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.

通过生成人工智能在地球科学领域的潜在应用，我们探讨了机器学习和深度学习的最新发展，以及它们在解决与地球科学和地球系统动力学相关的预测问题、模拟和多准则决策挑战方面所发挥的作用。并且介绍了在地球科学中使用的几种生成对抗网络（GANs）、物理信息神经网络（PINNs）和基于生成预训练转换器（GPT）的结构。这些工具在数据生成 / 增强、超分辨率、全色锐化、去雾、恢复和地表变化等多个应用中帮助地球科学界取得了进展。但还存在一些挑战，如确保物理解释性、恶意用途案例和可信度。总的来说，GAI 模型对地球科学界有着巨大潜力，特别是在支持气候变化、城市科学、大气科学、海洋科学和行星科学方面，通过其对数据驱动建模和不确定性量化的非凡能力。

地球科学与生成式人工智能以及大型语言模型的交叉研究：基础、趋势与未来挑战

When Geoscience Meets Generative AI and Large Language Models:  Foundations, Trends, and Future Challenges

The global occurrence, scale, and frequency of wildfires pose significant
threats to ecosystem services and human livelihoods. To effectively quantify
and attribute the antecedent conditions for wildfires, a thorough understanding
of Earth system dynamics is imperative. In response, we introduce the SeasFire
datacube, a meticulously curated spatiotemporal dataset tailored for global
sub-seasonal to seasonal wildfire modeling via Earth observation. The SeasFire
datacube comprises of 59 variables encompassing climate, vegetation, oceanic
indices, and human factors, has an 8-day temporal resolution and a spatial
resolution of 0.25 degrees, and spans from 2001 to 2021. We showcase the
versatility of SeasFire for exploring the variability and seasonality of
wildfire drivers, modeling causal links between ocean-climate teleconnections
and wildfires, and predicting sub-seasonal wildfire patterns across multiple
timescales with a Deep Learning model. We publicly release the SeasFire
datacube and appeal to Earth system scientists and Machine Learning
practitioners to use it for an improved understanding and anticipation of
wildfires.

全球野火的发生、规模和频率对生态系统服务和人类生计构成重大威胁。为了有效量化和归因于野火的先决条件，对地球系统动力学的彻底理解是必需的。为此，我们介绍了 SeasFire 数据立方体，这是一个经过精心策划的时空数据集，可用于通过地球观测进行全球亚季节到季节性野火建模。SeasFire 数据立方体包括 59 个变量，涵盖气候、植被、海洋指数和人类因素，在时间上具有 8 天的分辨率，在空间上具有 0.25 度的分辨率，时段为 2001 年至 2021 年。我们展示了 SeasFire 在探索野火驱动因素的变异性和季节性、建模海洋 - 气候相互关系与野火之间的因果关系以及使用深度学习模型预测多个时间尺度下的亚季节性野火模式的多功能性。我们公开发布了 SeasFire 数据立方体，并呼吁地球系统科学家和机器学习实践者使用该数据立方体来改善对野火的理解和预测。