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Diffraction Separation and Imaging Using an Improved Singular Value Decomposition Method
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AI
DOI：10.1109/LGRS.2022.3168016.png)
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Abstract
En 中文
Faults, fractures, karst caves, and other small-scale geological targets are critical for carbonate oil and gas exploration. However, seismic responses of these small-scale geological targets are low-energy diffractions and it is difficult for traditional method to image them with high resolution. To identify these targets, the diffraction separation is a key technology. Based on the difference of diffractions and reflections in both kinematic and dynamic properties, the singular value decomposition (SVD) method can separate diffractions and reflections effectively. However, how to select the appropriate singular value sequence for diffractions and reflections is a key issue in the application. Based on the analysis of the singular value spectrum characteristics, we propose a second-order difference spectrum strategy to improve the SVD method. The improved SVD method can separate the diffractions and reflections with minimal error. It is stable when seismic data contain Gaussian noise. Reverse time migration (RTM) method is used in the diffraction imaging because it can keep the true shape of subsurface scatterers when diffractions information is complete. Synthetic examples demonstrate the effectiveness of this method.
Keywords:
Diffraction
Reflection
Image reconstruction
Transforms
Data models
Singular value decomposition
Geology
Diffraction separation
reverse time migration (RTM)
second-order difference spectrum strategy
singular value decomposition (SVD)
Journal
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16.4
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1.0W
Citations:
5.1K