Seismic Data Processing

• US 20120275267A1
• Filed: 03/26/2012
• Published: 11/01/2012
• Est. Priority Date: 04/26/2011
• Status: Abandoned Application

• Current Assignee: Anatoly Baumstein, Jerome R. Krebs, Partha S. Routh, Ramesh Neelamani, Thomas A. Dickens
• Original Assignee: Anatoly Baumstein, Jerome R. Krebs, Partha S. Routh, Ramesh Neelamani, Thomas A. Dickens
• Inventors: Neelamani, Ramesh, Routh, Partha S., Krebs, Jerome R., Baumstein, Anatoly, Dickens, Thomas A.
• Application Number:
  US13/430,380
• Publication Number:
  US 20120275267A1
• Time in Patent Office: Days
• US Class Current: 367/38
• CPC Class Codes: G01V 1/28 Processing seismic data, e.g. for interpretation or for event detection G01V1/48 takes precedence
• CPC Class Codes: G01V 2210/51 Migration

Abstract

Provided is a method for processing seismic data. One exemplary embodiment includes the steps of obtaining a plurality of initial subsurface images; decomposing each of the initial subsurface images into components; identifying a set of components comprising one of (i) components having at least one substantially similar characteristic across the plurality of initial subsurface images, and (ii) components having substantially dissimilar characteristics across the plurality of initial subsurface images; and generating an enhanced subsurface image using the identified set of components. Each of the initial subsurface images is generated using a unique random set of encoding functions.

Claims

1. A method for processing seismic data, the method comprising:

a. obtaining a plurality of initial subsurface images, wherein each of the initial subsurface images is generated using a unique random set of encoding functions;

b. decomposing each of the initial subsurface images into components;

c. identifying a set of components comprising one of (i) components having at least one substantially similar characteristic across the plurality of initial subsurface images, and (ii) components having substantially dissimilar characteristics across the plurality of initial subsurface images; and

d. generating an enhanced subsurface image using the set of components identified in step c.

2. The method of claim 1 wherein the initial subsurface images and the enhanced subsurface image are SS-RTM images.

3. The method of claim 1 further comprising the step of attenuating components not identified as having at least one substantially similar characteristic across the plurality of initial subsurface images.

4. The method of claim 1 wherein decomposing each initial subsurface image includes transforming each initial subsurface image into another domain.

5. The method of claim 4 wherein the domain is frequency.

6. The method of claim 4 wherein a curvelet transform is used to transform each initial subsurface image into another domain.

7. The method of claim 4 wherein at least one of a fourier transform, wavelet transform, F-K transform, curvelet transform, and radon transform is used to transform a subsurface image into another domain.

8. The method of claim 1 wherein the initial subsurface images are generated by:

a. obtaining a set of shot gathers comprising forward and backward wave component data;

b. selecting first and second random encoding functions;

c. encoding the forward wave component data for each source in the set of shot gathers using the first random encoding function to form an Encoded Source Super-Shot Wave Component;

d. encoding the backward wave component data for each receiver in the set of shot gathers using the second random encoding function to form an Encoded Receiver Super-Shot Wave Component;

e. forward propagating the Encoded Source Super-Shot Wave Component to generate a Forward Propagated Wave Component;

f. back propagating the Encoded Receiver Super-Shot Wave Component to generate a Back Propagated Wave Component;

g. applying an imaging condition to the Forward and Back Propagated Wave Components to generate a subsurface image; and

h. iteratively repeating steps b-g until a predetermined condition is satisfied, wherein the first and second random encoding functions are selected so that the functions are unique for each iteration.

9. The method of claim 8 wherein the encoding the forward and backward wave components is performed using scalars in the time domain.

10. The method of claim 8 wherein the encoding the forward and backward wave components is performed using scalars in the frequency domain.

11. The method of claim 8 wherein the first and second random encoding functions are reciprocal.

12. The method of claim 11 wherein at least one of the first and second reciprocal random encoding functions is a unit-magnitude encoding function.

13. The method of claim 12 wherein one or more of the first and second reciprocal random encoding function include a unit-magnitude complex number encoding function.

14. The method of claim 8 wherein the first and second random encoding functions include reciprocal random encoding functions on plane waves with different angles of incidence.

15. The method of claim 8 wherein the first random encoding function is equivalent to the second random encoding function.

16. A method for processing seismic data, the method comprising:

a. obtaining a plurality of SS-RTM subsurface images, wherein each of the SS-RTM subsurface images is generated using a unique random unit-magnitude set of encoding functions;

b. decomposing each of the plurality of SS-RTM subsurface images into curvelet coefficients;

c. averaging the curvelet coefficients to generate a preliminary signal curvelet coefficient estimate;

d. computing a variance of a subset of the curvelet coefficients to determine a noise level in the preliminary signal curvelet coefficient estimate;

e. attenuating noise in the curvelet coefficients using the determined noise level and the preliminary signal curvelet coefficient estimate to generate attenuated curvelet coefficients; and

f. performing an inverse curvelet transform on the attenuated curvelet coefficients to generate an enhanced SS-RTM image.

17. The method of claim 16 wherein step e is performed using one or more of hard-thresholding, soft-thresholding, empirical Bayes thresholding, and Wiener filtering.

18. A method for computing a gradient of a cost function associated with seismic data, the method comprising:

a. generating a plurality of gradients of objective functions computed using unique sets of random reciprocal encoding functions;

b. decomposing each of the gradients into components;

c. identifying a set of components having at least one substantially similar characteristic across the plurality of gradients; and

d. generating an enhanced gradient using the set of components identified in step c.