Abstract:

With the continuous advancement of oil and gas exploration in China,seismic exploration is facing great challenges.Affected by the complex exploration environment,acquisition method,detector sensitivity and other factors,the obtained seismic data are often mixed with a large amount of random noise,resulting in the decrease in fidelity,signal-to-noise ratio (SNR) and resolution of subsequent seismic data processing,and the accuracy and reliability of geological interpretation are ultimately affected.In order to break through the limitations of traditional seismic data processing problems,a spatially adaptive directional total variation (SADTV) regularization model for random noise suppression of seismic data is proposed.First,aiming at the problem that the seismic reflection events have the directivity of spatial variation and the poor noise resistance of dip angle calculation,a point by point estimation formula for the spatially varying dip angle based on the gradient structure tensor (GST) is proposed to obtain the direction information on events;Then,the denoising model of SADTV seismic data is established,and the Majorization-Minimization (MM) algorithm for solving the model is derived.Finally,the parameter selection method of the model is discussed,and the denoising results of synthetic and real seismic data are compared with those by similar methods.Experimental results show that the proposed model can not only improve the vertical resolution of the seismic profile and the lateral continuity of the seismic event,but also retain more geological feature information while improving the signal-to-noise ratio.

Key words: seismic data, random noise, total variation, majorization-minimization algorithm, gradient structure tensor