Seismic Imaging Parameter Selection Automation
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Solution Overview
Problem
The manual selection of migration parameters for seismic imaging techniques, such as vertical seismic profiling, is time-consuming and error-prone, especially in complex subterranean environments with conflicting dips, leading to suboptimal image quality.
Innovation Solution
Automated selection of multiple values for migration parameters, applying an imaging technique multiple times with different parameter sets, and aggregating the resulting images to produce a more accurate output image, potentially using Monte Carlo random selection and averaging or other aggregation methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of migration parameters is used, then analyst expertise and experience can be applied, but the process is time-consuming and error-prone
Solution Approach 1:
The system performs self-service by automatically selecting migration parameters through iterative testing and evaluation without requiring manual analyst intervention. The computer system autonomously adjusts parameters, applies imaging techniques, evaluates results, and selects optimal parameters based on predefined criteria, eliminating the time-consuming manual process while maintaining or improving image accuracy.
Solution Approach 2:
The system implements feedback by evaluating the quality of images produced with different migration parameter sets and using this evaluation to guide subsequent parameter selections. The computer system analyzes image quality metrics, compares results across multiple iterations, and uses this feedback to converge on optimal parameters, thereby reducing time loss while improving measurement precision.
2Measurement precision
If manual selection of migration parameters is used, then geological knowledge can be applied, but the process is error-prone
Solution Approach 1:
The system performs self-service by autonomously selecting migration parameters through systematic evaluation rather than relying on manual analyst judgment. The computer system independently tests multiple parameter combinations, evaluates image quality objectively, and selects parameters based on quantitative criteria, eliminating human errors while maintaining geological accuracy through algorithmic rigor.
Solution Approach 2:
The system performs preliminary action by pre-testing multiple migration parameter sets before final image production. The computer system evaluates various parameter combinations in advance, identifies promising candidates through preliminary imaging, and then refines the selection based on detailed quality assessment, thereby reducing errors through thorough pre-evaluation.
3Measurement precision
If multiple imaging applications with different parameters are performed, then image accuracy is improved, but computational complexity increases
Solution Approach 1:
The system applies segmentation by dividing the imaging process into distinct stages: initial parameter set generation, preliminary imaging applications, quality evaluation, and final selection. Each stage processes a specific subset of parameters and produces intermediate results that feed into the next stage, managing computational complexity through structured decomposition while achieving high image accuracy through cumulative refinement.
Solution Approach 2:
The system applies partial action by performing imaging with a selected subset of parameter combinations rather than exhaustively testing all possible parameters. The computer system strategically samples the parameter space, applying imaging techniques to representative parameter sets that are most likely to yield optimal results, thereby reducing computational complexity while maintaining image accuracy through intelligent sampling.
Data Source
AI summary
Different values of at least one migration parameter are selected. An imaging technique is applied a plurality of times, where each application of the imaging technique uses a corresponding different one of the different values. Each application of the imaging technique produces a corresponding image of a subterranean structure. An aggregate of the images is computed to produce an output image of the subterranean structure.