An improved characterization of the subsurface in the Limerick Basin (Ireland) using deep generative model-based 2D gravity inversion constrained with drill hole and petrophysics data

Mineralization in the Limerick Basin, located in southwest Ireland, uniquely associates with volcanic rocks, unlike other mineralized zones in the Irish midlands, where mineral systems align with large-scale normal faults. To better visualize the subsurface structures influencing Limerick’s mineralization, we conducted 2D gravity inversion incorporating geological and petrophysical constraints.

Conventional methods of deterministic inversion involve adding a model norm term to the data misfit term in the objective function to regularize an ill-posed problem and obtain stable solutions. While previous studies on constrained deterministic inversion have modified the model norm to include prior information or constraints in geophysical inversion, the complex nature of geological priors makes encoding this information mathematically and computationally challenging. To tackle this problem, we implemented a deep generative model, specifically a conditional variational autoencoder (cVAE)-based inversion framework, to incorporate structural constraints derived from drill hole and petrophysical data.

Initially, we tested this framework on a synthetic case by training the cVAE on thousands of 2D density models comprising geological features analogous to the field geology and populated with density values consistent with the drill core measurements acquired from the study area. Artificial drill holes were created to fix the depths of geological units at the drill hole contact points across all training models, ensuring that the predicted models adhered to prior constraints. Following training, we tested the network on some test data, which showed that the predicted models successfully captured the structural and petrophysical property constraints. The geometries of the geological features were also well recovered.

We applied this method to gravity data from a NW-SE trending profile crossing the western part of Limerick Syncline. Thousands of density models were generated using drill hole data, incorporating measured rock densities for training. Since the profile’s central and deeper sections lacked sufficient geological data for direct validation of the results, we implemented a hypothesis-testing approach. In each hypothesis, geological features were added to the training density models based on prior geological knowledge of the study area. If simulated data from an inverted model failed to match field data, more geological features were added to the training models in the next hypothesis, and the workflow was repeated to achieve a low data misfit.

The inversion provided three key insights into the study area’s geology. First, it identified potential volcanic intrusions in the southern Limerick Syncline, possibly extending from depths below the basement. Second, it estimated the syncline’s geometry in areas with limited geological constraints. Third, it revealed a sharp vertical displacement in stratigraphy, indicating a potential south-dipping fault in the northwest portion of the syncline. This fault may have influenced mineralizing fluid migration, playing a critical role in mineral deposit localization.