当地球科学遇见生成式人工智能与大语言模型：基础、趋势与未来挑战
When Geoscience Meets Generative AI and Large Language Models: Foundations, Trends, and Future Challenges
AI总结：
本文综述 GAN、PINN 和基于 GPT 的生成式 AI 与大语言模型在地球科学中的应用、成效、挑战及未来方向。
摘要：
生成式人工智能（GAI）代表一个新兴领域，有望创建合成数据以及不同模态的输出。GAI 最近已在生物学、医学、教育、立法、计算机科学和金融等广泛应用中展现出令人瞩目的成果。在人们努力提升安全性、效率和可持续性之际，生成式 AI 确实成为关键差异化因素，并有望推动该领域的范式转变。本文探讨生成式 AI 和大语言模型在地球科学中的潜在应用。机器学习与深度学习领域的最新发展，使生成模型能够用于处理与地球科学和地球系统动力学相关的各类预测问题、模拟以及多准则决策挑战。本综述讨论了已用于地球科学的若干 GAI 模型，包括生成对抗网络（GAN）、物理信息神经网络（PINN）以及基于生成式预训练 Transformer（GPT）的结构。这些工具已在多种应用中帮助地球科学界，包括但不限于数据生成/增强、超分辨率、全色锐化、去雾、复原和地表变化。仍存在一些挑战，例如确保物理可解释性、恶意用例以及可信性。除此之外，GAI 模型凭借其在数据驱动建模和不确定性量化方面的卓越能力，对地球科学界展现出广阔前景，尤其为气候变化、城市科学、大气科学、海洋科学和行星科学提供支持。
英文摘要：
Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impressive results across a large spectrum of applications ranging from biology, medicine, education, legislation, computer science, and finance. As one strives for enhanced safety, efficiency, and sustainability, generative AI indeed emerges as a key differentiator and promises a paradigm shift in the field. This paper explores the potential applications of generative AI and large language models in geoscience. The recent developments in the field of machine learning and deep learning have enabled the generative model's utility for tackling diverse prediction problems, simulation, and multi-criteria decision-making challenges related to geoscience and Earth system dynamics. This survey discusses several GAI models that have been used in geoscience comprising generative adversarial networks (GANs), physics-informed neural networks (PINNs), and generative pre-trained transformer (GPT)-based structures. These tools have helped the geoscience community in several applications, including (but not limited to) data generation/augmentation, super-resolution, panchromatic sharpening, haze removal, restoration, and land surface changing. Some challenges still remain such as ensuring physical interpretation, nefarious use cases, and trustworthiness. Beyond that, GAI models show promises to the geoscience community, especially with the support to climate change, urban science, atmospheric science, marine science, and planetary science through their extraordinary ability to data-driven modeling and uncertainty quantification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
