Beam-domain local correction for migration imaging artifacts
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Solution Overview
Problem
Existing methods for generating images from sparse waveform return data are hindered by significant artifacts, requiring large datasets that are costly and resource-intensive to process, while increasing data quantity does not always improve image quality due to duplicative information and high computing resource demands.
Innovation Solution
The proposed solution transforms the imaging problem from the shot-data domain to the beam domain using windowed Fourier transform frames expansion, allowing for local correction of artifacts by identifying relevant beams that pass through regions of interest, thereby reducing the migration imaging artifacts without increasing the number of shots processed, and utilizing machine-learning models to select relevant waveform return data for processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the quantity of data is increased to reduce artifacts, then image quality is improved, but computing resources and processing time increase dramatically
Solution Approach 1:
The patent segments the image into multiple patches and processes each patch independently using optimization techniques. This divides the large-scale computationally intensive problem into smaller, manageable sub-problems that can be solved more efficiently with fewer computing resources while maintaining overall image quality.
Solution Approach 2:
The patent applies local correction techniques to specific regions of the image rather than processing the entire image uniformly. By identifying regions with artifacts and applying targeted correction only to those areas, the method improves image quality where needed while minimizing unnecessary computation in already-clean regions.
2Measurement precision
If the quantity of data is increased to reduce artifacts, then image quality is improved, but processing time increases
Solution Approach 1:
The patent divides the image into multiple patches and processes them in parallel or sequentially with optimized algorithms. This segmentation approach reduces the overall processing time compared to treating the entire image as a single large problem, while still achieving artifact reduction across the complete image.
Solution Approach 2:
The patent applies correction techniques selectively to regions where artifacts are present rather than processing the entire image uniformly. This partial action approach reduces processing time by avoiding redundant computations in regions that do not require correction.
3Productivity
If optimization techniques are used to generate solutions to inverse problems, then image data can be generated from sparse data, but significant artifacts are introduced
Solution Approach 1:
The patent segments the image into multiple patches and applies optimization techniques to each patch individually. This segmentation approach reduces the artifacts introduced by optimization because each smaller patch produces fewer artifacts compared to optimizing the entire large-scale inverse problem at once.
Solution Approach 2:
The patent applies local correction techniques to specific regions where artifacts are present, improving the local image quality without requiring reprocessing of the entire image. This targeted approach maintains the productivity benefit of optimization while reducing the harmful artifacts in critical regions.
Data Source
AI summary
A method includes obtaining waveform return data including waveform return records for multiple sampling events associated with an observed area and generating image data based on the first subset of waveform return records. The method also includes reducing imaging artifacts in a region of interest of the image data using beam-domain local correction operations.