Tomographic Velocity Analysis Using Structure Tensor Constraints
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing tomographic migration velocity analysis (MVA) processing methods require tedious and time-consuming manual picking of geological horizons and residual moveouts, especially in complex structures, which hampers efficient subsurface imaging in seismic exploration.
Innovation Solution
An automatic 3D grid-based tomographic MVA approach using a structure tensor as a constraint to estimate local dip and azimuth information, eliminating the need for manual picking by calculating Fréchet derivatives during tomographic inversion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual picking is used for horizon and residual moveout estimation, then measurement precision can be achieved, but productivity is significantly reduced due to tedious and time-consuming operations
Solution Approach 1:
The system performs self-service by automatically estimating dip and azimuth information through structure tensor computation without requiring manual operator intervention. The algorithm independently processes the depth image volume to extract structural parameters, eliminating the need for operators to manually pick horizons and measure dips, thus resolving the contradiction between measurement precision and productivity
Solution Approach 2:
The patent replaces the mechanical manual picking process with an automated computational system. Instead of operators manually analyzing depth images and measuring structural parameters, a structure tensor-based algorithm automatically computes dip and azimuth information, substituting human mechanical operations with automated image processing and mathematical computations
2Measurement precision
If manual picking is used in complex structures, then measurement precision can be maintained, but loss of time increases significantly
Solution Approach 1:
The structure tensor computation provides continuous dip and azimuth estimates across the entire depth image volume, rather than requiring discrete manual measurements at specific locations. This continuous automated processing maintains measurement precision in complex structures while dramatically reducing the time required, as the algorithm processes all regions simultaneously without interruption or operator fatigue
3Productivity
If automated methods are used for velocity analysis, then productivity is improved, but measurement precision may deteriorate due to lack of manual control
Solution Approach 1:
The patent replaces manual mechanical picking with an automated structure tensor computation system that uses mathematical operations to estimate dip and azimuth. This substitution maintains high productivity through full automation while preserving measurement precision through rigorous mathematical formulations that objectively analyze the depth image volume's structural characteristics without human bias or error
Data Source
AI summary
An example method for tomographic migration velocity analysis may include collecting seismographic traces from a subterranean formation and using an initial velocity model to generate common image gathers and a depth image volume based, at least in part, on the seismographic traces. A structure tensor may be computed with the depth image volume for automated structural dip and azimuth estimation. A semblance may be generated using said plurality of common image gathers and said structure tensor. Image depth residuals may be automatically picked from said semblance. A ray tracing computation may be performed on said initial velocity models using said structure tensor. An updated velocity model may be generated with a tomographic inversion computation, wherein said tomographic inversion computation uses said plurality of image depth residuals and said ray tracing computation.