Hour reduction in training time
Improvement in model accuracy
The client conducts highly expensive geological surveys for oil exploration and was 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.
GitLab
Python
Google Cloud Platform
TensorFlow
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.
