Testing the Optimization-Based Recursive Filtering Algorithm to Suppress Harmonics on Model and Field Correlograms

Geophysical Prospecting

Egorov

Shneerson

2021

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.

Section: Synthetic Examplementioning

confidence: 99%

Optimization‐based recursive filtering for separation of signal from harmonics in vibroseis

Geophysical Prospecting

Egorov

Shneerson

2021

Study of properties of real vibroseis signals contaminated by harmonic noise

Russian Journal of geophysical technologies

Zykov

2022

Properties of the ground force signal complicated by harmonics are studied. It is shown that the adaptation filters, that enable matching the amplitude- and phase- frequency characteristics of harmonics in the corresponding frequency range, have a simple form. The problem of focusing a signal containing harmonics using correlation and deconvolution is discussed. An example of using harmonics to expand the signal spectrum is given.

Improvement of the algorithm for adaptive separation of the vibroseis signal from its harmonics in case of strong additive noise

Russian Journal of geophysical technologies

Zykov

2022

An algorithm for separating a vibroseis signal from its harmonics implies preliminary prediction of harmonics with their subsequent adaptive subtraction from the correlogram. To obtain the adaptation filter estimates, a statistical criterion is used that minimizes the energy of the subtraction result. The amplitudes of the signals in a seismic trace decay due to geometrical spreading, which leads to statistical inhomogeneity in the objective formed. Therefore, an increase in the statistical reliability of estimation should be associated with an increase in signal amplitudes at large recording times. On the other hand, the source records always contain additive noise, and the signal-to-noise ratio decreases at longer times. In order to provide a compromise between the growth of signal amplitudes and maintaining a satisfactory signal-to-noise ratio in the operator adjusting gate, self-tuning weighting functions are included into the objective. A method for modifying the objective is proposed, which enables increased performance of the algorithm.