Noise attenuation without spatial assumptions about seismic coherent events

Summary Some conventional noise attenuation methods in seismic data processing often need assume that coherent events are piecewise-stationary, piecewise-linear and regularly sampled along spatial direction. In this paper, without any spatial assumptions about coherent signal, a noise attenuation method using Bayesian inversion is presented. Its essential idea is to directly invert the “clean” data, regarded as model parameter, from observed seismic data by maximizing a posterior distribution, which is made up of prior distribution and likelihood function. Whether this method can reduce noise is dependent on the choice of prior information. Based on a statistical knowledge that coherent data oscillates slightly and random noise strongly, the minimization of L1 norm of model parameters’ difference quotient, also called as total variation, is used as prior information. What advantage of this method is that it can enhance nonstationary and nonlinear seismic events. Moreover, the de-noised effect neither strongly relies on the size and layout of time windows nor depends on whether traces are sampled regularly. Especially, this method has good ability for preserving edges of discontinuous events, which often correspond to important geologic features, and deblurring amplitude’s variation along spatial direction, which is probably AVO response. A model data and a real data are used to test its validity.