TL;DR

Scalable Bayesian wavelet estimation with uncertainty quantification for large seismic datasets.

Summary

This paper develops a Bayesian approach (model + Gibbs sampler) to estimate seismic wavelets efficiently at scale. Scalability is achieved by embedding the data in a cyclic domain.

When to use

• You want uncertainty-aware wavelet inference rather than a single point estimate.
• You are OK with Gaussian reflectivity and seismic wavelet priors.
• You have well observations.

Resources

• Paper
• GitHub