The performance of the algorithm for removing harmonic noise distortions of the vibroseis wavelet on the synthetic and the field data is studied. At the first stage, the noise is predicted. At the second stage, its adaptive subtraction from the original correlograms is performed. The algorithm demonstrates good performance, most clearly its advantages are manifested in slip-sweep data processing.
Harmonic noise may significantly complicate the processing of slip‐sweep vibroseis data. We propose a model of this noise and an optimal recursive filtering algorithm based on this model. In contrast to some alternatives, this method can remove harmonic noise caused by all the events on the seismic gather, instead of only removing the noise associated with the first arrivals. First, the algorithm predicts a number of noise models that correspond to the harmonics of different orders. Second, these models are subtracted from the input gather via adaptive subtraction, which estimates frequency‐dependent relative amplitudes of harmonics and introduces the needed phase shifts into the noise models. When applied to the field vibroseis data, the proposed algorithm successfully separates the harmonic noise from the signal.
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