final study of typical processing methods and their parameters in advance seismic data...

Study of typical processing methods and their parameters in advanced seismic data processing workflow

SUPERVISOR: PROFESSOR. LIU CAI BY

MOHAMED MHMOD

2016

Thesis Outline

Thesis BackgroundThesis highlightsConclusions

Special analysis

2D transform Input data 2D,3D data

Signal enhancement

Deconvolution

Filtering

General applications

Time variant frequency analysis

F-K analysis

Time variant amplitude analysis

Signal /noise frequency estimation

Effect of trace spacing

Effect of smoother trace

Signal /noise calculation options

Crosscorrelation

Multi Coherence

SVD

Predicative deconvolution

Spiking deconvolution Effect of operator length

Effect of percent prewhiting

Effect of operator length

Effect lags

Land 2D data (PSTM)

FX-FK application t

Radial Trace Transform

Tau-p transform

Forward

inverse

in F-K domain

in F-X domain

Forward

inverse

in F-K domain

in F-X domain

2DFX-Deconvoltution

3D FKx-Ky Linear

Logifer Design

Alpha trim Mean Filter.

2D F-X predication Levinson-Durbin filter 2D F-X predication Cadzow filter 2D F-X predication Burg recursive2D F-X predication F-X projection

2D F-X predication F-X Deblur

THESIS BACKGROUND

Ormsby Band Pass

Butter worth Filter

Notch filter

In frequency domain

In time domain

THOR 2D/3D theshod

Random noise attenuation

Post stack migration FDMig 2d

Parameters effect on PSTM data

FX

4D-DEC in stacked data

Predication

projection

SPECIAL ANALYSIS

Effect of trace space& Effect of Smoother trace

input F-K Filter Design Parameters with apply AGC window 250 ms.

(e) Tracing space 50m

(a) Input Data Example with, Pie Rejection Applied, (b) with tracing space =0, raw, (c) tracing space =0.5m, (d) tracing space 25m,( e) tracing space 50m,data sorted in Shot order 2d crooked line.

(c ) Tracing space 0.5 m (d) Tracing space 25m (a) Input data (b) Tracing space =0m

(a) Input Data ) F-K Filter Display , with Pie Rejection Applied,( b) F-K Filter Display with Smoother trace 50,(c) Smoother trace 100, raw data sorted in Shot order, 2d data.

(a) 2d Input data (b) Smoother trace= 50 (c) Smoother trace 100

(d) 3d Input Data, Example with Pie Rejection Applied, (e)3d smoother trace=50, (f) 3d smoother trace=100 raw data 3d land.

(d) 3d Input Data (e) Smoother trace= 50 (f) Smoother trace 100

Special analysis

Special analysis Time window Variant Frequency Analysis

Case

Length of

window ms

Lower frequency limit Hz

Upper frequency limit Hz

1 100 0 602 50 0 603 200 0 604 100 0 1005 100 20 60

Time Variant Frequency Analysis display Length of window 100ms

, Lower frequency limit 0 Hz, upper frequency limit 60Hz.

Time Variant Frequency Analysis display Length of window 50ms,

Lower frequency limit 0 Hz, upper frequency limit 60Hz.





Time Variant Frequency Analysis display Length of window

100ms, Lower frequency limit 20 Hz, upper frequency limit 60Hz.

Special analysisTime Variant Amplitude Spectrum

Length FFT window ms1 256

2 150

3 350

Time Variant Amplitude Spectrum, Length FFT window values were used.

Time Variant Amplitude Spectrum display with Length FFT window 256 ms.

Time Variant Amplitude Spectrum display

with length FFT window 150 ms.Time Variant Amplitude Spectrum display

with Length FFT window 350 ms.

Special analysis


Signal enhancement

Deconvolution

Filtering



F-K analysis






Crosscorrelation

Multi Coherence

SVD





Effect lags

Land 2D data (PSTM)

FX-FK application t


Tau-p transform

Forward

inverse

in F-K domain

in F-X domain

Forward

inverse

in F-K domain

in F-X domain

2DFX-Deconvoltution

3D FKx-Ky Linear

Logifer Design




Ormsby Band Pass

Butter worth Filter

Notch filter

In frequency domain

In time domain

THOR 2D/3D theshod




FX


Predication

projection

Spiking and Predicative deconvolution

Performing predictive deconvolution on single traces. The Effect of Operator Length

Predictive Deconvolution

INPUT DATA

Operator Length (n) ms Pre-Whitening 1%

OUTPUT

Amplitude scaling (Mean scale)

K1 Surface Consistent Decon Flow_K2_scdecon_apply (with predication deconvolution)

OUTPUT

Work flow were used.

(a) (b) (c) (d)

(a) Data before applying predication deconvolution, (b) after applying predictive Deconvolution with operator length 240 ms,. The predication lag (α=2 ms), and the percent prewhitening

(1%).also using Amplitude scaling (Mean scale), (c) after after applying predictive Deconvolution with operator length 128 ms,The predication lag (α=2 ms), and the percent prewhitening

(1%).also using Amplitude scaling (Mean scale),(d) after applying predictive Deconvolution with operator length 40 ms,The predication lag (α=2 ms), and the percent prewhitening (1%).also

using Amplitude scaling (Mean scale).

(a) Data before applying predication deconvolution (b) operator length 240 ms

(c) Operator length 128 ms (d) operator length 40ms

comparison Spectral analysis Amplitude with frequency before and after applying predication deconvolution ((n=240, 128, 40) ms), trace

1 in linear display (blue is trace and red is average).

Spiking and Predicative deconvolutionFirst step

Spiking and Predicative deconvolutionSecond step :Applying SCDsolve flow

after applying SCDsolve, Shot _sequence number: Predication deconvolution for operator length (a) 240 ms, (b) 128 ms, and (c) 40 ms.

after applying SCDsolve, RECV_SEQUENCE_NUMBER: Predication deconvolution, (a) deconvolution for operator length 240 ms, (b) 128 ms, (c) 40 ms. (d), data before applying SCDsolve.

(a)Operator length 240 ms (b) Operator length 128 ms (c) Operator length 40 ms

(a)Operator length 240 ms (b) Operator length 128 ms (c) Operator length 40 ms

Step 3

show the flow were used for third step.

(a) (b) (c ) (d) (e) (f)

after applying Predictive and SC predication Deconconvolution, (a) operator length 240 ms, (b) operator length 128 ms,(c) operator length (40 ms). After Spectral Balancing, (d) with operator (240 ms), (e) 128 ms, (f) with operator (40 ms).


Scale

Optional Output


The Effect of Operator Length on performing spiking deconvolution on single traces.

Spiking Deconvolution

INPUT DATA

OUTPUT

K1 Surface Consistent Deconvolution

Flow_K2_scdecon_apply (with spiking

deconvolution)

OUTPUT

Amplitude scaling (Mean scale)

Work flow processes

(d) (e) (f) (a) (b) (c)

(a) Data before applying spiking deconvolution, (b) after applying spiking Deconvolution with operator length 240 ms, and the percent prewhitening (1%), (c) after applying spiking Deconvolution with operator length 128 ms, and the percent prewhitening (1%),(d) after applying spiking deconvolution with operator length 40 ms,), and the percent

prewhitening (1%),(e) after applying spiking Deconvolution with operator length 20 ms, and the percent prewhitening (1%),(f) after applying spiking Deconvolution with operator length 10 ms, and the percent prewhitening (1%).


(a) (b) (c ) (d) (e)(a) 2d land final PSTM Data CMP sort, before applying spiking deconvolution, (b) after applying spiking Deconvolution with operator length 240 ms, (c) after applying spiking

Deconvolution with operator length 128 ms,(d) after applying spiking Deconvolution with operator length 40 ms,(e) after applying spiking Deconvolution with operator length 10 ms and the, percent prewhitening for all values of operators length (0%).

(a). (b) (c ) (d) (e)

(a) 2d land final PSTMData CMP sort, before applying spiking deconvolution, (b) after applying spiking Deconvolution with operator length 240 ms, (c) after applying spiking Deconvolution with operator length 128 ms,(d) after applying spiking Deconvolution with operator length 40 ms,(e) after

applying spiking Deconvolution with operator length 10 ms and the, percent prewhitening for all values of operators length (1%).

Spiking and Predicative deconvolution Performing spiking and predictive deconvolution on (PSTM) land 2D data

Spiking and Predicative deconvolution•Second predictive deconvolution, this step creates a new data set with applied trace by trace predictive. No sort is required. For design window, is the entry trace, operator length (240, 128, 40) ms. The predication lag is unity and equal to (α=0 ms, α=1ms,α=2ms) sampling rate. The percent prewhitening (1%).also using amplitude scaling (Mean scale) with applying signal band pass filter (Low Truncation frequency 10 HZ, Low Cut frequency 15 HZ, High-Cut frequency 200, High Truncation frequency 250 HZ).

(a) (b) (c) (d) (e)

(a) 2d land pstm CMP sort, before applying predictive deconvolution, (b) after ap-plying predictive Deconvolution with operator length 240 ms, (c) after applying predictive De-convolution with operator length 128 ms,(d) after applying predictive Deconvolution with operator length 128 ms,(d) after applying predictive Deconvolution with operator length 40 ms,(e) after applying predictive

Deconvolution with operator length 10 ms and the, lag for all value of operator length(α=0 ms), and the percent prewhitening for all value of operator length (1%).

(a) (b) (c) (d) (e) (f)

(a) before applying predictive deconvolution, after applying predictive deconvolution with operator length 240 ms, (b) after applying predictive deconvolution with operator length 128 ms, (c) after applying predictive deconvolution with operator length 10 ms and the, lag for all value of operator length(α=1ms), and the percent prewhitening for all value of operator length

(1%).(e). Final pstm, (f) data after applying predictive deconvolution.

Special analysis


Signal enhancement

Deconvolution

Filtering



F-K analysis






Crosscorrelation

Multi Coherence

SVD





Effect lags

Land 2D data (PSTM)

FX-FK application t


Tau-p transform

Forward

inverse

in F-K domain

in F-X domain

Forward

inverse

in F-K domain

in F-X domain

2DFX-Deconvoltution

3D FKx-Ky Linear

Logifer Design




Ormsby Band Pass

Butter worth Filter

Notch filter

In frequency domain

In time domain

THOR 2D/3D theshod




FX


Predication

projection

2D transform FX-FK Filter application to remove or isolate Linear Noise and 3D Linear Noise.

Input data 2d/3d FK-FX FILTER FK-FX data

Sub input

Ormsby band filter

Adaptive subtraction

Removal noise

Input 2

2d land data raw data Shot point sort, 120 traces Fk-Fx data, 2d land data after applied FK-FX Filter, Shot point sort, 120 traces. After applied flow , Removal noise.

3d raw data Shot point sort, 192 traces. Fk-Fx data, 3d land data after applied FK-FX Filter. 3d land data after applied flow in , 3D Linear Noise removal.

2D transform

(a) raw data, 2d land data. (b)After Applied Radial Transform Forward. (c)After Applied Radial Transform Forward + AGC. (d) After Applied Radial Transform Forward+ Ormsby pass band filter,(e) ) After Applied Radial Transform Forward+ Ormsby pass band filter+ AGC.(f) After Applied Ormsby pass band filter +Radial Transform Forward+ Ormsby pass band filter+ AGC,(h) difference

between (e,f).

(a) (b) (c) (d)

(e) (f) (h)

shows the flows used in first step.

2D transform A Review of Forward Radial Trace Transform

(1) (2) (3) (4) (5)

Input raw data Radial Transform Forward (RTFOR)

Output

INPUT RTFOR AGC OUTPUT

INPUT RTFOR OUTPUT Ormsby pass band

INPUT RTFOR AGC OUTPUT Ormsby pass band

INPUT RTFOR AGC OUTPUT Ormsby pass band

Ormsby pass band

Radial Transform inverse (RTRev)

RTRev

RTRev

RTRev

RTRev

shows the flows used in second step.

(a) raw data, 2d land data. (b)After Applied Radial Transform Forward+ RTRev. (c)After Applied Radial Transform Forward + RTRev+ AGC. (d) After Applied Radial Trans-form Forward+ Ormsby pass band filter+ RTRev, (e) After Applied Radial Transform Forward+ Ormsby pass band filter+ RTRev+ AGC.(f) After Applied Ormsby pass band filter

+Radial Transform Forward+ Ormsby pass + RTRev +band filter+ AGC.

(a) (b) (c) (d)

(e) (f)

A Review of Forward &inverse Radial Trace Transform

2D transform

Special analysis


Signal enhancement

Deconvolution

Filtering



F-K analysis






Crosscorrelation

Multi Coherence

SVD





Effect lags

Land 2D data (PSTM)

FX-FK application t


Tau-p transform

Forward

inverse

in F-K domain

in F-X domain

Forward

inverse

in F-K domain

in F-X domain

2DFX-Deconvoltution

3D FKx-Ky Linear

Logifer Design




Ormsby Band Pass

Butter worth Filter

Notch filter

In frequency domain

In time domain

THOR 2D/3D theshod




FX


Predication

projection

Signal enhancement Signal Enhancement using 2D F-X Prediction Design Levinson–Durbin Filter mode

FX FILTER MODE FIKTER

LENGTH/TRACES

DESIGN TRACE WINDWO /TRACES

DESING

TIME WINDOW/MS

End Frequency/

Hz

Taper

End Frequency/Hz

Levinson–Durbin

3 100 200 100 10

6 200 400 200 101 50 100 50 10

Show the Parameters used for FX Filter Levinson–Durbin.

Input data 2d crocked line, Shot point N 16 with 300 traces,

Amplitude Spectral for Input data 2d crocked line.

Applying Levinson–Durbin, 1, FIKTER LENGTH/TRACES 3

(a) Applying Levinson–Durbin, , FIKTER LENGTH/TRACES “3”, (b) Amplitude Spectral display in dB for Input data 2d crocked line after Applying

(b) Levinson–Durbin, FIKTER LENGTH/TRACES 3, (c) Applying Levinson–Durbin, 1, FIKTER LENGTH/TRACES “6”(d) Amplitude Spectral for Fig(c),(e)

(c) Applying Levinson–Durbin, 1, FIKTER LENGTH/TRACES “1 “ (f) Amplitude Spectral for Fig (e ).

(a)

(b)

(c)

(d)

(e)

(f)

(b) (a) (c)

(a)Applying Levinson–Durbin, 1, FIKTER LENGTH/TRACES 3 for second Time, (b)Difference between raw data and after applying Levinson–Durbin, 1, FIKTER LENGTH/TRACES 3, second Time applied,(c) Amplitude Spectral display in dB, and after applying Levinson–Durbin, FIKTER LENGTH/TRACES 3, second Time applied.

Now to see the effect on amplitude, we will compare the raw data with data after applied the Levinson–Durbin filter on analysis windows. As we can see in amplitude –frequency window from 20H to 80 H ON frequency, the single becomes more regular and at frequencies 20Hz TO 40 Hz, it appeared to increase amplitude.

Frequency analysis for 2d raw data. Frequency analysis after applying, Levinson–Durbin, 1, FIKTER LENGTH/TRACES 3.

Frequency analysis after applying, Levinson–Durbin, 1, FIKTER LENGTH/TRACES 3.

Signal enhancement

Comparison between Levinson–Durbin Filter mode and other kind of Filter modeThe input data are normally CMP stacked traces, but they can also be prestack data sorted by some key (e.g. Shot, Receiver or CMP sort indexes). In these cases, FX-decon will automatically stop at the end of each "group" (be it shot, Receiver or CMP gather). In other words, no "mixing" will occur between adjacent shots, receivers or CMP's.

Comparison between Levinson–Durbin Filter mode and other kind of Filter mode

Signal enhancement

SHOT POINT N 26 with 300 traces, Levinson–Durbin, 1, Filter LENGTH/ TRACES 3.

shot point N26 with 300 traces,

Levinson–Durbin,2 Filter LENGTH/ TRACES 10.

shot point N 26with 300 traces, Burg Recursive 1, Filter LENGTH/ TRACES 3

shot point N 26 with 300 traces, Burg Recursive 2,

Filter LENGTH/ TRACES 10.

shot point N 26 with 300 traces, Cadzow(

Lanzos Decom)1, Filter LENGTH/ TRACES 3.

shot point N 26 with 300 traces, Cadzow

(Lanzos Decom) 2, Filter LENGTH/ TRACES10.

shot point N26with 300 traces,

FX projection1, Filter LENGTH/ TRACES 3.

shot point N 26 with 300 traces,

FX projection2, Filter LENGTH/ TRACES10.

shot point N 26 with 300 traces, FX-Decon Deblur 1,


shot point N 26 with 300 traces, FX-Decon Deblur 2,


Signal enhancement

Input raw data small window for comparing. after applied Logifer with Input Velocity (v-Noise) 3000 m/s.

Different After applied Logifer with Input Velocity (v-Noise)

3000 m/s from original input.

after applied Logifer to raw input data,

with Input Velocity (v-Noise) 1000m/s.

Different After applied Logifer with Input Velocity (v-Noise)

1000 m/s from original input.

Signal enhancement )Logifer ( Logical Filter Noise Reduction

Special analysis


Signal enhancement

Deconvolution

Filtering



F-K analysis






Crosscorrelation

Multi Coherence

SVD





Effect lags

Land 2D data (PSTM)

FX-FK application t


Tau-p transform

Forward

inverse

in F-K domain

in F-X domain

Forward

inverse

in F-K domain

in F-X domain

2DFX-Deconvoltution

3D FKx-Ky Linear

Logifer Design




Ormsby Band Pass

Butter worth Filter

Notch filter

In frequency domain

In time domain

THOR 2D/3D theshod




FX


Predication

projection

FILTERING Ormsby Band Pass filter

Ormsby Band Pass works by computing the forward Fast Fourier Transform of each trace. The frequency samples are then multiplied by a function which is specified in the Ormsby Band Pass Filter dialog. After this multiplication (which will set some low and high-frequency samples to zero), the result is passed through an Inverse Fast Fourier transforms to arrive at a Time-domain result.

Filter Domain ： can be in Time-domain or frequency-domain, and this option filters seismic data a trace at a time with a trapezoidal filter. If you select the frequency-domain option, then the input is padded out with zeros. It is first increased by the percent zero padding for FFT and then increased to the next highest power of two samples. The data is transformed, and the values are multiplied by the trapezoid in frequency. And if we wish to limit our filter length, then we have to select the Time-domain option. In this case, the filter operator is computed and applied as a time series.

Apply Ormsby Band Pass On 2d Land Data, 3d Land Data And 2d Marin Data

Low Truncation Freq Low Cut Freq High-Cut Freq High Truncation Frequency

10 HZ 15 HZ 55 HZ 60 HZ

Parameters of Ormsby Band Pass.

And the work can be illustrated by following, the input data 2d, 3d, and applying Ormsby Band Pass Filter in frequency and then in Time-domain, then compare the results:

shows the work and flow.

(a) (b) (c) (d)(a) raw 2d land data,120 traces,Shot point N20.(b) 2d land data after applied Ormsby Band Pass Filter in frequncy domain.(c) 2d land data after applied Ormsby Band Pass Filter in Time domian.(d)

difference between b and c.

(a) (b) (c) (d)(a) raw 3d land data,256 traces N337.(b) 3d land data after applied Ormsby Band Pass Filter in frequncy domain.(c) 3d land data after applied Ormsby Band Pass Filter in Time domian.(d)

difference between b and c.

(a) (b) (c)(a) raw 2d Marin data,256 traces N337.(b) 2d Marin data after applied Ormsby Band Pass Filter in frequency domain.(c) 2d Marin data after applied Ormsby Band Pass Filter in Time domain.

Filtering Ormsby Band Pass filter

Applied notch Filter in frequency-domain N 16. DIFFRENCT between raw data and after applied

notch Filter in frequency-domain.

Applied notch Filter in Time-domain. Different between raw data and after applied notch

Filter in Time-domain.

(a )

(b)

zoom on to see the difference,

(a) before and (b) after applied Filter.

Filtering Notch filter

BWorth [Butter Worth Filter]

BWorth works by computing a "poles only" filter from the input parameters. The poles are computed to most closely match the frequency and amplitude characteristics desired. Note that it is impossible to drive the amplitude at any particular frequency to zero. The filter is then applied as a Time-domain recursive filter. The phase is either set to zero or to minimum-phase otherwise.

Raw data. after applied Butter worth Filter. Difference between raw data and after applied Butter worth Filter.

Applied Butter worth Filter without applying Zero-Phase Filter.

Filtering

Special analysis


Signal enhancement

Deconvolution

Filtering



F-K analysis






Crosscorrelation

Multi Coherence

SVD





Effect lags

Land 2D data (PSTM)

FX-FK application t


Tau-p transform

Forward

inverse

in F-K domain

in F-X domain

Forward

inverse

in F-K domain

in F-X domain

2DFX-Deconvoltution

3D FKx-Ky Linear

Logifer Design




Ormsby Band Pass

Butter worth Filter

Notch filter

In frequency domain

In time domain

THOR 2D/3D theshod




FX


Predication

projection

General applications THOR 2D/3D THESHOD NOISE Attenuation / Replacement.

A CDP gather with noise bursts. THOR vs. Time-domain Median.

To show what THOR process produces, in fact threshold was set near zero, and all the data in the gather have been replaced). Operation character sequence signal in space, and thus is friendly AVO with the appropriate Median length. Module automatically sorts data in the offset and if there is a need for medians longer have the capability to super gather CDPs.

Replace all the samples in a gather with THOR.

stack with noise bursts, (a) stack input data.

(b) Zoom stack. (c) Gather CDP.

General applications FX Prediction &projection for Attenuation of Random Noise in Stacked Data

The FX Prediction works by first calculating the Fourier Transform of every trace. The complex frequency samples are then multiplexed so that one gets a series of Mono-frequency values across space (F-X transform). Next calculates a two-sided complex Wiener prediction filter for each Mono-frequency series. This filter is then applied and the inverse F-X transform calculated. The effect is to "smooth" the data across space (X).the Fig shows the input data (stacked)The FX Filter mode can be prediction or projection, the FX parameters are shown in table below .the filter bass band were applied before and after FX applied.The FX-deconvolution noise Attenuation output is shown and the data Time scale has been expanded for comparison. We can see the improvement in removal of random noise in the

Filter length Design window Cut of frequency 3 trace 100 trace 100 Hz

FX parameters for FX Prediction and FX projection.

Input data Trim Static unfiltered. stacked section after applied FX-Deconvolution (prediction). stacked section after applied FX-Deconvolution (projection).

the power Spectrum of original data (a) and processed by FX-deconvolution (prediction) (b), FX-deconvolution (projection) (c).

(a) (b)(c)

General applications 4D-DEC (Principal component in 4D - Time/Dip/CDP/Offset) for Attenuation of Random Noise in Stacked Data

This 4D-DEC (2d Principal Component Decomposition in 4D) removes white noise by creating a wavelet (reflectivity and signature) within each of small overlapping windows in time and space. It picks a single dip within each window. It then constructs the wavelet by first stacking along the chosen dip and then re-stacking with Crosscorrelation statics so that we produce a high resolution wavelet. It then matches the wavelet in amplitude and time to each trace in turn. Thus the fine details of the signal amplitude and static are preserved while the trace wavelet within a window is constant. Since the process is overlapping the windows the trace wavelet actually varies slowly. The module subtracts this model from the input and outputs the difference so that in this process one can have several dips at the same time by running the module repeatedly (usually three times). This process subtracts the final output from the input and this is our final section. The process also has options to smooth the amplitude match and also the statics match.The amplitude smoothing generally looks reasonable but the statics smoothing may smear the output and make it look wormy. This module is a very strong competitor for FXPred. It avoids having to predict dips at different frequencies separately and because of the matching does not tend to smear data. It simply takes out the signal component that already exists on the data. This allows it to operate effectively on migrated data in which we may find discontinuities on the signal both in time and space. Random noise attenuation using 4D-DEC. This flow extracts random noise from the data, the application in cascade will soften the filtering process. The input is the Trim Static Unfiltered Stack .

stacked section after applied 4D-DEC.The original data. Difference between input data and after applied 4D-DEC.

(a) (b)

Amplitude Spectrum (a) original data, and after applied 4D-DEC (b).

mk:@MSITStore:F:/chapter/vistawindows.chm::/topic303.htm

General applications FK (Stolt) 2-D Post Stack Time Migration.

Post stack Migration using FDMig2D

In this section an example of Post stack Migration using the FDMig2D flow tool (2D Finite Difference Migration) Processing is presented. The FDMig2D works by using the so-called 45

degree approximation to the wave equation.The FKMig2D works by calculating the 2-D Fourier Transform of the input stack traces after undergoing the Stolt Pseudo-depth conversion. FK points are then mapped to lower

frequencies along lines of constant K according to the published equations .The mapped FK transform is then transformed back to time and the depth conversion is reversed to give the

final 2-D time migrated section. Trace distance Tau step Percent of RMS velocity used 10 m 10 ms 100

parameters for D Finite Difference Migration.

The original data. Migration from irregular surface Velocity referenced

with floating datum.

Migration from irregular surface Velocity

referenced with fixed datum.

Migration from irregular surface Velocity referenced with true surface.

General applications Parameters Effects on spiking deconvolution of land Seismic data.

PSTM section.Pstm Data processed by spiking deconvolution with operator

length 5 ms when percent prewhitening length 0%.

Pstm data processed by spiking deconvolution

with operator length 10 ms when per-cent prewhitening length 0%.

Pstm data processed by spiking deconvolution

with operator length 15 ms when per-cent prewhitening length 0%.

(a). (b)

(c) (d) (a) Original data; (b) operator length 5 ms; (c) operator length 10 ms;

( d) operator length 15 ms.power Spectrum of original data and processed by

spiking deconvolution with operator lengths 5 ms, 10 ms and

15 ms when percent prewhitening 0%.

Pstm data processed by spiking deconvolution with

operator length 40 ms when percent prewhitening length 1%.

Pstm data processed by spiking deconvolution with

operator length 60 ms when percent prewhitening length 1%.

(a) (b)

(a) Operator length 40ms; (b) operator length 60 ms

power Spectrum processed by spiking deconvolution

Conclusions

We tested the effect of trace spacing /FT (0,0.5,25and 50)m for 2d land data, when the value of trace spacing /FT was smaller the F max was bigger, we made comparison of input and output to see the difference made on output after applying Pie Rejection.

The 128 ms long operator gives better result, we see that increasing operator length does not indefinitely improve the results, and very short operators produce the same type of noise spikes

When the source signature is known a designature process can be applied as an alternative or a complement to this step. In our cause we had a different approach. First we applied a trace by trace predictive deconvolution to eliminate some multiple reverberation, and the deconvolution which In this case the desired output was a lagged version of the input. The standard equations are solved for the predictive operator.

from work above when applying Spike deconvolution on Marin 2d data the small value of operator length gives better results.

Minimum apparent slowness must be smaller than real minimum slowness of linear noise because we limit operator length when we apply. Max slowness can be just huge number, if we want to eliminate all low velocity linear noise. The shorter is operator length, the milder application of operator will be. To eliminate all linear noise this procedure can be applied in iterative manner.

The fk-fx linear noise removal was reviewed and tested on 2d land and 3d land seismic data.It was found that On noisy synthetic images, numerical measurements indicate the FK-FX Filter performs better at attenuation random noise than the F-K Filter only. The residual noise after f-x filtering still appears fairly random, and the filter does not give rise to the same type of coherent In addition.

The linear noise is filtered by taking the forward Tau-Pi transform in cascade noise is obtained by subtracting Tau-Pi filtered data from the initial data and the Adaptive subtraction is used to accurately remove noise without damaging the signal. Each trace of the forward Tau-p transform corresponds to a slope or "p" value, these "p" values are linearly interpolated between the Minimum Tau- P Slope (our value in this work 0 S/M) and the Maximum Tau-P Slope (our value in this work 0.005 S/M). The distance between traces of the input data was 10 M, and the lowest frequency we wishes to keep was 3HZ,All lower frequencies set to zero..

After applied Tau-p transform the Amplitude Spectrum for input data in Fig (3,b,c,d) increased and so the average trace also and from results we see that Tau-p transform give s very good results and comparison of Tau-p in F-K domain .

The default parameters in RTFOR and RTRev have been chosen to give reasonable results for arbitrary input, but close attention to the parameter can lead to better performance and more appropriate parameter choices. We have demonstrated a technique for wavefield separation and coherent noise attenuation based on the Radial Trace Transform. Further, we have shown that it can be effective in attenuating noise while preserving lateral detail in panels of traces to which it is applied. Because it is a simple mapping, the Radial Trace Transform is easy to compute and invertible, if precautions are taken to avoid aliasing

Transform design parameters are easily related intuitively to coherent events on panels of seismic traces; and in some filter applications, it can be more effective than the more traditional F-K transform. Unlike F-K filters, R-T domain filters can be applied repeatedly, so that several passes can be applied to a single gather or panel, each pass directed at a particular type or mode of coherent noise (Henley, 1999, cas1). Also unlike F-K filtering, R-T domain operations do not require that the original data panel be uniformly gridded in either X or T, since any point in the R-T domain can be appropriately determined by interpolation from existing points in the X-T domain. Also, the entire X-T domain does not need to be mapped into the R-T domain. Any subset can be completely transformed from the X-T plane to the R-T domain and back. The latter two properties of the R-T transform can provide a real advantage over an integral transform like the F-K for some applications.

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Conclusions

Our way is to improve the signal through using 2D F-X Prediction Design Levinson–Durbin filter mode and also compare the effect of the filter to the raw data on our signal. We took raw data then applied throw flow unite and changing our parameters to see the difference and we find also As a general rule, the effects of F–X prediction are harsher on smaller windows – when was fewer traces and short time intervals. The big disadvantage of F–X is of course the inability to handle conflicting dips such as "curving" structure, so split the data into sections each containing only consistent dips prior to inputting to F–X prediction. The number of traces to use in design and application of the filter. In fact a 2–sided filter is used so that a value of three here would use 3 traces on each side of the trace being computed. Default = 3. As a rule of thumb make this number equal to the number of distinct sets of dipping events in the design window. It will usually be in the range of 3 to 5.

Number of traces in the design window this usually less than the total number of traces in the data set. And filters should be redesigned every 50 to 100 traces.

The End Frequency value acts as a high-cut filter, And It can also shorten the run time by only computing up to this value. As a result, in the same computation time, the algorithm in this paper can enhance the single and the effect can be see when applying frequency analysis window. Filter gave good result when we applied same filter for second time but then started effect to single frequencies and amplitude

Thank you! 谢谢！

final study of typical processing methods and their parameters in advance seismic data...

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