Simultaneous multitrace impedance inversion with transform-domain sparsity promotion

Abstract

The impedance inversion technique plays a crucial role in seismic reservoir properties prediction. However, most existing impedance inversion methods often suffer from spatial discontinuities and instability because each vertical profile is processed independently in the inversion. We tested a transform-domain sparsity promotion simultaneous multitrace impedance inversion method to address this issue. The approach was implemented through minimizing a data misfit term and a transform-domain sparsity constraint term that incorporates the (2D or 3D) structural information into the inversion processing. A 2D synthetic data example was applied to mainly explain the roles of the transform-domain sparsity constraint. We determined that the transform-domain sparsity constraint can help in stabilizing the inversion, reducing the influence of high-wavenumber noise on the inverted result, and exploring spatial continuities of structures. Furthermore, a 3D field data example was used to examine the effectiveness of the proposed method for dealing with the real data and to reveal the difference between the results from the 3D simultaneous inversion and the section-by-section inversion. We found that the inverted results roughly matched a lowpass-filtered version of impedance curves derived from well log data. Also, it has been demonstrated that the 3D simultaneous inversion technique provided a better estimation than the section-by-section inversion technique in terms of guaranteeing more spatial continuities of geologic features in the impedance model.

Reconstruction of seismic data with missing traces based on local random sampling and curvelet transform

“…At present, the reconstruction methods of missing traces can be divided into three types: the first one is transform-based approach such as Fourier transform (Sacchi et al, 1998;Abma, 2005;Xu et al, 2005;Zwartjes and Sacchi, 2007;Meng et al, 2008;Kaplan et al, 2010;Yuan et al, 2015), Radon transform (Trad et al, 2003) and curvelet transform Donoho, 2000, 2004;Candes et al, 2006;Herrmann and Hennenfent, 2008). This method does not require a priori assumption; the reconstruction process is relatively stable and is now in use widely.…”

Low-frequency data analysis and expansion

“…They pointed out that the original signal can be reconstructed by using the optimal sparse reconstruction algorithm based on the signal sparse prior information, and the signal of the adaptive linear projection for sampling far below the Nyquist frequency. In signal reconstruction, we generally obtain the solution using sparse constraints (Yuan et al, 2015;Han et al, 2012). Presently, compressed sensing has been used in seismic data recovery (Herrmann et al, 2006;Bai et al, 2014), plane wave decomposition , and other cases.…”

The roles of the spatial regularization in seismic deconvolution

“…Generally, the robust inversion (e.g. Crase et al 1990;Yuan et al 2015) is a choice to deal with the undersampled data set or low quality migration data set to obtain an acceptable inversion result. In this paper, we illustrate that the spatial regularization can help recovering the reflectivity of the missing and/or poor-quality traces by slightly rewriting Eq.…”

“…Besides ignoring the spatial connection among traces, trace-by-trace processing often suffer from the lateral instability of the estimated reflectivity or impedance (e.g. Zhang et al 2013;Yuan et al 2015), probably mainly due to the influence of high-wavenumber components in model error or the inconsistency of the energy and waveforms among seismic traces.…”