加额外认知不确定性的分支，但发现其他分支也有影响，例如整个区域 M ≥ 5 个事件的发生率。我们讨论了我们发现的更广泛的影响，并注意到时空聚类引起的时间依赖性对 AAL 的影响可能比这里探讨的认知不确定性要大得多。