Prestack structurally constrained impedance inversion

Abstract

Classical prestack impedance inversion methods are based on performing a common-depth point (CDP) by CDP inversion using Tikhonov-type regularization. We refer to it as lateral unconstrained inversion (1D-LUI). Prestack seismic data usually have a low signal-to-noise ratio, and the 1D-LUI approach is sensitive to noise. The inversion results can be noisy and lead to an unfocused transition between vertical formation boundaries. The lateral constrained inversion (1D-LCI) can suppress the noise and provide sharp boundaries between inverted 1D models in regions where the layer dips are less than 20°. However, in complex geology, the disadvantage of using the 1D-LC approach is the lateral smearing of the steeply dipping layers. We have developed a structurally constrained inversion (1D-SCI) approach to mitigate the smearing associated with 1D-LCI. SCI involves simultaneous inversion of all seismic CDPs using a regularization operator that forces the solution to honor the local structure. The results of the 1D-SCI were superior compared with the 1D-LUI and 1D-LCI approaches. The steeply dipping layers are clearly visible on the SCI inverted results.

Sparse seismic reflectivity inversion using an adaptive fast iterative shrinkage‐thresholding algorithm

“…For two-dimensional seismic reflectivity inversion, in other words, the reflectivity inversion of seismic profiles, a multichannel inversion method is usually adopted to better represent the lateral changes in the strata structure Pidlisecky, 2015, 2016;Wang et al, 2017;Hamid et al, 2018; Zhang Li et al, 2018;. In two-dimensional multichannel case, the convolutional model of equation ( 1) can be rewritten as follows:…”

“…For two‐dimensional seismic reflectivity inversion, in other words, the reflectivity inversion of seismic profiles, a multichannel inversion method is usually adopted to better represent the lateral changes in the strata structure (Hamid and Pidlisecky, 2015, 2016; Wang et al ., 2017; Hamid et al ., 2018; Zhang et al ., 2018; Li et al ., 2018; Dai et al ., 2021). In the two‐dimensional multichannel case, the convolutional model of equation () can be rewritten as follows: $$\begin{equation}{{\bf S = \overset{\scriptscriptstyle\frown}{W}R + n}},\end{equation}$$where $${{\bf S}} = {[ {{{{\bf s}}_1},{{{\bf s}}_2},\ldots,{{{\bf s}}_m}} ]^T}$$ and $${{\bf R}} = {[ {{{{\bf r}}_1},{{{\bf r}}_2},\ldots,{{{\bf r}}_m}} ]^T}$$ represent the concatenated vectors of seismic traces and reflection coefficient sequences, respectively.…”

A Structure-Guided and Sparse-Representation-Based 3d Seismic Inversion Method

“…(3) are implemented as 1D inversion frameworks with each 1D trace being inverted independently. Following [13], we refer to these methods as lateral unconstrained inversion (1D-LUI). Since the seismic data, especially the prestack seismic data, usually have a low signal-to-noise ratio.…”

“…[3] proposed to use a 2D TV constraint to improve spatial and temporal correlation of solutions. [13] pointed out that the disadvantage of naive lateral constraint inversion [14,LCI] is lateral smearing of the steeply dipping layers, thus they developed a structurally constrained inversion (SCI) approach by using dip information. However, the dip-based SCI relies on obtaining accurate dip information which is difficult in the real world.…”

“…However, the dip-based SCI relies on obtaining accurate dip information which is difficult in the real world. Meanwhile, [13] is only designed for 2D inversion, which becomes extremely complex in 3D case. In fact, there is very little research on 3D inversion at present, not because it is not important, but because a good 3D inversion scheme may be computationally time-consuming, thus many scholars only stay on the study of 2D inversion.…”