AI-Enhanced Velocity Model Building for Subsurface Imaging
Analysis
This paper introduces a novel deep learning framework to improve velocity model building, a critical step in subsurface imaging. It leverages generative models and neural operators to overcome the computational limitations of traditional methods. The approach uses a neural operator to simulate the forward process (modeling and migration) and a generative model as a regularizer to enhance the resolution and quality of the velocity models. The use of generative models to regularize the solution space is a key innovation, potentially leading to more accurate and efficient subsurface imaging.
Key Takeaways
- •Proposes a novel deep learning framework for velocity model building.
- •Combines neural operators and generative models.
- •Uses a generative model as a regularizer to improve resolution.
- •Demonstrates effectiveness with synthetic and field data.
Reference
“The proposed framework combines generative models with neural operators to obtain high resolution velocity models efficiently.”