Hour reduction in training time
Inference in Minutes
Improvement in model accuracy
The customer conducts highly expensive geological surveys for oil exploration and had been facing problems in decision-making due to blurry and bad quality imaging. Their research team had an existing GAN-based machine learning model on-premise that demonstrated promising results for 2D images and 3D volumes.
Although they encountered a few bottlenecks when trying to improve the model convergence and performance, finalizing exploration sites based on the low resolution image continued to pose a risk as billions of dollars could be wasted. Therefore, their research team wanted a solution for Super Resolution of low resolution seismic 2D images and 3D volumes, which in turn would allow Geologists make better informed decisions.
Quantiphi implemented state-of-the-art SR-GAN and Conditional SR-GAN architectures to reconstruct both 2D seismic images and 3D seismic images on Cloud Machine Learning Engine (CMLE) platform. By leveraging hyperparameter tuning in CMLE, an extra 5 to 10 percent performance improvement was achieved. Overall, this solution acheived Structural Similarity Indexes (SSIM) of 0.9 for both 2D and 3D seismic images; equipping the client’s research team with a modular training and serving pipeline to enhance their seismic images quality for faster and more accurate analysis.