Noniterative f-x-y streaming prediction filtering for random noise attenuation on seismic data

Next: Data processing path in Up: Theory Previous: Theory

## 3D - - streaming prediction filtering

A 2D seismic section containing linear events can be described as a plane wave function in the - domain. In the - domain, the linear events in seismic section are decomposed into a series of sinusoids. These sinusoids are superimposed and become harmonics at each frequency, which shows the prediction relationship of seismic traces in a frequency slice:

 (1)

where and are the indices of the seismic sample along the axis and axis, respectively. is the index of filter coefficients along the direction. denotes the data point in and indicates the filter coefficient in the - domain. When curve events or amplitude-varying wavelets are shown in the seismic data, filter coefficients change from one data point to the next, which help to manage the nonstationary case:

 (2)

where denotes the transpose operator, denotes the vector including the data points near . is the vector of coefficients in a 2D adaptive prediction filter. Let , Fig. 1a illustrates how (2) works. Equation (2) denotes that the filter predicts data point along the spatial direction rather than the frequency direction. Therefore, an extension to the 3D - - domain is straightforward:

where is the index of the data sample along the axis. and are the indices of filter coefficients in two spatial directions, is the vector of 3D filter coefficients. The adaptive prediction filter is defined as a space-noncausal structure and the filter size in the spatial direction is . The vector contains the data points near . Let and , Fig. 2a demonstrates the distribution of the vectors and . Assuming that the contained noise is white Gaussian noise, the filter can be obtained by solving the minimization problem:

 (4)

equation (4) describes an ill-posed problem that the number of the unknown filter coefficients is greater than that of the known equations. Without any regularization, the equation will lead to an unstable solution:

 (5)

where denotes the conjugate operator.

To solve the underdetermined problem (4), constraint conditions based on local similarity/smoothness are used to stabilize the solution of (4). Assuming that the adaptive prediction filter at position is similar to another one at position in the - - domain, can be treated as the constraint condition on the axis. The autoregression equation can be expressed as follows:

and the simplified block matrix can be written as:

 (7)

Equation (7) is solvable since there are equations with unknown coefficients, which correspond to the following minimization problem:

 (8)

where is the constant weight for the regularization term along the axis. In the frequency direction, one can assume that the SPFs change smoothly and treat the irregular perturbations as the interference of noise. Meanwhile, the smoothness of the 3D SPFs also exists in different spatial directions and may change at different data point, therefore, we implemented local smoothness along , , and axis as constraints to calculate the - - SPF. The block matrix form is:

 (9)

and the corresponding least-squares problem takes the following form:

where , and denotes the variable weights of regularization terms along the frequency axis, space axis, and space axis, respectively. They measure the variable similarity or closeness between the filter and the adjacent filters , , and . Due to the prediction filter can characterize the energy spectrum of the input data Claerbout (1976), the adaptive filter shares analogical smoothness property with the 3D data, so the variation of the weights may consist with the smooth version of data. For simplicity, we select these weights with constant value, , and , to demonstrate the constrained relationship. The introduced regularization terms convert the ill-posed problem to the overdetermined inverse problem, and the least-squares solution of (9) and (10) is:

where

 (12)

The Sherman-Morrison formula is an analytic method for solving the inverse matrix Hager (1989):

 (13)

The derivation of the Sherman-Morrison formula in the complex space is described in Appendix . Elementary algebraic simplifications lead to the analytical solution:

Equation (14) is a recursion equation, which suggests that the filter recursively updates in a certain order. The residual can be written as:

 (15)

Once obtaining the solution of the 3D - - SPF, one can compute the noise-free data with the following equation:

 (16)

The configuration of parameters , , and is the basis for the proposed method. When the three parameters are 0 , the corresponding regularization terms have no effect on restricting the inverse problem and the result of the - - SPF becomes (5). By choosing and removing the axis, equation (14) is reduced to the solution of the 2D - SPF. On the contrary, when the three parameters tend to infinity, more weight is applied on regularization terms. A large denominator in (14) indicates that the filter cannot receive any updates to maintain its adaptive and predictive properties. This denominator suggests that parameters and in (12) should have the same order of magnitude as , and the value of might be in the range of , which can balance the noise suppression and signal protection. Therefore, they can smoothly adjust the change of filters. Meanwhile, data distribution in the frequency axis may change sharply, which is not as smooth as those in the spatial directions; thus, should be smaller than and .

fig1,fig2
Figure 1.
Schematic illustration of - prediction filter (a) and filter processing path (b).

fig3,fig4
Figure 2.
Schematic illustration of - - prediction filter (a) and filter processing path in each frequency slice (b).

 Noniterative f-x-y streaming prediction filtering for random noise attenuation on seismic data

Next: Data processing path in Up: Theory Previous: Theory

2022-04-21