Published as IMAGE Expanded Abstracts, 2847-2851, (2022)

Least-squares non-stationary triangle smoothing

Reem Alomar$^{1}$ and Sergey Fomel$^{1}$
$^1$The University of Texas at Austin

Abstract:

We propose a fast and accurate method to estimate the radius of non-stationary triangle smoothing for matching two seismic datasets. The smoothing radius is estimated by non-linear least-squares inversion using an iterative Gauss-Newton approach. We derive and implement the derivative of the smoothing operator to compute the gradient for inversion. The proposed method is useful for implementing non-stationary triangle smoothing as a low-cost edge-preserving filter. The efficiency of the proposed method is also confirmed in several field data examples of seismic data matching applications in non-stationary local signal and noise orthogonalization, non-stationary local slope estimation, and matching low-resolution and high resolution seismic images from the same exploration area.