This paper presents a novel approach for estimating the ground shaking
intensity using social media data and CCTV footage. Employing the Gemini Pro
(Reid et al. 2024) model, a multi-modal language model, we demonstrate the
ability to extract relevant information from unstructured data utilizing
generative AI and natural language processing. The model output, in the form of
Modified Mercalli Intensity (MMI) values, align well with independent
observational data. Furthermore, our results suggest that beyond its advanced
visual and auditory understanding abilities, Gemini appears to utilize
additional sources of knowledge, including a simplified understanding of the
general relationship between earthquake magnitude, distance, and MMI intensity,
which it presumably acquired during its training, in its reasoning and
decision-making processes. These findings raise intriguing questions about the
extent of Gemini's general understanding of the physical world and its
phenomena. The ability of Gemini to generate results consistent with
established scientific knowledge highlights the potential of LLMs like Gemini
in augmenting our understanding of complex physical phenomena such as
earthquakes. More specifically, the results of this study highlight the
potential of LLMs like Gemini to revolutionize citizen seismology by enabling
rapid, effective, and flexible analysis of crowdsourced data from eyewitness
accounts for assessing earthquake impact and providing crisis situational
awareness. This approach holds great promise for improving early warning
systems, disaster response, and overall resilience in earthquake-prone regions.
This study provides a significant step toward harnessing the power of social
media and AI for earthquake disaster mitigation.

使用社交媒体数据和闭路电视画面，利用生成式人工智能和自然语言处理，本研究提出了一种估计地面震动强度的新方法，通过 Gemini Pro 模型的多模态语言模型，将相关信息从非结构化数据中提取出来，并得到 Modified Mercalli Intensity (MMI) 值，与独立观测数据吻合良好。这些研究结果揭示了 Gemini 除了拥有先进的视听理解能力外，似乎还利用了其他知识来源，包括对地震震级、距离和 MMI 强度之间一般关系的简化理解，这些推理和决策过程可能在其训练过程中获取。这些发现引发了有关 Gemini 对物理世界及其现象一般理解程度的有趣问题。Gemini 能够生成与已建立的科学知识一致的结果，突显了像 Gemini 这样的多模态语言模型在增进我们对复杂物理现象（如地震）的理解方面的潜力。具体而言，本研究的结果突显出像 Gemini 这样的多模态语言模型在通过分析民众参与的数据获取地震影响评估和危机态势意识方面，将公民地震学革命化的潜力。该方法为改善地震预警系统、灾难响应和地震高风险地区整体弹性提供了巨大的希望，为利用社交媒体和人工智能进行地震灾害缓解迈出了重要的一步。