基于混合滤波架构的地震信号非线性估计与去噪方法研究

Research on Nonlinear Estimation and Denoising Methods for Seismic Signals Based on a Hybrid Filtering Architecture

摘要: 地震勘探采集的数据常受到多种噪声干扰，这些噪声会掩盖有效的地震反射信号，影响后续地质构造的识别与解释。为提高信号质量，本文提出一种基于期望最大化(EM)算法、粒子滤波(PF)与卡尔曼滤波(KF)的混合智能去噪模型(EM-KF-PF)。该模型通过EM算法增强粒子多样性，利用PF处理非线性与非高斯噪声，并引入KF以提升状态估计的收敛效率，从而在信噪比(SNR)与估计精度之间实现有效平衡。数值实验表明，相较于传统方法，本文算法在提升SNR、降低均方根误差(MSE)及状态轨迹拟合方面均表现更优，展现出良好的非线性适应性与多尺度噪声抑制能力。该模型适用于地震信号处理场景，但在参数自适应性、高维扩展性及非高斯噪声建模等方面仍有改进空间。未来研究方向包括GPU并行加速、在线学习机制及物理约束嵌入等，以提升算法的工程适用性。

Abstract: Seismic exploration data are often contaminated by various types of noise, which can obscure effective seismic reflection signals and impact the identification and interpretation of subsequent geological structures. To improve signal quality, this paper proposes a hybrid intelligent denoising model (EM-KF-PF) based on the Expectation-Maximization (EM) algorithm, Particle Filtering (PF), and Kalman Filtering (KF). This model enhances particle diversity through the EM algorithm, utilizes PF to handle nonlinear and non-Gaussian noise, and incorporates KF to improve the convergence efficiency of state estimation, thereby achieving an effective balance between signal-to-noise ratio (SNR) and estimation accuracy. Numerical experiments demonstrate that, compared to traditional methods, the proposed algorithm performs better in terms of improving SNR, reducing mean square error (MSE), and fitting state trajectories, exhibiting strong nonlinear adaptability and multi-scale noise suppression capabilities. This model is suitable for seismic signal processing scenarios, but there is still room for improvement in areas such as parameter self-adaptability, high-dimensional scalability, and non-Gaussian noise modeling. Future research directions include GPU parallel acceleration, online learning mechanisms, and the incorporation of physical constraints to enhance the engineering applicability of the algorithm.