Full Wavefield Inversion Using Time Varying Filters

• US 20110238390A1
• Filed: 02/03/2011
• Published: 09/29/2011
• Est. Priority Date: 03/29/2010
• Status: Active Grant

• Current Assignee: Jerome R. Krebs, John E. Anderson
• Original Assignee: Jerome R. Krebs, John E. Anderson
• Inventors: Anderson, John E., Krebs, Jerome R.
• Granted Patent: US 8,223,587 B2
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G01V 1/282 Application of seismic models, synthetic seismograms

Abstract

An improved method for reducing the accuracy requirements on the starting model when performing multi-scale inversion of seismic data (65) by local objective function optimization (64). The different scales of inversion are brought about by incorporating a low-pass filter into the objective function (61), and then decreasing the amount of high-frequency data that is filtered out from one scale to the next. Moreover, the filter is designed to be time varying, wherein the filter'"'"'s low-pass cutoff frequency decreases with increasing traveltime of the seismic data being filtered (62). The filter may be designed using Pratt'"'"'s criterion for eliminating local minima, and performing averages (or other statistical measure) of the period and the traveltime error only with respect to source and receiver location but not traveltime (63).

Claims

1. A method for inverting measured seismic data to infer a physical property model of a subsurface region, comprising successively updating the model by performing iterative, multi-scale inversion of the measured seismic data on a computer using local-minimum optimization of an objective function that computes misfit between model-simulated seismic data and the measured seismic data, wherein a changing low-pass filter, hereinafter called the filter, is used to successively alter the objective function from scale to scale by filtering the measured and simulated seismic data in the misfit computation, said filter being time-varying wherein the filter'"'"'s low-pass cutoff frequency varies with traveltime of the seismic data being filtered at one or more scales of the inversion.

2. The method of claim 1, wherein the filter'"'"'s low-pass cutoff frequency decreases with increasing traveltime.

3. The method of claim 1, wherein each successively altered objective function corresponds to the filter being changed to reject less data.

4. The method of claim 3, wherein the objective function is successively altered until a final scale is reached at which the objective function is unaltered and wherein iteration continues until a convergence criterion is satisfied or other stopping condition is met.

5. The method of claim 4, wherein the filter corresponding to the final scale passes all data and rejects none.

6. The method of claim 3, wherein the filter is changed to reject less data by increasing the filter'"'"'s low-pass cutoff frequency.

7. The method of claim 1, wherein one or more iterations are performed at each scale of the multi-scale inversion, i.e. for each change of the high-pass filter and alteration of the objective function.

8. The method of claim 1, wherein the local-minimum optimization of the objective function comprises computing the gradient of the objective function with respect to one or more parameters of a current physical property model, then searching for an updated physical property model, that is a perturbation of the current physical property model in a direction based on the gradient that better explains the measured seismic data.

9. The method of claim 8, wherein the gradient is computed by an adjoint method.

10. The method of claim 1, wherein the inversion is full wavefield inversion.

11. The method of claim 2, wherein to ensure that the iterations converge to a global minimum of the objective function, the following criterion is used to design a time-varying low-pass filter:

the model when iteration begins should be accurate enough to predict traveltimes for any propagation mode to within a half period of that mode after the time varying filter is applied.

12. The method of claim 11, wherein said convergence criterion may be mathematically expressed as:

13. The method of claim 12, wherein α

is a number within the range ½

to 1.

14. The method of claim 12, where rate of decrease of the low-pass cutoff frequency with increasing traveltime is determined consistent with the convergence criterion.

15. The method of claim 1, further comprising using different time-varying filters for different source-receiver offsets.

16. The method of claim 9, wherein the filter is implemented using the inverse covariance matrix C⁻

1 in Equation 1.

17. The method of claim 16, wherein the inverse covariance matrix C⁻

1 is chosen to be non-diagonal in the time dimension, with elements equal to a temporal representation of the time varying filter'"'"'s coefficients, said coefficients varying with time.

18. The method of claim 12, wherein {circumflex over (t)}_error is assumed to be a linear function of traveltime of the form {circumflex over (t)}_error=β

(M₀)t, where M₀ is an initial choice for the model when iteration begins, and β

is a selected constant, wherein if the iterative inversion does not converge using this filter design, then the value of β

would be increased and inversion with this more conservative estimate of {circumflex over (t)}_error would be attempted, and so on until a β

is found that yields convergence for the initial model.

19. The method of claim 1, wherein the inversion is full wavefield inversion.

20. The method of claim 1, wherein a computer is programmed to perform at least some of the method'"'"'s steps.

21. A computer program product, comprising a computer usable medium having a computer readable program code embodied therein, said computer readable program code adapted to be executed to implement a method for full wavefield inversion of measured data to infer a model of a propagating medium for the wavefield, said method comprising:

successively updating the model by performing iterative, multi-scale inversion of the measured data using local-minimum optimization of an objective function that computes misfit between model-simulated data and the measured data;

wherein a changing low-pass filter, hereinafter called the filter, is used to successively alter the objective function from scale to scale by filtering the measured and simulated data in the misfit computation, said filter being time-varying;

wherein the filter'"'"'s low-pass cutoff frequency varies with wavefield traveltime through the medium corresponding to the data being filtered at one or more scales of the inversion.

22. The method of claim 21, wherein the filter'"'"'s low-pass cutoff frequency decreases with increasing traveltime.

23. The method of claim 21, wherein the data being inverted are seismic data, and the propagating medium is a subsurface region of the earth.