Artificial intelligence for geoscience: Progress, challenges, and perspectives.

Area of Science:

• Geoscience
• Artificial Intelligence
• Data Science

Background:

• Traditional physics-based models offer robust explanations but struggle with Earth's complexity and uncertainties.
• Data-driven models, including machine learning (ML) and deep learning (DL), leverage extensive data for insights but face challenges like data scarcity and the "black-box" nature of AI.
• The integration of AI and advanced data collection techniques is transforming geoscientific inquiry.

Purpose of the Study:

• To review the evolution of geoscientific research paradigms.
• To highlight opportunities at the intersection of AI and geoscience.
• To examine methodologies, advances, challenges, and future prospects of AI in geoscience.

Main Methods:

• Exploration of traditional physics-based modeling.
• Analysis of contemporary data-driven approaches utilizing ML and DL.
• Investigation of hybrid models integrating physics-based and data-driven methodologies.

Main Results:

• Data-driven models show promise but face integration challenges.
• Hybrid models demonstrate improved efficiency and performance with reduced data requirements.
• Significant advances in large-scale AI models for geoscience are emerging.

Conclusions:

• The field of AI in geoscience is dynamic and offers vast potential for new discoveries.
• Hybrid models represent a promising paradigm for overcoming limitations of individual approaches.
• Continued research and development at the AI-geoscience interface will unlock deeper understanding of Earth systems.