• 191 Downloads • Abstract Reliable analysis of low-energy earthquakes (microseismic) depends on how accurately one can detect and pick the arrival times, which are strongly influenced by the noise content. The study of microseismic events becomes even more challenging when the sensors are located on the surface because of the poor signal-to-noise ratio (SNR).

Consequently, efficient and robust techniques for denoising microseismic data are necessary. In this study, we propose a method based on an infinite impulse response (IIR) Wiener filter. The proposed method uses statistics based on signal observations (noisy data) and the underlying noise, both recorded by various sensors. The method presented here precludes the need for statistics or prior knowledge of the signal of interest. The second-order statistics of the noise and the noisy data are extracted from the recorded data only.

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We propose a new low-rank based noise attenuation method using an efficient algorithm for tracking subspaces from highly corrupted seismic observations. The subspace tracking algorithm requires. The 3D seismic reflection data from offshore Egypt reveal sediments deposited in. Alternatively, the variation could reflect enhanced input of sediment to the. Or more M > 4.0 earthquakes under the westerly side of the deep, perhaps caused. Mar Geod 32:355–371 Bintanja R, van de Wal RSW (2008) North American.

As an advantage, in deriving the filter’s impulse response, no underlying structure of noise is assumed. Therefore, our method works for various types of noise, e.g., uncorrelated, spatially correlated, temporally correlated, Gaussian and non-Gaussian noise. Hence, the proposed method can be suitable as well for microseismic data recorded in diverse seismic noise environments. The criteria used to optimize the filter impulse response is the minimization of the mean square error. The proposed method is tested on synthetic and field data sets and found to be effective in denoising microseismic data with very low SNR ( (-~12 ) dB).