aceuse of data-driven ML/DL models in geoscience research
Source article: Artificial 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…
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A Proliferation of Lakes on the Tibetan Plateau (154011 - tm5 19940801 lrg) by NASA Earth Observatory images by Michala Garrison, using Landsat data from the U.S. Geological Survey. Story by Adam Voiland.. Public domain
This peer-reviewed review from August 2024 examines the evolution of geoscience inquiry from traditional physics-based numerical models to modern data-driven ML and DL approaches enabled by advances in AI and data collection. It describes how data-driven models leverage large geoscience datasets and how hybrid models that embed domain knowledge aim to improve efficiency and reduce training data needs.
The synthesis matters because it clarifies both the promise and the bottlenecks for AI in Earth science: while hybrid approaches may lower data requirements and improve performance, issues of data scarcity, computational cost, privacy, and model interpretability continue to limit seamless adoption. The paper points to future opportunities at the AI-geoscience intersection without presenting new experimental results.
- Traditional physics-based models explicitly reconstruct physical processes but struggle to capture Earth's complexities and uncertainties.
- Data-driven ML and DL approaches use extensive geoscience data to address Earth science questions without exhaustive theoretical knowledge.
- Hybrid models that incorporate domain knowledge to guide AI demonstrate enhanced efficiency and performance with reduced training data requirements.
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.
The rundown
The review traces a shift from physics-based numerical models that explicitly reconstruct underlying physical processes to contemporary data-driven ML and DL models that leverage extensive geoscience data.
It identifies persistent barriers including data scarcity, computational demands, privacy concerns, and black-box opacity, and highlights hybrid methodologies that incorporate domain knowledge to guide AI as an alternative paradigm with improved efficiency.
Published in August 2024, the paper frames the field as poised to unlock new understandings of Earth's complexities while noting that optimization and real-world applicability remain challenging.
Integration is constrained by data scarcity, high computational demands, data privacy concerns, and the black-box nature of AI models.
Sources
- Peer-reviewedThe Innovation2024-08-23
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