基于多模态低秩处理的沙漠地震随机噪声压制

沙漠地带的随机噪声使沙漠地震记录中的有效信号很大程度上被淹没。针对此问题，提出将自适应噪声 辅助的集合经验模态分解方法(CEEMDAN: Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)与鲁棒标准正交子空间方法 (ＲOSL: Ｒobust Orthonormal Subspace Learning) 有效融合。首先利用 CEEMDAN 算法对沙漠地震数据进行分解，将分解得到的所有模态拼成一幅新记录，并对其进行低秩分解，再 将得到的稀疏部分中每道的所有模态重新叠加获得去噪结果。二者相结合，不仅解决了单一的低秩处理对沙 漠地震数据效果不明显的问题，同时也规避了要对 CEEMDAN 算法分解得到的模态进行取舍的难题。模拟实 验和实际数据处理表明，该算法压制低频随机噪声具有明显的优势，同时对有效信号的保幅均能保证在 85% 以上，对实际数据中面波的压制也相对比较彻底。

End-to-End Quality of Service Guarantee Methods Based on Mobile IP Network

The random noise in the desert zone has low-frequency characteristics in addition to nonlinear and non-Gaussian characteristics． It exists in both analog records and actual desert seismic records． The effective signal is largely submerged in the noise posing great difficulties to the processing of subsequent data． Based on these problems，this paper combines the CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) with the ＲOSL(Ｒobust Orthonormal Subspace Learning)． The CEEMDAN algorithm is used to decompose the desert seismic data． All the modalities decomposed are combined into a new record，and the new record is subjected to low rank decomposition． Then superimpose all the modes of each channel in the obtained sparse part to obtain the denoising result． The combination of the two solves the problem that the single low rank processing has no obvious effect on the desert seismic data，and avoids the problem of choosing the modal of the CEEMDAN algorithm decomposition． The simulation experiment and the actual data processing prove that the proposed algorithm has obvious advantages in suppressing the low frequency random noise． The amplitude of the effective signal can be guaranteed to be more than 85%． The suppression of the surface wave in the actual data is relatively thorough．