Property estimation from seismic data is an ill-posed inverse problem. This work introduces stratigraphy-guided deep learning, where geologic units are used as model features to better constrain subsurface property prediction.

This work applies stratigraphy- and rock physics-guided deep learning to predict elastic and petrophysical properties from seismic data, improving thin-bed resolution and geological consistency for reservoir characterization, exploration, and CO₂ storage.

This work evaluates deep learning for seismic fluid detection by predicting fluid-sensitive elastic properties. On Volve data, DL captures blind-well fluid trends, separates hydrocarbon-bearing from dry wells, and shows seismic as the key input.