deconvolution bryce hutchinson sumit verma objectives: -understand the difference between...

Deconvolution

Bryce Hutchinson Sumit Verma Objectives:- Understand the difference between exponential

and surface consistent gain- Identify power line noise is the frequency

spectrum and remove it - Grasp what deconvolution is doing to the data

Exponential Gain

1. Choose the 10-100 band pass volume we created last week as the input. 2. Set the Exponential Gain Constant to 2

Exponential gain multiplies each trace by an exponential function of the form e(nt) where we define what the exponential gain constant (n).So, according to the formula the gain factor will increase with increasing time (which you can think of as increasing depth).

Bandpass

Bandpass with Exp gain

See the effect of Exponential gain!

exponential gain : exp(nt) or ent, we kept n=2 and t= time.

What is this noise ? /// Have you seen it in exam!

Noise in the frequency spectrum

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3. There is a consistent noise at 60Hz in the frequency spectrum. Zoom in to see it better, also take note of how the noise looks in both the trace and frequency spectrum

1. Click the seismic analysis icon 2. Choose Frequency Analysis Window from the dropdown menu. Then the green plus to view it.

Zoomed in

Power line noise

Surface consistent (SC) gain I’ve copied the input windows for each of flow icons in this slide. I have put the flow icon in the top right of each window to show you which input it represents.

4. For SCApply, you will not have a choice of inputs yet. Be sure the bin size is set to 220. You must run the SCApply before these options will appear!

1. Choose the bandpass data as the input

2. We want to create a tight window around the power line noise in the notch filter

3. Change the offset bins size in the Options tab and select the Entire Trace in the Window tab

Power line noise removal

Surface Consistent ScaleWhy : Surface-consistent scaling is used on land seismic data in order to compensate for the effects of the highly variable near-surface layers and variable source and receiver signatures and coupling. M. Turhan Taner and Fulton Koehler (1981). ”Surface consistent corrections.” Surface consistent corrections, 46(1), 17-22.

How in this software : In the SCScale the average amplitude (Mean or RMS) of each input trace is computed. Each trace scalar is assumed to be of the form:Trace Multiplier = Shot multiplier x Receiver Multiplier x Offset dependent Multiplier x CMP multiplier.

By utilizing logarithms, this equation becomes a sum of factors rather than a product. The sum can then be solved by the usual Gauss-Seidel iterative process.

Surface Consistent Gain

1. Run the flow again, and now you will have inputs for SCApply

2. Your output should appear something like this. Don’t worry about subtle differences between this image and yours, we ran our flow in a different order.

Compare the bandpassed data and SC gained data, what differences do you see?

Before sc gain

What is this noise ? /// Have you seen it in exam!

After sc gain

what step removed it ??? Do you see a better amplitude balance?

• We have done several experiments to change

You can change: Iterations and offset bin size.And Time window

We experiment here !

Surface Consistent Scaling

Deconvolution Surface Consistent Deconvolution

Surface Consistent Scaling

Surface Consistent Deconvolution

Experiment # 1 Experiment # 2 Experiment # 3

We will complete this part here ! We will upload this in lab 7b!

Defining a timegate

2.

1.

3.

Define the top and bottom of the timegate. Double click to end your pick. You can define the window on the raw data.

Deconvolution

2. Set the Decon Type to Zero-Phase (since our source was vibroseis)

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1. This is the deconvolution flow. Use the SC gain data as the input.

This is seismic was acquired with Vibroseis!

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5

Surface Consistent Scaling

Deconvolution

Experiment # 1

Decon assumptions

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1. The earth is composed of horizontal layers of constant impedance

2. The source generates a compressional wave at normal incidence – there are no shear waves

3. The source wavelet does not change as it travels – it is stationary

4. The noise component n(t) is zero

5. The source waveform is known

6. The reflectivity is random (autocorrelation and spectra are similar)

7. The seismic waveform is minimum phase and thus has a minimum phase inverse

Dr. Marfurt’s class slide

Vibroseis has zero phase source wavelet !

Decon assumptions

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The earth is composed of horizontal layers of constant impedance decon works if the multiple generators are flat

The source generates a compressional wave at normal incidence – there are no shear waves

works ok at near offsets

The source wavelet does not change as it travels – it is stationary use time-variant decon in running windows

The noise component n(t) is zero design operator on noise-free parts of the data; design decon operators

on stacked traces and then apply to prestack data

Dr. Marfurt’s class slide

Decon assumptions

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The source waveform is knownIf source wavelet is minimum phase, you can obtain a near perfect result

The reflectivity is random (autocorrelation and spectra are similar)If the source wavelet is not known, you are in serious trouble!

The seismic waveform is minimum phase and thus has a minimum phase inverse

The result of spiking decon is degraded if the source wavelet is not minimum phases

(Yilmaz, 2001; p 190)Dr. Marfurt’s class slide

In VISTA they have Zero Phase decon , in which they calculate zero phase decon operator .

DeconvolutionSpiking Decon:- Wiener Levinson algorithm- Auto-correlation of a segment of the

trace which normally varies with offset (think normal equations) is computed

Further – Zero Phase:- Forward Fourier Transform is

calculated giving the amplitude and phase spectrum

- Phase spectrum is then set to zero and an inverse transform is performed

- Resulting in a zero phase equivalent of the spiking (minimum phase) operator.

- The zero phase operator is then convolved with the data resulting in an image showing the reflectors much tighter

The autocorrelation function looks like:

Assume the Earth’s input xt is a series of spikes approximated by a random Gaussian distribution:

Output

2. Set Compare your output deconvolution to the SC gained data and to the band pass data. What changes do you see?

Input for SC gain

After SC gain Output

After Zero phase decon Output

Please check the amplitude spectrum.

After Deconvolution

After Surface consistent gain Before Surface consistent gain

Notch filter to remove the powerline noise

Deconvolution shows better balance in frequency spectrum.

Deconvolution generates some artifacts on the higher frequencies

OutputAfter Zero phase decon

So we apply band pass filter to remove the noises

After Zero phase decon and bandpass filter

Experiment : 2 Surface Consistent Deconvolution

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3.13.2

3.3

4

4.24.1

Experiment 2 Result!

After Zero phase decon and bandpass filter

Experiment 1 Result!

Which one do I like better !

Processor’s dilemma !

Acknowledgement !

• Dr. Kurt Marfurt• Thang Ha• Marcus Cahoj for giving inspiration for this lab.