Microseismic (MS) signals recorded by sensors are often mixed with various noise, which produce some interference to the further analysis of the collected data. One problem of many existing noise suppression methods is to deal with noisy signals in a unified strategy, which results in low-frequency noise in the non-microseismic section remaining. Based on this, we have developed a novel MS denoising method combining variational mode decomposition (VMD) and Akaike information criterion (AIC). The method first applied VMD to decompose a signal into several limited-bandwidth intrinsic mode functions and adaptively determined the effective components by the difference of correlation coefficient. After reconstructing, the improved AIC method was used to determine the location of the valuable waveform, and the residual fluctuations in other positions were further removed. A synthetic wavelet signal and some synthetic MS signals with different signal-to-noise ratios (SNRs) were used to test its denoising effect with ensemble empirical mode decomposition (EEMD), complete ensemble empirical mode decomposition (CEEMD), and the VMD method. The experimental results depicted that the SNRs of the proposed method were obviously larger than that of other methods, and the waveform and spectrum became cleaner based on VMD. The processing results of the MS signal of Shuangjiangkou Hydropower Station also illustrated its good denoising ability and robust performance to signals with different characteristics.

中文翻译：

---

自适应变分分解和Akaike信息准则抑制微震信号的噪声

传感器记录的微地震（MS）信号通常与各种噪声混合在一起，这会对采集数据的进一步分析产生一些干扰。现有许多噪声抑制方法的一个问题是以统一的策略处理噪声信号，这导致在非微震部分中残留了低频噪声。基于此，我们开发了一种新颖的结合变分模式分解（VMD）和Akaike信息准则（AIC）的MS去噪方法。该方法首先应用VMD将信号分解为几个有限带宽的本征函数，然后根据相关系数的差异自适应地确定有效分量。重建后，使用改进的AIC方法确定有价值波形的位置，并进一步消除了其他位置的残留波动。合成小波信号和一些具有不同信噪比的合成MS信号（使用SNR（ s）通过集成经验模态分解（EEMD），完全集成经验模态分解（CEEMD）和VMD方法来测试其降噪效果。实验结果表明，该方法的信噪比明显大于其他方法，并且基于VMD可以使波形和频谱更加清晰。双江口水电站MS信号的处理结果也表明了其对不同特征信号的良好去噪能力和鲁棒性。