Seismic data are highly corrupted with noise or unwanted energy arising from different kinds of sources. Generally, the noises are classified as two groups, random noise and coherent noise, and are treated with different methods. Traditional methods often utilize the differences in frequency, wave number or amplitude to separate signal and noise. But the application of the traditional methods is limited if the differences are too small to distinguish. In this abstract, we proposed a novel morphology based method to extract signal from the noisy data, i.e., to simultaneously attenuate random and coherent noise. The input data is first transformed into a stratigraphic coordinate system. We then utilize information of different morphological scales along the first axis direction of the input data to separate useful signal and noise. Application of this proposed novel method on two field data examples demonstrates a successful performance.


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