Guochang Liu

Stacking seismic data using local correlation

Stacking plays an important role in improving signal-to-noise ratio and imaging quality of seismic data. However, for low-fold-coverage seismic profiles, the result of conventional stacking is not always satisfactory. To address this problem, we have developed a method of stacking in which we use local correlation as a weight for stacking common-midpoint gathers after NMO processing or common-image-point gathers after prestack migration. Application of the method to synthetic and field data showed that stacking using local correlation can be more effective in suppressing random noise and artifacts than other stacking methods.

Time‐frequency characterization of seismic data using local attributes

Seismic data, as a nonstationary signal, can have varying frequency in time. To locally represent frequency components, we present a novel method of computing nonstationary frequency characterization using nonstationary Fourier coefficients, which are computed in an iterative inversion framework. Instead of using sliding windows, we use shaping regularization to control locality of the characterization. The local dominant frequency of seismic data can be estimated from a local frequency characterization map. Adaptive computation of local frequency characterization is an advantage of our method, which is demonstrated by synthetic and field data examples.

Seismic data interpolation using frequency domain complex nonstationary autoregression

We have developed a novel method for missing seismic data interpolation using f-x-domain regularised nonstationary autoregression. f-x regularised nonstationary autoregression interpolation can deal with the events that have space-varying dips. We assume that the coefficients of f-x regularised nonstationary autoregression are smoothly varying along the space axis. This method includes two steps: the estimation of the coefficients and the interpolation of missing traces using estimated coefficients. We estimate the f-x regularised nonstationary autoregression coefficients for the completed data using weighted nonstationary autoregression equations with smoothing constraints. For regularly missing data, similar to Spitz f-x interpolation, we use autoregression coefficients estimated from low-frequency components without aliasing to obtain autoregression coefficients of high-frequency components with aliasing. For irregularly missing or gapped data, we use known traces to establish nonstationary autoregression equations with regularisation to estimate the f-x autoregression coefficients of the complete data. We implement the algorithm by iterated scheme using a frequency-domain conjugate gradient method with shaping regularisation. The proposed method improves the calculation efficiency by applying shaping regularisation and implementation in the frequency domain. The applicability and effectiveness of the proposed method are examined by synthetic and field data examples.

Prestack nonstationary deconvolution based on variable-step sampling in the radial trace domain

The conventional nonstationary convolutional model assumes that the seismic signal is recorded at normal incidence. Raw shot gathers are far from this assumption because of the effects of offsets. Because of such problems, we propose a novel prestack nonstationary deconvolution approach. We introduce the radial trace (RT) transform to the nonstationary deconvolution, we estimate the nonstationary deconvolution factor with hyperbolic smoothing based on variable-step sampling (VSS) in the RT domain, and we obtain the high-resolution prestack nonstationary deconvolution data. The RT transform maps the shot record from the offset and traveltime coordinates to those of apparent velocity and traveltime. The ray paths of the traces in the RT better satisfy the assumptions of the convolutional model. The proposed method combines the advantages of stationary deconvolution and inverse Q filtering, without prior information for Q. The nonstationary deconvolution in the RT domain is more suitable than that in the space-time (XT) domain for prestack data because it is the generalized extension of normal incidence. Tests with synthetic and real data demonstrate that the proposed method is more effective in compensating for large-offset and deep data.

Seismic Attenuation Estimation Using S Transform With Regularized Inversion

The seismic quality factor Q, can provide important information for hydrocarbon exploration. In this paper, we present a method for Q estimation using S transform with regularized inversion. We first use S transform obtain the amplitude spectrum at two different given traveltimes. When using the spectral ratio method, we do the division with shaping regularization, which constrains the ratio to have a desired behavior, such as smoothness. It avoids the instability of division, which might be caused by noise, interfering reflectors, windowing distorts, etc. We use synthetic examples and field data examples to illuminate our method. These examples show that this method can estimate Q in stable way.

Seismic wavefield modeling based on time-domain symplectic and Fourier finite-difference method

Seismic wavefield modeling is important for improving seismic data processing and interpretation. Calculations of wavefield propagation are sometimes not stable when forward modeling of seismic wave uses large time steps for long times. Based on the Hamiltonian expression of the acoustic wave equation, we propose a structure-preserving method for seismic wavefield modeling by applying the symplectic finite-difference method on time grids and the Fourier finite-difference method on space grids to solve the acoustic wave equation. The proposed method is called the symplectic Fourier finite-difference (symplectic FFD) method, and offers high computational accuracy and improves the computational stability. Using acoustic approximation, we extend the method to anisotropic media. We discuss the calculations in the symplectic FFD method for seismic wavefield modeling of isotropic and anisotropic media, and use the BP salt model and BP TTI model to test the proposed method. The numerical examples suggest that the proposed method can be used in seismic modeling of strongly variable velocities, offering high computational accuracy and low numerical dispersion. The symplectic FFD method overcomes the residual qSV wave of seismic modeling in anisotropic media and maintains the stability of the wavefield propagation for large time steps.

A stabilized least-squares imaging condition with structure constraints

Conventional shot-gather migration uses a cross-correlation imaging condition proposed by Clarebout (1971), which cannot preserve imaging amplitudes. The deconvolution imaging condition can improve the imaging amplitude and compensate for illumination. However, the deconvolution imaging condition introduces instability issues. The least-squares imaging condition first computes the sum of the cross-correlation of the forward and backward wavefields over all frequencies and sources, and then divides the result by the total energy of the forward wavefield. Therefore, the least-squares imaging condition is more stable than the classic imaging condition. However, the least-squares imaging condition cannot provide accurate results in areas where the illumination is very poor and unbalanced. To stabilize the least-squares imaging condition and balance the imaging amplitude, we propose a novel imaging condition with structure constraints that is based on the least-squares imaging condition. Our novel imaging condition uses a plane wave construction that constrains the imaging result to be smooth along geological structure boundaries in the inversion frame. The proposed imaging condition improves the stability of the imaging condition and balances the imaging amplitude. The proposed condition is applied to two examples, the horizontal layered model and the Sigsbee 2A model. These tests show that, in comparison to the damped least-squares imaging condition, the stabilized least-squares imaging condition with structure constraints improves illumination stability and balance, makes events more consecutive, adjusts the amplitude of the depth layers where the illumination is poor and unbalanced, suppresses imaging artifacts, and is conducive to amplitude preserving imaging of deep layers.

Stacking Angle-domain Common-image Gathers For Normalization of Illumination

Unequal illumination of the subsurface highly impacts the quality of seismic imaging. Different image points of the media have different folds of reflection-angle illumination, which can be caused by irregular acquisition or by wave propagation in complex media. To address this problem, we present a method of stacking angle-domain common-image gathers (ADCIGs), in which we use local similarity with soft thresholding to decide the folds of local illumination. Normalization by local similarity regularizes local illumination of reflection angles for each image point of the subsurface model. This approach can restore good fidelity of amplitude by selective stacking in the image space, whatever the cause of acquisition or propagation irregularities. We use two synthetic examples to demonstrate that our method can normalize migration amplitudes and effectively suppress migration artifacts.

Structure-enhancing Nonlinear Filtering of Seismic Images

Attenuation of random noise and enhancement of structural continuity can significantly improve the quality of seismic interpretation. We present a novel filtering method, which aims at reducing random noise while protecting seismic structures. The method is based on combining predictive flattening with similarity-mean filtering. We use predictive flattening to form a structural prediction of seismic traces from neighboring traces. We apply a nonlinear similarity-mean filter to select the best samples from different predictions. Parameters of the nonlinear filter allow us to control the balance between eliminating random noise and protecting structural information. Numerical tests using synthetic and field data confirm the effectiveness of proposed structure-enhancing filtering.

Automatic stacking-velocity estimation using similarity-weighted clustering

Local seismic event slopes contain subsurface velocity information, and can be used to estimate seismic stacking velocity. In this paper, we propose a novel approach to estimate the stacking velocity automatically from seismic reflection data using similarity-weighted k-means clustering, in which the weights are local similarity between each trace in common midpoint gather and a reference trace. Local similarity reflects the local signal-to-noise ratio in common midpoint gather. We select the data points with high signal-to-noise ratio to be used in the velocity estimation with large weights in mapped traveltime and velocity domain by similarity-weighted k-means clustering with thresholding. By using weighted k-means clustering, we make clustering centroids closer to those data points with large weights which are more reliable and have higher signal-to-noise ratio. The interpolation is used to obtain the whole velocity volume after we have got velocity points calculated by weighted k-means clustering. Using the proposed method, one obtains a more accurate estimate of the stacking velocity because the similarity based weighting in clustering takes into account the signal-to-noise ratio and reliability of different data points in mapped traveltime and velocity domain. In order to demonstrate that, we apply the proposed method to synthetic and field data examples and the resulting images are of higher quality when compared to the ones obtained using existing methods. This article is protected by copyright. All rights reserved