Geophysical Data

Geophysical data analysis aims to extract meaningful information from diverse Earth-related measurements, such as seismic waves, geoelectric signals, and gravity data, to understand subsurface structures and processes. Current research heavily utilizes machine learning, particularly foundation models and deep learning architectures like convolutional neural networks and gradient boosted regression trees, to improve data interpretation, prediction, and processing efficiency across various scales and data types. These advancements enable more accurate and cost-effective exploration for resources (e.g., minerals, geothermal energy, hydrocarbons), improved environmental monitoring (e.g., landfill leachate detection), and a deeper understanding of Earth's systems. The integration of physics-informed machine learning is also gaining traction, combining the strengths of physical models with the flexibility of data-driven approaches.