Adaptive Denoising of Acoustic Noise Injections Performed at the Virgo Interferometer

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 Hthr 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 Hthr. This suggests that the local Hurst exponent could be calculated as an initial step in order to select the threshold Hthr. The algorithms could be used for detector characterisation purposes such as the investigation of non-Gaussian noise.