Dynamic Resolution FWI for Low-Wavenumber Velocity Updates
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Solution Overview
Problem
Existing full-waveform inversion (FWI) methods struggle to balance the contribution of low- and high-wavenumber components in velocity models, with low-wavenumber updates being weak and dominated by high-wavenumber components, limiting the accuracy of subsurface formation imaging.
Innovation Solution
The Dynamic Resolution FWI method separates and optimizes the contribution of low- and high-wavenumber components by applying dynamic weighting to the full wavefield in the FWI kernel, using raw seismic data to enhance the tomographic component and preserve diving and migration components, thereby balancing energy contributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional FWI methods are used to process seismic data, then the velocity model can be updated, but the low-wavenumber updates are weak and dominated by high-wavenumber components, limiting imaging accuracy
Solution Approach 1:
The patent segments the velocity gradient into distinct wavenumber components (low-wavenumber tomographic component and high-wavenumber migration component) and processes them separately through dynamic resolution FWI, allowing independent optimization of each component's contribution to the velocity model
Solution Approach 2:
The patent dynamically changes the resolution parameter during the inversion process, adjusting the weighting between low- and high-wavenumber components based on the current iteration stage and local geological conditions, thereby optimizing information utilization at different processing stages
2Measurement precision
If dynamic weighting is applied to enhance the tomographic component, then low-wavenumber velocity updates are improved, but the complexity of the inversion process increases
Solution Approach 1:
The patent implements dynamic weighting where the relative contribution of low- and high-wavenumber components is adjusted automatically during the inversion process based on convergence criteria and illumination conditions, rather than using fixed static weights, thereby optimizing precision while managing complexity through adaptive control
3Reliability
If the full wavefield is used for inversion, then both diving waves and reflections are optimized, but the computational cost increases
Solution Approach 1:
The patent segments the wavefield processing into distinct components (diving wave component and reflection component) with different weighting strategies, allowing computational resources to be allocated efficiently to each component based on its contribution to the overall velocity model accuracy
Solution Approach 2:
The patent applies local quality by using dynamic weighting that varies spatially across the subsurface, adjusting the contribution of different wave components based on local illumination conditions and geological heterogeneity, thereby optimizing computational efficiency while maintaining reliability in different regions
Data Source
AI summary
A method for raw seismic data inversion includes receiving raw seismic data acquired over an underground formation, receiving an initial velocity model of the underground formation, performing a dynamic resolution full waveform inversion, DR-FWI, on the raw seismic data so that a tomography component of a velocity gradient G is compensated to generate a compensated tomographic component while a diving wave component and a migration component are preserved, outputting an updated velocity model based on an illumination compensated velocity gradient G′, which is calculated based on the compensated tomographic component), and locating natural resources in the underground formation using the updated velocity model updated by the DR-FWI.