Angle dependent surface multiple attenuation for two-component marine bottom sensor data

• US 6,654,693 B2
• Filed: 10/22/2001
• Issued: 11/25/2003
• Est. Priority Date: 02/27/2001
• Status: Expired due to Term

• Current Assignee: PGS Americas, Inc. (PGS ASA)
• Original Assignee: PGS Americas, Inc. (PGS ASA)
• Inventors: Liu, Faqi, Stoffa, Paul L., Sen, Mrinal K.
• Primary Examiner(s): Hoff, Marc S.
• Assistant Examiner(s): Gutierrez, Anthony
• Application Number:
  US10/045,466
• Publication Number:
  US 20020156583A1
• Time in Patent Office: 764 Days
• Field of Search: 702/7, 702/14, 702/17
• US Class Current: 702/17
• CPC Class Codes: G01V 1/362 Effecting static or dynamic corrections; Stacking
• CPC Class Codes: G01V 1/364 Seismic filtering G01V1/37 takes precedence
• CPC Class Codes: G01V 1/38 specially adapted for water-covered areas G01V1/28 takes precedence
• CPC Class Codes: G01V 1/3808 Seismic data acquisition, e.g. survey design
• CPC Class Codes: G01V 2210/27 Other pre-filtering
• CPC Class Codes: G01V 2210/56 De-ghosting; Reverberation compensation

Abstract

A method of processing data that uses an angle dependent filter from two-component sensor data allows for attenuation of free surface multiples. Typically, the sensors that are used to produce two-component ocean bottom sensor data are hydrophones and geophones. The method decomposes the recorded dual sensor data into upgoing and downgoing wavefields by combining the recorded pressure at the hydrophone with the vertical particle velocity from the geophone recorded at the ocean floor. Surface multiple attenuation is accomplished by application of an incident angle dependent deconvolution of the downgoing wavefield from the upgoing wavefield. The method uses an angle dependent filter to calibrate the geophone response so that the different coupling of the two instruments and associated noise are taken into account.

In a further embodiment, a method of attenuation of multiple reflections in seismic data is provided. The seismic data comprises pressure data and particle velocity data. The method comprises deconvolving the seismic data, and applying a moving average operator to the seismic data.

Claims

1. A method of attenuation of multiple reflections in seismic data, wherein the seismic data comprises pressure data and particle velocity data, the method comprising:

deconvolving the seismic data in the plane wave domain; and

applying a time signal moving average operator to the seismic data, using essentially the following formulae;

$\hat{P}<br><br>\left({\mathrm{\tau <br><br>}}_i,p\right)=\frac{1}{2<br><br>k}<br><br>\underset{j=i-k}{\overset{i+k}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br>P<br><br>\left({\mathrm{\tau <br><br>}}_j,p\right)$${\hat{V}}_z<br><br>\left({\mathrm{\tau <br><br>}}_i,p\right)=\frac{1}{2<br><br>k}<br><br>\underset{j=i-k}{\overset{i+k}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br>V_z<br><br>\left({\mathrm{\tau <br><br>}}_j,p\right).$

2. The method of claim 1, wherein said deconvolving the seismic data further comprises applying a predictive deconvolution to the seismic data.

3. The method of claim 2, wherein said applying a predictive deconvolution to the seismic data further comprises applying a predictive deconvolution to the pressure data and the particle velocity data independently.

4. The method of claim 1, further comprising bandpass filtering the seismic data.

5. The method of claim 4, further comprising bandpass filtering the pressure data and the particle velocity data independently.

6. The method of claim 1, comprising amplitude calibrating the seismic data.

7. The method of claim 6, wherein said amplitude calibrating further comprises defining a mean signal level.

8. The method of claim 1, wherein said applying a moving average operator to the seismic data further comprises inter-seismogram calibration.

9. The method of claim 8, wherein said inter-seismogram calibration further comprises comparing an average of the particle velocity data and the pressure data.

10. The method of claim 1, further comprising normalizing the particle velocity and pressure data.

11. A system for attenuation of multiple reflections in seismic data, wherein the seismic data comprises pressure data and particle velocity data, the system comprising:

means for deconvolving the seismic data in the plane wave domain; and

means for applying a time signal moving average operator to the seismic data,

using essentially the following formulae;

$\hat{P}<br><br>\left({\mathrm{\tau <br><br>}}_i,p\right)=\frac{1}{2<br><br>k}<br><br>\underset{j=i-k}{\overset{i+k}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br>P<br><br>\left({\mathrm{\tau <br><br>}}_j,p\right)$${\hat{V}}_z<br><br>\left({\mathrm{\tau <br><br>}}_i,p\right)=\frac{1}{2<br><br>k}<br><br>\underset{j=i-k}{\overset{i+k}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br>V_z<br><br>\left({\mathrm{\tau <br><br>}}_j,p\right).$

12. The system of claim 11, where said means for deconvolving the seismic data further comprises means for applying a predictive deconvolution to the seismic data.

13. The system of claim 11, further comprising means for bandpass filtering the seismic data.

14. The system of claim 11, comprising means for amplitude calibrating the seismic data.

15. The system of claim 14, wherein said means for amplitude calibrating further comprises means for defining a mean signal level.

16. The system of claim 11, wherein said means for applying a moving average operator to the seismic data further comprises means for inter-seismogram calibration.

17. The system of claim 16, wherein said means for inter-seismogram calibration further comprises means for comparing an average of the particle velocity data and the pressure data.

18. The system of claim 11, further comprising means for normalizing the particle velocity and pressure data.

19. Seismic data with two components processed by a method comprising:

deconvolving the seismic data in the plane wave domain; and

applying a time signal moving average operator to the seismic data, using essentially the following formulae for the two components;

$\hat{P}<br><br>\left({\mathrm{\tau <br><br>}}_i,p\right)=\frac{1}{2<br><br>k}<br><br>\underset{j=i-k}{\overset{i+k}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br>P<br><br>\left({\mathrm{\tau <br><br>}}_j,p\right)$${\hat{V}}_z<br><br>\left({\mathrm{\tau <br><br>}}_i,p\right)=\frac{1}{2<br><br>k}<br><br>\underset{j=i-k}{\overset{i+k}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br>V_z<br><br>\left({\mathrm{\tau <br><br>}}_j,p\right).$

where the time signal moving operator length k is varied to achieve substantially optimum calibration.