|Series||Geophysical References Series|
|Title||Digital Imaging and Deconvolution: The ABCs of Seismic Exploration and Processing|
|Author||Enders A. Robinson and Sven Treitel|
|Store||SEG Online Store|
What is a spiking filter, and how is it computed? A spiking filter is a shaping filter whose desired output is a spike. In such a case, all the coefficients of the desired output are zero, except the coefficient at time zero. For a causal spiking filter with causal input (so that ), the crosscorrelation coefficients for the normal equations are
The normal equations become
How is the prediction-error filter computed? Let h be a causal prediction filter for prediction distance one. Let the input be where occurs at time zero. The desired output is the input advanced by one time unit; that is, the desired output is , where occurs at time zero. Thus, the crosscorrelation is
The solution of the normal equations
gives the coefficients of the prediction operator. The predicted value is
The prediction error is
The coefficients of the prediction-error operator f for prediction distance one thus are found with the equation
This filter is necessarily a minimum-delay filter (Robinson and Treitel, 2000). The prediction-error filter for prediction distance one is the spiking filter normalized so that its leading coefficient is one.
Let us give some numerical examples. Suppose that the input signal is the two-length wavelet , that the filter has two coefficients , and that the desired output signal is the three-length wavelet . The actual output is obtained by carrying out the convolution
The normal equations are
As the first example, suppose that we let the input be the minimum-phase wavelet b = (2, 1) and the desired output be the spike d = (1, 0, 0), where the 1 occurs at time index 0. In other words, we seek to find the two-length filter that will convert the input wavelet into a unit spike. If we substitute the numerical values, the normal equations become
We find that the least-squares spiking filter is given by
It is customary to normalize the spiking filter so that its leading coefficient is one. A spiking filter normalized in this way is a prediction-error filter. Thus, the normalized spiking filter, or the prediction-error filter, is (1, –0.4).
As the second example, suppose that we let the input be the corresponding maximum-phase wavelet b = (1, 2) and the desired output be the same spike. In other words, we seek to find the two-length filter that will convert the input wavelet (1, 2) into the same unit spike d = (1, 0, 0). We note that the maximum-phase input wavelet b = (1, 2) has the same autocorrelation as does the minimum-phase input wavelet b = (2, 1) that we used in the first example. The normal equations now are
We find that now the least-squares filter is given by
Again, the prediction-error filter is (1, –0.4), just as before. The reason for this sameness is that the two input wavelets and have the same autocorrelation. Each wavelet with the same autocorrelation (regardless of whether the wavelet is minimum phase, mixed phase, or maximum phase) is associated with the same prediction-error filter.
Let us use the input wavelet , as before. Let the prediction filter (for one time unit ahead) be and the desired output signal be the two-length wavelet . Then the actual output is obtained by carrying out the convolution
The set of normal equations reduces to the single equation
As the third example, let the input be the minimum-phase wavelet b = (2,1). Then the desired output is d = (1, 0). The normal equation 26 becomes . The prediction-error filter is given by
As the fourth example, let the input be the maximum-phase wavelet b = (1, 2). Then the desired output is d = (2, 0). The normal equation 26 becomes , as before, and we obtain the same prediction-error filter, .
- ↑ Robinson, E. A., and S. Treitel, 2000, Geophysical signal analysis: SEG.
|Previous section||Next section|
|The shaping filter||White convolutional model|
|Previous chapter||Next chapter|
Also in this chapter
- The shaping filter
- White convolutional model
- Wavelet processing
- All-pass filter
- Convolutional model
- Nonminimum-delay wavelet
- Signature deconvolution
- Dual-sensor wavelet estimation
- Deconvolution: Einstein or predictive?
- Appendix I: Exercises