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Increasing seismic wavelet resolution by using Time - variant Wiener Deconvolution
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Seismic wavelet undergoes many changes when it first started from the seismic energy source to recorded at the receivers. The wavelet shape changes due to attenuation, dispersion and geological changes like lithology etc. The objective of any deconvolution method is to improve the resolution of the seismic wavelet.
In this time variant Wiener deconvolution, it presumes that the seismic wavelet is non-stationary and changes it's properties like shape and amplitude with varying time. To improve the resolution temporally, we design a time varying filter that improves the resolution of the wavelet with respect to change of time.
Seismic trace/wavelet is nothing but convolution of source wavelet with reflectivity. This also includes some noise component. As the source wavelet propagates through the sub-surface, it loses the energy and the higher frequencies (travel faster) get attenuated fast compared to the lower frequencies. To restore the reflectivity, we need to design a time-varying Wiener filter which minimizes the difference between the estimated reflectivity and original reflectivity.
Seismic trace = (source wavelet * reflectivity) + noise
How does time varying Wiener deconvolution filter works?
•Estimate the seismic wavelet at each time.
•Design a Wiener filter with varying time for the estimated wavelet.
•Convolve the seismic trace with the Wiener filter to get the deconvolved seismic trace.
•The resultant seismic trace is the estimated reflectivity which is much sharper.

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In the above parameters table, from time 0 to 1000 ms, with a time window of 200 ms, Wiener deconvolution filter works on this time window range and move to the next time window.
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Notify - It will notify the issue if there are any bad values or NaN. This is halt the workflow execution.
Fix - It will fix the bad values and continue executing the workflow.
Continue - This option will continue the execution of the workflow however if there are any bad values or NaN, it won't fix it.
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There is no information available for this module so the user can ignore it.
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In this example workflow, we are showing the results of a CDP gather and an inline stack section response after Time-variant Wiener Deconvolution.

In the 1st workflow, we've assigned parameters as per the input data as shown in the above image. User should pay attention to the % Prewhitening noise. Adding too much Prewhitening noise boosts the amplitudes and noise component also.


In the 2nd workflow, we've created an inline stack with and without Time variant Wiener Deconvolution filter. After the application of Time variant Wiener Deconvolution filter, the stack response is much sharper with an improvement in the spectral response.


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YouTube video lesson, click here to open [VIDEO IN PROCESS...]
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Yilmaz. O., 1987, Seismic data processing: Society of Exploration Geophysicist
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