A methodology using adaptive time series analysis is tested on data from a seismometer monitoring the north end building (NEB) of the Virgo interferometer during four acoustic noise injections. Empirical mode decomposition (EMD) is used for adaptive detrending, while the recently developed time-varying filter EMD algorithm is used for narrowband mode extraction. Mode persistency is evaluated with detrended fluctuation analysis, and denoising is achieved by setting a threshold $$H_{\text {thr}}$$ H thr on the Hurst exponent of the obtained modes. The adopted methodology is proven useful in adaptively separating the seismic noise induced by the acoustic noise injections from the underlying nonlinear non-stationary recordings of the seismometer monitoring NEB. The Hilbert–Huang transform provides a high-resolution time–frequency representation of the data. Furthermore, the local Hurst exponent exhibits a drop due to the injections that is of the same order of $$H_{\text {thr}}$$ H thr . This suggests that the local Hurst exponent could be calculated as an initial step in order to select the threshold $$H_{\text {thr}}$$ H thr . The algorithms could be used for detector characterisation purposes such as the investigation of non-Gaussian noise.

中文翻译：

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在 Virgo 干涉仪上执行的声学噪声注入的自适应去噪

使用自适应时间序列分析的方法在来自监测 Virgo 干涉仪北端建筑物 (NEB) 的地震仪的数据上进行了测试，该数据在四次声噪声注入期间进行。经验模式分解 (EMD) 用于自适应去趋势，而最近开发的时变滤波器 EMD 算法用于窄带模式提取。模式持续性通过去趋势波动分析进行评估，并通过在获得的模式的 Hurst 指数上设置阈值 $$H_{\text {thr}}$$H thr 来实现去噪。所采用的方法被证明可用于将声噪声注入引起的地震噪声与地震计监测 NEB 的潜在非线性非平稳记录自适应地分离。Hilbert-Huang 变换提供了数据的高分辨率时频表示。此外，由于与 $$H_{\text {thr}}$$ H thr 相同顺序的注入，局部 Hurst 指数表现出下降。这表明可以将局部 Hurst 指数计算为初始步骤，以便选择阈值 $$H_{\text {thr}}$$H thr 。该算法可用于检测器表征目的，例如非高斯噪声的调查。