Lab 4: Seismic Deconvolution

1. INTRODUCTION 

Deconvolution is a filtering process which removes a wavelet from the recorded seismic trace by reversing the process of convolution. The commonest way to perform deconvolution is to design a Wiener filter to transform one wavelet into another wavelet in a least-squares sense. By far the most important application is predictive deconvolution in which a repeating signal (e.g. primaries and multiples) is shaped to one which doesn't repeat (primaries only). Predictive deconvolution suppresses multiple reflections and optionally alters the spectrum of the input data to increase resolution. It is almost always applied at least once to marine seismic data.

OBJECTIVES:

• ·       To increase the vertical resolution of the data by compressing the source wavelet
• ·       To extract the useful information from the header using MATLAB function
• ·       For noise attenuation of the seismic data
• PROCEDURE:

clear,clc,close all

load SeismicDataA_gain

%%

shot_num=4:6;

p=1;

[Dshot,dt,dx,t,offset]=extracting_shots(Dbpf,H,shot_num,p);

[nt,nx]=size(Dshot);

%%

scale=5;

mwigb(Dshot,scale,offset,t)

xlabel('Trace number','FontSize',14)

ylabel('Time(s)','FontSize',14)

%%

max_lag=0.2;

[Dauto,lags]=auto_correlation_map(Dshot,max_lag,dt);

scale=5;

mwigb(Dauto,scale,offset,lags)

xlabel('Trace number','FontSize',14)

ylabel('Time lag(s)','FontSize',14)

title('Auto_correlograms of shot gathers 4-6','FontSize',14)

%%

mu=0.1;

Ds=spiking_decon(Dshot,max_lag,mu,dt);

scale=5;

mwigb(Dt,scale,offset,t)

xlabel('Trace number','FontSize',14)

ylabel('Time(s)','FontSize',14)

After applied frequency filtering via BPFs, the seismic data were smooth hence its vertical resolution affected due to the loss of some of original wider frequency band. The main objective  for seismic deconvolution is to increase the vertical resolution of the data by compressing the source wavelet to a zero phase if possible. 

This concept will be illustrated by using the spiking deconvolution as an example. The aim of spiking deconvolution is to compress the source wavelet w(t) into a zero-phase spike of zero width, i.e., δ(t). This means that we are eliminating the effect of the source wavelet and leave only the effect of the Earth’s reflectivity in the seismogram.By using M-function (provide in the manual) such as spiking_decon.m and auto_correlation_map.m to perform spiking deconvolution filtering.

both 

figure show that the shot gathers 4, 5 and 6 after apply the spiking deconvolution and instantaneous AGC with window length of 0.5 s to the deconvolved data which to compensate for the lost amplitude after deconvolution process.

Figure above showed the auto-correlograms for shot gather 4, 5 and 6. This auto-correlation window is important to choose the deconvolution parameters. The autocorrelation window should include the part of the record that contains useful reflection signal and should exclude coherent (e.g., ground roll) or incoherent noise (e.g., later parts of the record).

Figure above show the PSD of the average trace of shot gathers 4, 5 and 6 before and after using spiking deconvolution. From this figure we can conclude that the spiking deconvolution increase the frequency after apply the BPF (lab 3) which increase the vertical resolution.

DISCUSSION:

Deconvolution is done to increase the vertical resolution of the data. The deconvolution method specifically spiking deconvolution is conducted by compressing the source wavelet. Based on result  , it can be seen that the noise is reduced and the seismic data is much smoother. Thus, it is proved that spiking deconvolution does improve the seismic section.

CONCLUSION:

·       All objectives are achieved.

·       Spiking deconvolution is functioning in enhance the vertical resolution.