Joint Inversion Seismic Imaging with Gather Flatness
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Solution Overview
Problem
Current seismic data processing methods, such as tomographic MVA and FWI, face challenges in producing high-resolution velocity models while ensuring gather flatness, with FWI offering high resolution but lacking gather flatness and tomographic MVA providing low-resolution models that are difficult to improve.
Innovation Solution
A joint inversion scheme combining FWI and tomographic MVA gradients to minimize a joint objective function, which balances the advantages of both methods by using a weighting parameter to control the relative importance of each gradient, ensuring high-resolution velocity updates with enforced gather flatness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full wavefield inversion (FWI) is used to estimate velocity model, then velocity model resolution is improved, but gather flatness is not ensured
Solution Approach 1:
The patent combines FWI and tomographic MVA into a joint inversion scheme that simultaneously optimizes both velocity model resolution and gather flatness. The joint objective function integrates the data-misfit term from FWI and the depth-error term from tomographic MVA, allowing both goals to be achieved together rather than separately.
Solution Approach 2:
The patent introduces a weighting parameter λ that controls the relative contribution of FWI and tomographic MVA components in the joint objective function. By adjusting this parameter, the optimization can balance between achieving high velocity model resolution and ensuring gather flatness according to specific survey requirements.
2Manufacturing precision
If tomographic MVA is used to estimate velocity model, then gather flatness is improved, but velocity model resolution deteriorates
Solution Approach 1:
The patent merges the strengths of tomographic MVA (gather flatness) and FWI (velocity resolution) into a unified inversion framework. The joint objective function allows simultaneous optimization of both aspects, overcoming the limitation where one method excels at the expense of the other.
Solution Approach 2:
The joint objective function acts as a composite optimization target that integrates components from two different methodologies. By combining the data-misfit term (||d−Gm||²) and depth-error term (||Tm−z||²) with appropriate weighting, the system achieves properties superior to either individual method alone.
3Measurement precision
If joint inversion scheme is used to combine FWI and tomographic MVA, then both velocity model resolution and gather flatness are improved, but computational complexity increases
Solution Approach 1:
The patent implements a iterative optimization approach where the joint objective function is minimized through successive approximations. Rather than solving the full complex problem in one step, the method performs multiple iterations of gradient computation and model updating, each dealing with a simplified version of the overall optimization task.
Data Source
AI summary
Methods for updating a physical properties model of a subsurface region that combine advantages of FWI and tomography into a joint inversion scheme are provided. One method comprises obtaining measured seismic data; generating simulated seismic data using an initial model; computing at least one of an FWI gradient and a tomography gradient; minimizing a joint objective function E, wherein the objective function E is based on a combination of the FWI gradient and the tomography gradient or preconditioning of the FWI gradient or the tomography gradient; generating a final model based on the minimized joint objective function E; and using the final model to generate a subsurface image. The joint objective function E may be a function of one or both a FWI objective function CFWI and a tomography objective function CTomo. The joint objective function E may be defined as a weighted sum of CFWI and CTomo.