Triply Laplacian Scale Mixture Modeling for Seismic Data Noise Suppression

2025-02-20

AI Summary

Conventional sparse recovery methods for nonstationary noisy seismic data suffer from degraded performance due to unknown variances of tensor coefficients. Method: This paper proposes a Triple Laplacian Scale Mixture (TLSM) Bayesian modeling framework—the first to introduce a three-level sparsity-inducing prior into seismic denoising—jointly estimating sparse tensor coefficients and latent noise scale parameters. The method integrates tensor low-rank–sparse structure with multiscale Bayesian priors and employs an efficient ADMM-based algorithm to solve the resulting nonconvex optimization problem. Results: On both synthetic and field seismic data, the proposed method achieves average improvements of 3.2 dB in PSNR and 0.15 in SSIM, while accelerating computation by over 40% compared to state-of-the-art denoising approaches. It demonstrates superior robustness against complex nonstationary noise and high-accuracy joint estimation capability.

Technology Category

• Machine Learning: Matrix & Tensor Methods
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Improves seismic data noise suppression accuracy
• Estimates sparse tensor coefficients effectively
• Enhances computational efficiency in noise reduction

Innovation

• Triply Laplacian scale mixture modeling
• Sparsity-based tensor recovery methods
• Alternating direction method of multipliers