Use nuos technology to acquire optimized 2D data
A method for 2D seismic data acquisition includes determining source-point seismic survey positions for a combined deep profile seismic data acquisition with a shallow profile seismic data acquisition wherein the source-point positions are based on non-uniform optimal sampling. A seismic data set is acquired with a first set of air-guns optimized for a deep-data seismic profile and the data set is acquired with a second set of air-guns optimized for a shallow-data seismic profile. The data are de-blended to obtain a deep 2D seismic dataset and a shallow 2D seismic dataset.
1. A method for 2D seismic data acquisition, the method comprising:
determining source-point seismic survey positions for a combined deep profile seismic data acquisition with a shallow profile seismic data acquisition, the source-point seismic survey positions based on non-uniform optimal sampling using a Monte Carlo Optimization scheme and a cost-function, wherein the source-point survey positions are determined irrespective of any nominal grid;
obtaining a seismic dataset acquired with a first set of air-guns optimized for a deep-data seismic profile and acquired with a second set of air-guns optimized for a shallow-data seismic profile; and
de-blending the seismic dataset to obtain a deep 2D seismic dataset and a shallow 2D seismic dataset.
2. The method of claim 1 , further comprising:
reconstructing the seismic dataset to a nominal grid using interpolated compressive sensing.
3. The method of claim 2 , wherein the nominal grid is a uniformly sampled grid.
4. The method of claim 2 , wherein reconstructing the seismic dataset implements an analysis-based recovery including alternative direction method (ADM) and a variable splitting technique.
5. The method of claim 1 , further comprising:
reconstructing the seismic data to obtain a receiver gather.
6. The method of claim 1 , wherein the cost-function is a Signal-to-Noise Ratio cost-function (SNR cost-function) defined as the root-mean-square SNR of the data to be reconstructed minus the SNR of an elastic wave synthetic dataset over an area of interest using an appropriate velocity model.
7. The method of claim 1 , wherein the cost-function is selected from: diagonal dominance, a conventional array response, a condition number, eigenvalue determination, mutual coherence, trace fold, and azimuth distribution.
8. The method of claim 1 , wherein the first set of air-guns has a first encoded source signature and the second set of air-guns has a second encoded source signature.
9. The method of claim 1 , wherein the determining of the source-point seismic survey positions includes determining an underlying uniformly sampled grid.
10. A method for seismic data acquisition, the method comprising:
determining source-point seismic survey positions for a combined deep profile seismic data acquisition with a shallow profile seismic data acquisition, the source-point seismic survey positions based on non-uniform optimal sampling using a Monte Carlo Optimization scheme, the Monte Carlo Optimization scheme further comprising a Signal-to-Noise Ratio cost-function (SNR cost-function) defined as the root-mean-square SNR of the data to be reconstructed minus the SNR of an elastic wave synthetic dataset over an area of interest using an appropriate velocity model;
obtaining a seismic dataset acquired using a first set of air-guns optimized for a deep-data seismic profile and a second set of air-guns optimized for a shallow-data seismic profile; and
de-blending the seismic dataset to obtain a deep seismic dataset and a shallow seismic dataset.
11. The method of claim 10 , wherein the source-point survey positions are determined irrespective of any nominal grid.
12. The method of claim 10 , further comprising:
reconstructing the seismic dataset to a previously undefined nominal grid using interpolated compressive sensing.
13. The method of claim 12 , wherein the previously undefined nominal grid is a uniformly sampled grid.
14. The method of claim 12 , wherein reconstructing the seismic dataset implements an analysis-based recovery including alternative direction method (ADM) and a variable splitting technique.
15. The method of claim 14 , wherein the analysis based recovery is governed by:
min u ∥Su∥s.t.∥Ru−b∥ 2 ≤σ,
where u is reconstructed seismic data, S is an appropriately chosen dictionary, R is a restriction operator, b is observed seismic data, and σ is noise.
16. A method for seismic data acquisition, the method comprising:
determining source-point seismic survey positions for a combined deep profile seismic data acquisition with a shallow profile seismic data acquisition, the source-point seismic survey positions based on non-uniform optimal sampling using a Monte Carlo Optimization scheme, the Monte Carlo Optimization scheme further comprising a cost-function to determine optimized locations, the cost-function selected from: diagonal dominance, a conventional array response, a condition number, eigenvalue determination, mutual coherence, trace fold, and azimuth distribution;
obtaining a seismic dataset acquired with a first set of air-guns optimized for a deep-data seismic profile and a second set of air-guns optimized for a shallow-data seismic profile; and
de-blending the seismic dataset to obtain a deep seismic dataset and a shallow seismic dataset.
17. The method of claim 16 , wherein the source-point survey positions are determined irrespective of any nominal grid.
18. The method of claim 16 , further comprising:
reconstructing the seismic dataset to a previously undefined nominal grid using interpolated compressive sensing.
19. The method of claim 18 , wherein the previously undefined nominal grid is a uniformly sampled grid.
20. The method of claim 19 , wherein reconstructing the seismic dataset implements an analysis-based recovery including alternative direction method (ADM) and a variable splitting technique.