  This paper presents a novel approach to extract scientifically valuable
information about Earth's physical phenomena from unconventional sources, such
as multi-modal social media posts. Employing a state-of-the-art large language
model (LLM), Gemini 1.5 Pro (Reid et al. 2024), we estimate earthquake ground
shaking intensity from these unstructured posts. The model's output, in the
form of Modified Mercalli Intensity (MMI) values, aligns well with independent
observational data. Furthermore, our results suggest that LLMs, trained on vast
internet data, may have developed a unique understanding of physical phenomena.
Specifically, Google's Gemini models demonstrate a simplified understanding of
the general relationship between earthquake magnitude, distance, and MMI
intensity, accurately describing observational data even though it's not
identical to established models. These findings raise intriguing questions
about the extent to which Gemini's training has led to a broader understanding
of the physical world and its phenomena. The ability of Generative AI models
like Gemini to generate results consistent with established scientific
knowledge highlights their potential to augment our understanding of complex
physical phenomena like earthquakes. The flexible and effective approach
proposed in this study holds immense potential for enriching our understanding
of the impact of physical phenomena and improving resilience during natural
disasters. This research is a significant step toward harnessing the power of
social media and AI for natural disaster mitigation, opening new avenues for
understanding the emerging capabilities of Generative AI and LLMs for
scientific applications.
