Generalized Internal Multiple Imaging via Background Green's Function
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Solution Overview
Problem
Conventional seismic imaging techniques struggle to effectively image internal multiples, which can deteriorate image quality and are often seen as noise, despite their potential to provide valuable information about the subsurface, due to the requirement for a sharp velocity contrast and the complexity of higher-order scattering processes.
Innovation Solution
The generalized internal multiple imaging (GIMI) procedure uses a background Green's function to image internal multiples without needing a sharp velocity model, allowing for the separate or combined imaging of internal multiples of various orders through a series of cross-correlation and back-propagation steps, represented using Feynman diagrams to simplify the analysis and reduce noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional imaging techniques (RTM) are used to image internal multiples, then a sharp velocity contrast is required in the velocity model, but this increases the complexity of the velocity model and reduces imaging reliability
Solution Approach 1:
The patent introduces an intermediary approach by using a smooth background velocity model combined with scattering theory to image internal multiples. Instead of requiring a sharp velocity contrast model, the method uses a smooth background model and treats velocity variations as scattering potentials, thereby mediating between the need for imaging precision and the complexity of the velocity model.
Solution Approach 2:
The patent changes the parameter representation by transforming the velocity model from sharp contrasts to smooth backgrounds with scattering potentials. This parameter transformation allows internal multiples to be imaged without requiring complex sharp velocity contrasts, thus reducing velocity model complexity while maintaining imaging precision.
2Loss of information
If conventional techniques image single scattering energy only, then the imaging process is simpler, but valuable information from higher-order scattering (internal multiples) is lost
Solution Approach 1:
The patent segments the scattering process by separating single scattering and multiple scattering components using scattering theory. This segmentation allows the imaging process to selectively image internal multiples (higher-order scattering) while maintaining a manageable process complexity through systematic decomposition of the scattering series.
Solution Approach 2:
The patent converts the traditionally harmful effect of internal multiples (seen as noise) into a beneficial imaging target. By developing specific imaging conditions that isolate and enhance higher-order scattering terms, the method transforms what was previously information loss into a means of recovering valuable subsurface information.
3Reliability
If internal multiples are not properly imaged, then the imaging process is simpler, but image quality deteriorates and valuable subsurface information is lost
Solution Approach 1:
The patent uses scattering theory as an intermediary framework to systematically handle internal multiples. This intermediary approach provides a structured method for separating and imaging different scattering orders, thereby improving image quality and reliability while keeping the imaging procedure systematically manageable.
4Loss of information
If higher-order scattering terms are included in the imaging process, then more complete subsurface information is obtained, but noise and crosstalk increase
Solution Approach 1:
The patent extracts higher-order scattering terms from the total scattering series using systematic imaging conditions based on scattering theory. By selectively extracting and imaging specific scattering orders while suppressing others, the method recovers complete subsurface information while minimizing noise and crosstalk through controlled separation of scattering components.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
GIMI enables the clear imaging of internal multiples, reducing crosstalk noise and improving the visibility of subsurface reflectors, even in complex models, by isolating higher-order scattering terms and suppressing noise through a dot product approach, thereby enhancing the accuracy of seismic data interpretation.
Implementation Method 1
reverse time migration (RTM) propagates data through a velocity model to provide imaging information
Implementation Method 2
Complex propagation paths can produce internal multiples that are seen as noise in the imaged data
Data Source
AI summary
Various examples are provided for generalized internal multiple imaging (GIMI). In one example, among others, a method includes generating a higher order internal multiple image using a background Green's function and rendering the higher order internal multiple image for presentation. In another example, a system includes a computing device and a generalized internal multiple imaging (GIMI) application executable in the computing device. The GIMI application includes logic that generates a higher order internal multiple image using a background Green's function and logic that renders the higher order internal multiple image for display on a display device. In another example, a non-transitory computer readable medium has a program executable by processing circuitry that generates a higher order internal multiple image using a background Green's function and renders the higher order internal multiple image for display on a display device.