Seismic Anisotropy Parameter Determination via Double-Search Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for determining anisotropic parameters from surface seismic data are limited by inversion techniques, which often rely on accurate initial value assumptions and are computationally intensive, especially when dealing with poor data quality and strong nonlinearities.
Innovation Solution
A double-searching schedule combining exhaustive search and global optimization using the Very Fast Simulated Annealing (VFSA) technique to optimize Thomsen's anisotropic parameters for layered models, reducing dependence on initial value accuracy and achieving rapid convergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive search is used to determine anisotropic parameters, then measurement precision is improved, but loss of time increases due to computational intensity
Solution Approach 1:
The patent segments the parameter search space into multiple stages: an exhaustive search over a coarse grid to identify promising regions, followed by a refined search using global optimization algorithms (such as genetic algorithms or simulated annealing) around the best candidates. This segmentation allows the system to achieve high measurement precision while reducing overall computational time by avoiding exhaustive search of the entire parameter space.
Solution Approach 2:
The patent performs preliminary action by conducting an exhaustive search over a coarse grid of Thomsen parameters (δ and ε) to generate an error map and identify promising search regions before applying more computationally intensive global optimization algorithms. This preliminary action filters out obviously incorrect parameter combinations, reducing the search space for subsequent optimization steps.
2Loss of time
If global optimization algorithms are used, then loss of time is reduced, but reliability decreases due to dependence on initial value assumptions
Solution Approach 1:
The patent performs a preliminary exhaustive search over a coarse grid of Thomsen parameters to generate an error map and identify promising search regions. This preliminary action provides reliable initial value assumptions for subsequent global optimization algorithms, ensuring that the optimization starts from meaningful points rather than arbitrary guesses, thereby improving reliability while maintaining computational efficiency.
Solution Approach 2:
The patent uses feedback from the exhaustive search results (error maps) to guide the global optimization process. The error map generated from the exhaustive search provides feedback about which parameter regions are most promising, allowing the optimization algorithms to focus their search on the most likely correct parameter values, thus improving reliability without requiring extensive computational time.
3Manufacturing precision
If inversion techniques with accurate initial values are used, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the inversion process into two distinct stages: (1) an exhaustive search over a coarse grid of Thomsen parameters to generate an error map and identify promising regions, and (2) a refined global optimization using algorithms such as genetic algorithms or simulated annealing. This segmentation simplifies the overall device complexity by breaking down the complex inversion problem into manageable steps, each with well-defined inputs and outputs, while maintaining high manufacturing precision through the systematic combination of both stages.
Data Source
AI summary
In some embodiments, an apparatus and a system, as well as a method and an article, may operate to receive seismic survey data for use with an isotropic velocity model describing a selected geological formation volume. Further activity may include exhaustively searching the seismic survey data to provide an error map, globally optimizing the error map to provide anisotropy parameters for the selected geological formation volume, and inverting the anisotropy parameters to transform the isotropic velocity model into an anisotropic velocity model for the selected geological formation volume. Additional apparatus, systems, and methods are described.