Multichannel Deconvolution of Seismic Signals Using Statistical MCMC Methods

Abstract

Abstract-In this paper we propose two multichannel blind deconvolution algorithms for the restoration of two-dimensional (2D) seismic data. Both algorithms are based on a 2D reflectivity prior model, and use iterative multichannel deconvolution procedures which deconvolve the seismic data, while taking into account the spatial dependency between neighboring traces. The first algorithm employs in each step a modified maximum posterior mode (MPM) algorithm which estimates a reflectivity column from the corresponding observed trace using the estimate of the preceding reflectivity column. The second algorithm takes into account estimates of both the preceding and subsequent columns in the estimation process. Both algorithms are applied to synthetic and real data and demonstrate better results compared to those obtained by a single-channel deconvolution method. Expectedly, the second algorithm which utilizes more information in the estimation process of each reflectivity column is shown to produce better results than the first algorithm.Index Terms-Reflectivity estimation, multichannel deconvolution, seismic signals, Markov Chain Monte Carlo, maximum posterior mode method.

Sparse Multichannel Blind Deconvolution of Seismic Data via Spectral Projected-Gradient

“…In a seismic survey, the convolution of the source wavelet with a subsurface reflectivity series is recorded as a seismic trace. In a multichannel scenario [8], [11]- [13], the seismic traces are typically modeled as convolutions of the same waveform with multiple reflectivity models. Early work on seismic blind deconvolution depended on two major assumptions: the impulse response of the earth is a white sequence and the source wavelet is minimum phase.…”

Seismic Signal Deconvolution Using a Maximum Posterior Mode Algorithm

“…In order to quantify and compare the performances of the multichannel and single-channel algorithms, we used the four loss functions suggested in Kaaresen (1998), and three new ones defined in Ram et al (2010). The means and standard deviations of the loss functions calculated for the estimates obtained by single-channel and multichannel deconvolution are shown in percents in Table 1.…”

“…In this paper, which summarizes some of the results in Ram et al (2010), we introduce a multichannel blind deconvolution algorithm. The algorithm is based on the MBG I reflectivity model and iteratively deconvolves the seismic data, while taking into account the spatial dependency between neighboring traces.…”

Three-dimensional sparse seismic deconvolution based on Q attenuation model

“…Multichannel seismic deconvolution methods promote horizontal continuity of the seismic reflectivity by considering more than one trace in each channel estimation (Idier and Goussard, 1993;Mendel et al, 1981;Kormylo and Mendel, 1982;Kaaresen and Taxt, 1998;Heimer et al, 2007;Cohen, 2009, 2008;Ram et al, 2010;Gholami and Sacchi, 2013;Mirel and Cohen, 2017;.…”