基于CEEMDAN的时频峰值滤波微地震随机噪声压制方法

微地震监测被广泛应用于非常规油气勘探领域,促进油气的增储和高效开采.由于微地震数据具有非平稳性,现行去噪方法的效果并不理想.文中提出一种基于样本熵(SE)自适应白噪声完整集合经验模态分解(CEEMDAN)的时频峰值滤波(TFPF)方法,压制微地震数据中的噪声,保留有效信号幅值.原始微地震数据经CEEMDAN分解为若干个...

Suppression of random microseismic noise based on complete ensemble empirical mode decomposition with adaptive noise of TFPF

Microseismic monitoring is widely applied in unconventional oil and gas fields, and supports the production and reserve increase of oil and gas fields. Because microseismic data are non-stationary, conventional denoising methods are not effective. This paper proposes a time-frequency peak filtering (TFPF) method of adaptive white noise based on the sample entropy (SE) complete set of empirical mode decomposition (CEEMDAN) to suppress noises while preserving effective signals. First raw microseismic data are decomposed into several IMFs of intrinsic modal components by CEEMDAN. Then after calculating the sample entropy, the IMFs are divided into two groups — one group will be filtered and the other will be left alone. The former group is TFPF filtered after selecting filter windows, and reconstructed with the latter to get final filtered signals. Application to theoretical model and field data has shown that the noise suppression method proposed in the paper is more effective than traditional EMD and constant-window TFPF denoising methods.