Seismic Coherence Imaging Using Singular Vector Variance
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Solution Overview
Problem
Existing coherence estimation techniques in seismic data interpretation produce artifacts when trace energies are low, leading to incomplete images and missed subtle subsurface features.
Innovation Solution
Employing singular value decomposition (SVD) to estimate coherence based on the variance of right singular vectors, rather than total trace energy, to generate a geophysical image that accurately represents subsurface features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If eigenvalue-based coherence estimation using total trace energy is used, then computational simplicity is improved, but image quality deteriorates due to artifacts in low-energy regions
Solution Approach 1:
The patent changes the fundamental parameter used for coherence estimation from total trace energy to singular value decomposition metrics. By decomposing the data matrix into singular values and vectors, the method transforms the coherence calculation into a process that separates signal components from noise, eliminating artifacts in low-energy regions while maintaining computational feasibility through efficient SVD algorithms.
2Productivity
If coherence estimation based on total trace energy is applied, then processing speed is improved, but detection accuracy deteriorates for subtle subsurface features
Solution Approach 1:
The patent segments the seismic data representation by decomposing it into orthogonal singular vectors through SVD. This segmentation separates the coherent signal components (captured by dominant singular vectors) from incoherent noise (captured by smaller singular vectors), enabling accurate detection of subtle features while maintaining processing efficiency through the hierarchical structure of singular value decomposition.
3Device complexity
If traditional coherence estimation methods are used, then algorithm complexity is reduced, but image continuity deteriorates in low-amplitude regions
Solution Approach 1:
The patent introduces singular value decomposition as an intermediary process between raw seismic data and coherence estimation. The SVD acts as a mediator that transforms the data into a form where coherence can be accurately measured without being corrupted by low-energy artifacts, thereby ensuring image continuity while keeping the overall algorithm structure manageable through well-established linear algebra techniques.
Data Source
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AI summary
A method for generating a geophysical image of a subsurface region includes defining a computational sub-volume for the geophysical image including a predetermined number of a plurality of seismic traces and a predetermined number of samples per each one of the plurality of seismic traces, generating a data matrix corresponding to a first sub-volume of the subsurface region based on the defined computational sub-volume, the data matrix comprising the predetermined number of samples for the predetermined number of traces of a portion of a seismic dataset corresponding to the first sub-volume. The method also includes performing a singular value decomposition and estimating a coherence between the predetermined number of traces of the data matrix by performing a weighted sum of a variance of a plurality of right singular vectors of the data matrix, and assigning the estimated coherence to a location in the geophysical image.