aceArtificial intelligence for geoscience: Progress, challenges, and perspectives
This paper explores the evolution of geoscientific inquiry, tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence (AI) and data collection techniques. Traditional models, which are grounded in physical and numerical frameworks, provide robust explanations by explicitly reconstructing underlying physical processes. However, their limitations in comprehensively capturing Earth's complexities and uncertaintie…
Data-driven ML and DL models can leverage extensive geoscience data to glean insights without exhaustive theoretical knowledge, and hybrid physics-guided models show enhanced efficiency with reduced training data needs.
AI models in geoscience are hindered by data scarcity, computational demands, data privacy concerns, and black-box opacity, while traditional physics models struggle to capture Earth's complexities.
Integration is constrained by data scarcity, high computational demands, data privacy concerns, and the black-box nature of AI models.
Evidence
- Peer-reviewedThe Innovation2024-08-23
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Truvace Impact Record TRV-2026-0370, v1: “Artificial intelligence for geoscience: Progress, challenges, and perspectives.” Truvace, 2026-07-20. /record/TRV-2026-0370 (accessed at citation time). sha256 2c674218fa1b93a3…
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