Machine learning for data-driven discovery in solid Earth geoscience

Area of Science:

• Solid Earth geosciences
• Geophysics
• Geodynamics

Background:

• Understanding Earth's behavior is crucial but hindered by complex, multiscale processes.
• Direct observation of the Earth's subsurface is severely limited.
• Advancements in data availability and computational simulations offer new avenues for research.

Purpose of the Study:

• To review the current state of machine learning applications in solid Earth geosciences.
• To identify challenges and opportunities for accelerating scientific discovery.
• To provide recommendations for broadening and advancing the field.

Main Methods:

• Review of existing literature and case studies on machine learning in geosciences.
• Analysis of the potential of data-driven approaches and advanced simulations.
• Synthesis of current capabilities and future research directions.

Main Results:

• Machine learning (ML) is poised to play a pivotal role in advancing Earth science.
• Increased data and sophisticated simulations, when combined with ML, can enhance understanding of Earth's complex systems.
• The integration of ML requires addressing specific challenges in data handling and model interpretability.

Conclusions:

• Machine learning is essential for overcoming the inherent complexities and observational limitations in solid Earth geosciences.
• Further development and application of ML techniques are recommended to accelerate progress.
• Interdisciplinary collaboration and strategic investment are key to realizing the full potential of ML in Earth science.