Over the last decade, machine learning methods have established themselves as essential tools for geophysical data analysis, often substantially improving upon the conventional routines. They are applied across diverse datasets, ranging from seismic, GNSS, and InSAR measurements to laboratory recordings, to answer questions spanning states and processes of the Solid Earth and environmental systems. Nonetheless, numerous challenges remain in the application of machine learning for geophysical data, such as:
- How can machine learning bridge different data modalities and capture the different scales of geophysical processes?
- How can we efficiently encode physics knowledge into machine learning models or extract physical insights from machine learning black boxes?
- What standardized datasets and evaluation benchmarks are needed to ensure fair comparison, reproducibility, and progress?
- How can simulated data help alleviate data-poor scenarios, such as rare extreme events?
- What is the impact of recent developments in artificial intelligence, such as the advent of large language models and foundation models on geophysics?
- How can we lower model complexity to reduce environmental impact and enable use in low-power contexts?
What are the best practices for integrating machine learning into mission-critical production systems, such as early warning applications?
In this session, we aim to address these questions and related active topics in the development and application of machine learning for geophysical data. We aim to bring together machine learning researchers and practitioners from different geophysical domains to identify common challenges and opportunities. We welcome contributions from all fields of geophysics, covering a wide range of data types and machine learning techniques. We also encourage contributions for machine learning adjacent tasks, such as big-data management, data visualization, or software development.
Nikolaj Dahmen