Triply Laplacian Scale Mixture Modeling for Seismic Data Noise Suppression

TL;DR

This paper introduces a triply Laplacian scale mixture model combined with ADMM optimization to enhance seismic data noise suppression, effectively handling non-stationary noise and improving estimation accuracy over existing methods.

Contribution

The novel TLSM approach improves estimation of tensor coefficients and scalar parameters, advancing seismic noise suppression beyond current sparsity-based tensor recovery methods.

Findings

Outperforms state-of-the-art methods in synthetic and field data

Achieves higher quantitative and qualitative noise suppression

Offers computational efficiency in large-scale seismic data processing

Abstract

Sparsity-based tensor recovery methods have shown great potential in suppressing seismic data noise. These methods exploit tensor sparsity measures capturing the low-dimensional structures inherent in seismic data tensors to remove noise by applying sparsity constraints through soft-thresholding or hard-thresholding operators. However, in these methods, considering that real seismic data are non-stationary and affected by noise, the variances of tensor coefficients are unknown and may be difficult to accurately estimate from the degraded seismic data, leading to undesirable noise suppression performance. In this paper, we propose a novel triply Laplacian scale mixture (TLSM) approach for seismic data noise suppression, which significantly improves the estimation accuracy of both the sparse tensor coefficients and hidden scalar parameters. To make the optimization problem manageable, an alternating direction method of multipliers (ADMM) algorithm is employed to solve the proposed TLSM-based seismic data noise suppression problem. Extensive experimental results on synthetic and field seismic data demonstrate that the proposed TLSM algorithm outperforms many state-of-the-art seismic data noise suppression methods in both quantitative and qualitative evaluations while providing exceptional computational efficiency.