Robust Estimation of Structural Orientation Parameters and 2D/3D Local Anisotropic Tikhonov Regularization

TL;DR

This paper presents a new variational method that jointly estimates structural orientation and geophysical parameters in 2D and 3D inverse problems, improving the stability and accuracy of geological structure recovery.

Contribution

It introduces an adaptive anisotropic Tikhonov regularization approach using an alternating minimization scheme with automatic parameter tuning for enhanced geophysical modeling.

Findings

Robustly estimates tilt, dip, and strike fields in 2D and 3D cases.

Improves resolution and structural accuracy in geophysical models.

Effective in structure-oriented denoising and trace interpolation.

Abstract

Understanding the orientation of geological structures is crucial for analyzing the complexity of the Earths' subsurface. For instance, information about geological structure orientation can be incorporated into local anisotropic regularization methods as a valuable tool to stabilize the solution of inverse problems and produce geologically plausible solutions. We introduce a new variational method that employs the alternating direction method of multipliers within an alternating minimization scheme to jointly estimate orientation and model parameters in both 2D and 3D inverse problems. Specifically, the proposed approach adaptively integrates recovered information about structural orientation, enhancing the effectiveness of anisotropic Tikhonov regularization in recovering geophysical parameters. The paper also discusses the automatic tuning of algorithmic parameters to maximize the new method's performance. The proposed algorithm is tested across diverse 2D and 3D examples, including structure-oriented denoising and trace interpolation. The results show that the algorithm is robust in solving the considered large and challenging problems, alongside efficiently estimating the associated tilt field in 2D cases and the dip, strike, and tilt fields in 3D cases. Synthetic and field examples show that the proposed anisotropic regularization method produces a model with enhanced resolution and provides a more accurate representation of the true structures.