Seismic Data Least-Square Migration Using Dip Decomposition
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Solution Overview
Problem
Existing seismic data processing methods, such as Least Squares Migration (LSM), are computationally demanding and face limitations in accurately generating high-resolution subsurface images due to multi-dimensional filter limitations, which hinder effective dip-dependent corrections and result in compromised image quality.
Innovation Solution
The method employs a matching operator F to calculate an updated subsurface image by transforming and matching seismic data, using dip decomposition and data-space filtering to derive and apply filters separately for each dip volume, thereby overcoming the limitations of traditional LSM algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional Least Squares Migration (LSM) algorithms are used to generate subsurface images, then image resolution and quality are improved, but computational time and resource requirements increase significantly
Solution Approach 1:
The patent segments the complex LSM problem into separate dip-dependent processing steps. By decomposing the seismic data into different dip volumes and applying matching filters independently to each dip range, the method reduces the computational burden of processing the entire data set as a single multi-dimensional filter while maintaining image resolution quality.
Solution Approach 2:
The patent introduces a dip dimension to the filtering process by applying matching filters separately for different dip ranges. This dimensional approach transforms the single complex multi-dimensional filter into multiple simpler dip-specific filters, reducing computational complexity while preserving the ability to capture geologic changes at different orientations.
2Measurement precision
If multi-dimensional filters are used in traditional LSM to capture dip-dependent variations, then image quality improves, but filter size and computational complexity increase
Solution Approach 1:
The patent divides the complex multi-dimensional filter into multiple dip-specific matching filters. Each filter operates on a specific dip volume range, simplifying the overall filter structure while maintaining the ability to capture dip-dependent variations in the subsurface image through separate processing of each dip component.
3Productivity
If filter size is reduced to capture spatial changes in migration velocity and structure, then computational efficiency improves, but the ability to apply dip-dependent corrections deteriorates
Solution Approach 1:
The patent compensates for reduced filter size by introducing dip as an additional processing dimension. Instead of relying on larger spatial filters, the method applies multiple smaller dip-specific matching filters across different dip ranges, maintaining dip correction accuracy while improving computational efficiency through the use of smaller, more manageable filter sizes.
Data Source
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Figure 3A~3B
AI summary
Computing device, computer instructions and method for calculating an image of a subsurface based on least square migration and image de-convolution using a matching operator F. The method includes receiving (200) seismic data d; computing (202) a first image m of the subsurface based on the seismic data d; computing (206) a second image h of the subsurface based on the first image m; applying (204, 208) a transform operation to the first and second images m and h to obtain a first transform of the first image and a second transform of the second image; calculating (210) the matching operator F by matching the first transform of the first image to the second transform of the second image; and generating (212) an updated image mupdated of the subsurface based on the matching operator F and the first transform of the first image.