Deep-ultradeep fractured-vuggy recognition from seismic response simulation to CNN detection

What is it about?

This workflow uses point-spread functions (PSFs) to simulate cave response in the seismic data and allows us to easily generate realistic and diverse synthetic training datasets with different geological structures and cave features. By training the CNN with these synthetic datasets, it can effectively learn to detect cave features in field seismic volumes. We have evaluated the effectiveness of our method using multiple examples and found that it performs more accurately than previous methods, including seismic attributes and other CNN-based paleokarst characterization methods.

Featured Image

Photo by Kevin Charit on Unsplash

Photo by Kevin Charit on Unsplash

Why is it important?

Effectively reducing exploration costs