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The main purpose of this research is to evaluate the effectiveness of coherent noise clustering in reconstructing leaked signals after conventional noise attenuation filters. We use Generalised Auto Regressive Conditional Heteroskedasticity (GARCH) model. We apply clustering, conditional variance, and conditional standard deviation analysis to synthetic and experimental seismic field data. The conditional variance and conditional standard deviation of coherent noises that are attenuated by the Ormsby and f-k filter are calculated. Each cluster is labelled using the two-dimensional average clustering method and then leaked signals are reconstructed from the initially filtered data to improve the signal-to-noise ratio. Results show that the proposed method mostly reconstructs the leaked signals after conventional filters.

Enhanced data fidelity after ground roll attenuation using conditional standard deviation clustering obtained from the GARCH model