Seismic Data High-Frequency Restoration via Time-Frequency Masking
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Solution Overview
Problem
Seismic data processing techniques, such as nonlinear beamforming, often enhance signal-to-noise ratio at the expense of damaging higher frequencies, leading to reduced frequency band and vertical resolution in prestack seismic data, making it challenging to maintain high-frequency content essential for detailed subsurface imaging.
Innovation Solution
The method involves generating time-frequency spectra of original and enhanced seismic traces, constructing a time-frequency mask using noise and signal estimates, and applying this mask to restore high-frequency content in the output traces, thereby preserving the frequency band and improving image resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If SNR enhancement procedures (nonlinear beamforming, supergrouping) are applied to prestack seismic data, then signal-to-noise ratio is improved, but higher frequencies are damaged leading to reduced frequency band and vertical resolution
Solution Approach 1:
The seismic data processing is divided into two distinct stages: first applying SNR enhancement procedures to improve signal-to-noise ratio, then applying a separate high-frequency restoration process using time-frequency masks to recover lost high-frequency content. This segmentation allows each process to optimize for its specific goal without compromising the other.
Solution Approach 2:
A time-frequency mask serves as an intermediary element between the enhanced seismic data and the final output. The mask is constructed by comparing time-frequency spectra of enhanced and original data, identifying frequency regions where high-frequency content was lost, and selectively restoring these frequencies by applying the mask as a weighting function during spectral reconstruction.
2Reliability
If local stacking is performed to enhance weak seismic signals, then signal-to-noise ratio improves, but high-frequency content is suppressed
Solution Approach 1:
The method performs preliminary SNR enhancement through local stacking to make weak signals detectable, then subsequently applies high-frequency restoration as a corrective action. By first ensuring signal detectability and then recovering lost high-frequency information, the method addresses both needs in sequence.
Solution Approach 2:
The restoration process uses feedback by comparing the time-frequency spectrum of the enhanced data with the original data to identify which high-frequency components were lost during stacking. This comparison informs the construction of the time-frequency mask, which selectively restores only the missing high-frequency content while preserving the SNR improvements.
Data Source
AI summary
Methods, systems, and computer-readable medium to perform operations including: generating a first time-frequency spectrum of a first seismic trace from an original seismic dataset; generating a second time-frequency spectrum of a second seismic trace from an enhanced seismic dataset, where the second seismic trace; calculating a difference between the first time-frequency spectrum and the second time-frequency spectrum to generate a noise estimate in the first seismic trace; constructing, based on (i) the noise estimate, (ii) the first time-frequency spectrum, and (iii) the second time-frequency spectrum, a time-frequency mask (TFM); and using the constructed TFM to generate a third time-frequency spectrum of an output trace that corresponds to the first and second seismic traces.