Methods for simultaneous source separation

Patent metadata

Field	Value
Publication number	US-10605941-B2
Application number	US-201514974060-A
Country	US
Kind code	B2
Filing date	Dec 18, 2015
Priority date	Dec 18, 2014
Publication date	Mar 31, 2020
Grant date	Mar 31, 2020

Abstract

A multi-stage inversion method for deblending seismic data includes: a) acquiring blended seismic data from a plurality of seismic sources; b) constructing an optimization model that includes the acquired blended seismic data and unblended seismic data; c) performing sparse inversion, via a computer processor, on the optimization model; d) estimating high-amplitude coherent energy from result of the performing sparse inversion in c); e) re-blending the estimated high-amplitude coherent energy; and f) computing blended data with an attenuated direct arrival energy.

First claim

The invention claimed is:

1. A multi-stage inversion method for deblending seismic data, the method comprising:
a) acquiring, via a plurality of shots fired from at least one vessel, blended seismic data from a plurality of seismic sources using a compressive sensing sampling scheme, the at least one vessel driven at a constant speed while permitting natural causes to affect the constant speed;
b) constructing an optimization model that relates the blended seismic data to unblended seismic data;
c) performing sparse inversion, via a computer processor, on the optimization model to yield a result;
d) estimating high-amplitude coherent energy from the result;
e) re-blending the high-amplitude coherent energy; and
f) computing a deblended seismic data by attenuating at least a portion of the high-amplitude coherent energy from the blended seismic data.

2. The method of claim 1 , wherein steps c) to f) are iteratively repeated until a desired deblended data is computed.

3. The method of claim 1 , wherein the sparse inversion is by nonmonotone alternating direction method.

4. The method of claim 1 , wherein the optimization model is given by b=Mu, wherein b is the blended seismic data, u is the unblended seismic data, and M is a blending operator.

5. The method of claim 1 , wherein performing the sparse inversion provides at least an approximation of the unblended seismic data u.

6. The method of claim 1 , wherein the high-amplitude coherent energy is subtracted from the blended seismic data after performing the re-blending of the high-amplitude coherent energy.

7. The method of claim 1 , where the high-amplitude coherent energy is selected from, the group consisting of: direct arrival energy, ground roll, mud roll, multiples, near-surface scattering, topographic scattering, noise generated by permafrost, platform, surveys nearby, and any combination thereof.

8. The method of claim 1 , wherein the natural causes cause the at least one vessel to have a variable speed.

9. The method of claim 8 , wherein the blended seismic data is acquired via a plurality of vessels.

10. The method of claim 8 , wherein each of the plurality of vessels covers half of a survey area.

11. A multi-stage inversion method for deblending seismic data, the method comprising:
a) acquiring, via a plurality of shots fired from at least one vessel, blended seismic data from a plurality of seismic sources using a compressive sensing sampling scheme, the at least one vessel driven at a constant speed while permitting natural causes to affect the constant speed;
b) constructing an optimization model that relates the blended seismic data to unblended seismic data;
c) performing sparse inversion, via a computer processor, on the optimization model;
d) estimating a high-amplitude noise from the optimization model, the high-amplitude noise selected from the group consisting of: direct arrival energy, ground roll, and mud roll;
e) re-blending the high-amplitude noise;
f) computing deblended data by attenuating at least a portion of the high-amplitude noise from the blended seismic data; and
g) iteratively repeating steps c) to f) until a desired deblended data is computed.

12. The method of claim 11 , wherein the sparse inversion is by nonmonotone alternating direction method.

13. The method of claim 11 , wherein the optimization model is given by b=Mu, wherein b is the blended seismic data, u is the unblended seismic data, and M is a blending operator.

14. The method of claim 11 , wherein performing the sparse inversion provides at least an approximation of the unblended seismic data u.

15. The method of claim 11 , wherein the high-amplitude noise is subtracted from the blended seismic data after performing the re-blending of the high-amplitude noise.

16. A method for jointly deblending and reconstructing seismic data, the method comprising:
acquiring, via a plurality of shots fired from at least one vessel, blended seismic data from a plurality of seismic sources using a compressive sensing sampling scheme, the at least one vessel driven at a constant speed while permitting natural causes to affect the constant speed;
constructing an optimization model that relates the blended seismic data, unblended seismic data, and a restriction operator that maps data from a grid of reconstructed seismic sources to a grid of observed seismic sources;
performing sparse inversion, via a computer processor, on the optimization model, a high-amplitude energy being estimated from the optimization model, the high-amplitude energy being re-blended; and
computing a deblended data by attenuating at least a portion of the high-amplitude energy.

17. The method of claim 16 , wherein the blended seismic data is acquired by a non-uniform shooting pattern.

18. The method of claim 16 , wherein the sparse inversion is by nonmonotone alternating direction method.

19. The method of claim 16 , wherein the optimization model is given by b=MRu, wherein b is the blended seismic data, u is the unblended seismic data, M is a blending operator, and R is the restriction operator.

20. The method of claim 16 , wherein the performing of the sparse inversion provides at least an approximation of the unblended seismic data u.

Assignees

• Conocophillips Co

Inventors

• Li Chengbo
• Mosher Chuck
• Ji Leo
• Brewer Joel

Classifications

• G01V2210/57

  Trace interpolation or extrapolation, e.g. for virtual receiver; Anti-aliasing for missing receivers · CPC title

• G01V2210/127

  Cooperating multiple sources · CPC title

• G01V1/368 Primary

  Inverse filtering · CPC title

• G01V1/282

  Application of seismic models, synthetic seismograms · CPC title

• G01V1/364 Primary

  Seismic filtering (G01V1/37 takes precedence) · CPC title