When a damaging earthquake occurs, immediate information about casualties is critical for time-sensitive decision-making by emergency response and aid agencies in the first hours and days. Systems such as Prompt Assessment of Global Earthquakes for Response (PAGER) by the U.S. Geological Survey (USGS) were developed to provide a forecast within about 30 minutes of any significant earthquake globally. Traditional systems for estimating human loss in disasters often depend on manually collected early casualty reports from global media, a process that's labor-intensive and slow with notable time delays. Recently, some systems have employed keyword matching and topic modeling to extract relevant information from social media. However, these methods struggle with the complex semantics in multilingual texts and the challenge of interpreting ever-changing, often conflicting reports of death and injury numbers from various unverified sources on social media platforms. In this work, we introduce an end-to-end framework to significantly improve the timeliness and accuracy of global earthquake-induced human loss forecasting using multi-lingual, crowdsourced social media. Our framework integrates (1) a hierarchical casualty extraction model built upon large language models, prompt design, and few-shot learning to retrieve quantitative human loss claims from social media, (2) a physical constraint-aware, dynamic-truth discovery model that discovers the truthful human loss from massive noisy and potentially conflicting human loss claims, and (3) a Bayesian updating loss projection model that dynamically updates the final loss estimation using discovered truths. We test the framework in real-time on a series of global earthquake events in 2021 and 2022 and show that our framework streamlines casualty data retrieval, achieving speed and accuracy comparable to manual methods by USGS.

通过多语言的、群体化的社交媒体，引入一个端到端的框架，显著提高全球地震引发的人员损失预测的及时性和准确性。该框架集成了（1）基于大型语言模型、提示设计和少样本学习的分级伤亡提取模型，用于从社交媒体中检索定量人员损失索赔，（2）一个考虑物理约束的、动态真实性发现模型，从大量嘈杂和潜在冲突的人员损失索赔中发现真实的人员损失，和（3）一个贝叶斯更新损失投影模型，使用发现的真实数据动态更新最终的损失估计。我们实时测试该框架在2021年和2022年的一系列全球地震事件上，并展示我们的框架简化了伤亡数据的获取，实现了与美国地质调查局手动方法相当的速度和准确性。

利用众包数据和大型语言模型的接近实时地震致死人数估计