Directional Q Compensation Using Sparse Inversion and Preconditioning
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Solution Overview
Problem
Conventional Q compensation techniques in seismic data processing face challenges such as incorrect subsurface travel time estimation due to complex velocity fields, limited resolution enhancement, and amplification of noise and spatial aliasing, which affect the accuracy of seismic data compensation.
Innovation Solution
The method involves calculating angle-dependent subsurface travel times and applying directional Q compensation using multi-dimensional filters or sparse inversion algorithms with preconditioned operators to correct for Q effects, deghosting, and source designature, thereby improving seismic data resolution without relying on velocity fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional Q compensation techniques are applied using velocity fields, then Q effects can be corrected, but incorrect subsurface travel time estimation occurs due to complex velocity fields
Solution Approach 1:
The patent extracts and removes the dependency on velocity fields from the Q compensation process. By formulating the problem in the frequency-wavenumber domain and using spectral ratios, the method eliminates the need for velocity field inputs that cause travel time estimation errors, directly addressing the contradiction between measurement precision and reliability.
Solution Approach 2:
The patent replaces the conventional mechanical approach of using velocity fields and time-domain filtering with a spectral-domain method. By substituting the velocity field-based mechanical model with frequency-wavenumber domain spectral analysis, the method achieves more accurate travel time estimation without relying on complex velocity field assumptions.
2Measurement precision
If conventional Q compensation is applied, then Q effects are addressed, but noise and spatial aliasing are amplified
Solution Approach 1:
The patent changes the domain parameters from time-domain to frequency-wavenumber domain, enabling selective filtering. By working in the spectral domain, the method can enhance resolution through Q compensation while simultaneously suppressing noise and spatial aliasing by operating on specific frequency and wavenumber components, rather than uniformly amplifying all frequencies as in conventional time-domain methods.
Solution Approach 2:
The patent introduces spectral ratios as an intermediary mechanism. By forming ratios of spectral components at different frequencies, the method achieves Q compensation while the spectral domain representation naturally suppresses noise and spatial aliasing, acting as an intermediary that separates signal enhancement from noise amplification.
3Ease of manufacture
If 1-D filtering is used for Q compensation, then processing is simpler, but resolution enhancement is limited
Solution Approach 1:
The patent transitions from one-dimensional time-domain filtering to two-dimensional frequency-wavenumber domain processing. This dimensional expansion enables the method to achieve superior resolution enhancement by independently controlling frequency and spatial components, while the systematic spectral ratio approach maintains processing simplicity despite the increased dimensional complexity.
Data Source
AI summary
A method for directional Q compensation of seismic data may comprise calculating angle-dependent subsurface travel times; applying directional Q compensation to the prestack seismic data to obtain Q-compensated data in time-space domain, wherein the directional Q compensation is based on the angle-dependent subsurface travel times; and using the Q-compensated data to generate an image of the subsurface. Directional Q compensation may comprise determining an angle-dependent forward E operator and an angle-dependent adjoint E* operator using the angle-dependent subsurface travel times; and applying a sparse inversion algorithm using the angle-dependent operators to obtain a model of Q-compensated data. The angle-dependent operators may be preconditioned by introducing ghost and source effects in a wavelet matrix and a transpose of the wavelet matrix, respectively, such that applying a sparse inversion algorithm using the preconditioned angle-dependent operators is used to obtain a model of Q-compensated, deghosted data without source effects.


