AI RESEARCH
Well Log-Guided Synthesis of Subsurface Images from Sparse Petrography Data Using cGANs
arXiv CS.LG
•
ArXi:2603.09651v1 Announce Type: new Pore-scale imaging of subsurface formations is costly and limited to discrete depths, creating significant gaps in reservoir characterization. To address this, we present a conditional Generative Adversarial Network (cGAN) framework for synthesizing realistic thin section images of carbonate rock formations, conditioned on porosity values derived from well logs.