Automated system and methods for adaptive robust denoising of large-scale seismic data sets

1. A computer-implemented method, comprising:

improving denoising performance including using an automated process for denoising large-scale data sets, the automated process being adaptable to local signal and noise characteristics of a local region, the automated process comprising:

receiving, by an automated denoising system, seismic survey data;

indexing, by the automated denoising system, the received seismic survey data into index sets and partitioning each index set into data blocks;

estimating, by the automated denoising system, ambient noise spectra of the seismic survey data;

tuning algorithm parameters and model assumptions of the automated denoising system based on the local signal and noise characteristics and the estimated ambient noise spectra;

determining a normal range of values for the local region;

for each particular data block of a particular index set:

slicing the particular data block into frequency slices; and

for each particular frequency slice of the particular data block and using the tuned algorithm parameters and model assumptions, processing the particular frequency slice to remove random and erratic noise by:

forming a Hankel matrix from the particular frequency slice;

determining an optimal rank for the Hankel matrix;

determining a clean signal and erratic noise from the ranked Hankel matrix; and

returning the clean signal and erratic noise for the particular frequency slice;

assembling, by the automated denoising system, a clean signal data set from the index sets; and

providing, by the automated denoising system, the clean signal data set for drilling input.

2. The computer-implemented method of claim 1 , further comprising:

detecting, using the normal range of values for the local region, extreme values in the indexed seismic survey data;

removing the extreme values from the indexed seismic survey data to generate filtered seismic survey data; and

estimating the ambient noise spectra in the filtered seismic survey data.

3. The computer-implemented method of claim 2 , further comprising performing a Fourier transform on the data block to transform the data block into a frequency domain.

4. The computer-implemented method of claim 1 , wherein the determination of the optimal rank for the Hankel matrix is based on either a rank-revealing decomposition or a low-rank approximation.

5. The computer-implemented method of claim 4 , wherein the rank-revealing decomposition is one of a QR decomposition, a Lanczos bidiagonalization, or a partial singular value decomposition (SVD).

6. The computer-implemented method of claim 1 , wherein determining the clean signal and the erratic noise from the ranked Hankel matrix further comprises obtaining a vector from the ranked Hankel matrix, wherein the clean signal and the erratic noise are separate components of the obtained vector.

7. The computer-implemented method of claim 1 , further comprising performing an inverse Fourier transform on the returned clean signal and erratic noise for the particular frequency slice.

8. A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:

improving denoising performance including using an automated process for denoising large-scale data sets, the automated process being adaptable to local signal and noise characteristics of a local region, the automated process comprising:

receiving, by an automated denoising system, seismic survey data;

estimating, by the automated denoising system, ambient noise spectra of the seismic survey data;

indexing, by the automated denoising system, the received seismic survey data into index sets and partitioning each index set into data blocks;

tuning algorithm parameters and model assumptions of the automated denoising system based on the local signal and noise characteristics and the estimated ambient noise spectra;

determining a normal range of values for the local region;

for each particular data block of a particular index set:

slicing the particular data block into frequency slices; and

for each particular frequency slice of the particular data block and using the tuned algorithm parameters and model assumptions, processing the particular frequency slice to remove random and erratic noise by:

forming a Hankel matrix from the particular frequency slice;

determining an optimal rank for the Hankel matrix;

determining a clean signal and erratic noise from the ranked Hankel matrix; and

returning the clean signal and erratic noise for the particular frequency slice;

assembling, by the automated denoising system, a clean signal data set from the index sets; and

providing, by the automated denoising system, the clean signal data set for drilling input.

9. The non-transitory, computer-readable medium of claim 8 , further comprising:

detecting, using the normal range of values for the local region, extreme values in the indexed seismic survey data;

removing the extreme values from the indexed seismic survey data to generate filtered seismic survey data; and

estimating the ambient noise spectra in the filtered seismic survey data.

10. The non-transitory, computer-readable medium of claim 9 , further comprising performing a Fourier transform on the data block to transform the data block into a frequency domain.

11. The non-transitory, computer-readable medium of claim 8 , wherein the determination of the optimal rank for the Hankel matrix is based on either a rank-revealing decomposition or a low-rank approximation.

12. The non-transitory, computer-readable medium of claim 11 , wherein the rank-revealing decomposition is one of a QR decomposition, a Lanczos bidiagonalization, or a partial singular value decomposition (SVD).

13. The non-transitory, computer-readable medium of claim 8 , wherein determining the clean signal and the erratic noise from the ranked Hankel matrix further comprises obtaining a vector from the ranked Hankel matrix, wherein the clean signal and the erratic noise are separate components of the obtained vector.

14. The non-transitory, computer-readable medium of claim 8 , further comprising performing an inverse Fourier transform on the returned clean signal and erratic noise for the particular frequency slice.

15. A computer-implemented system, comprising:

a computer memory; and

a hardware processor interoperably coupled with the computer memory and configured to perform operations comprising:

improving denoising performance including using an automated process for denoising large-scale data sets, the automated process being adaptable to local signal and noise characteristics of a local region, the automated process comprising:

receiving, by an automated denoising system, seismic survey data;

estimating, by the automated denoising system, ambient noise spectra of the seismic survey data;

indexing, by the automated denoising system, the received seismic survey data into index sets and partitioning each index set into data blocks;

tuning algorithm parameters and model assumptions of the automated denoising system based on the local signal and noise characteristics and the estimated ambient noise spectra;

determining a normal range of values for the local region;

for each particular data block of a particular index set:

slicing the particular data block into frequency slices; and

for each particular frequency slice of the particular data block and using the tuned algorithm parameters and model assumptions, processing the particular frequency slice to remove random and erratic noise by:

forming a Hankel matrix from the particular frequency slice;

determining an optimal rank for the Hankel matrix;

determining a clean signal and erratic noise from the ranked Hankel matrix; and

returning the clean signal and erratic noise for the particular frequency slice;

assembling, by the automated denoising system, a clean signal data set from the index sets; and

providing, by the automated denoising system, the clean signal data set for drilling input.

16. The computer-implemented system of claim 15 , further comprising:

detecting, using the normal range of values for the local region, extreme values in the indexed seismic survey data;

removing the extreme values from the indexed seismic survey data to generate filtered seismic survey data; and

estimating the ambient noise spectra in the filtered seismic survey data.

17. The computer-implemented system of claim 16 , further comprising performing a Fourier transform on the data block to transform the data block into a frequency domain.

18. The computer-implemented system of claim 15 , wherein the determination of the optimal rank for the Hankel matrix is based on either a rank-revealing decomposition or a low-rank approximation, and the rank-revealing decomposition is one of a QR decomposition, a Lanczos bidiagonalization, or a partial singular value decomposition (SVD).

19. The computer-implemented system of claim 15 , wherein determining the clean signal and the erratic noise from the ranked Hankel matrix further comprises obtaining a vector from the ranked Hankel matrix, wherein the clean signal and the erratic noise are separate components of the obtained vector.

20. The computer-implemented system of claim 15 , further comprising performing an inverse Fourier transform on the returned clean signal and erratic noise for the particular frequency slice.