Seismic Coherence Estimation via Variance Summation
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Solution Overview
Problem
Existing coherence estimation techniques in seismic data interpretation produce artifacts at low trace energies, leading to incomplete imaging of subsurface features and missed subtle geological discontinuities.
Innovation Solution
A method that estimates coherence by computing the sum of squares of samples in data matrices for seismic traces, eliminating the need for singular value decomposition (SVD) and avoiding energy-based quotients, thereby generating more continuous images with reduced artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If eigenvalue-based coherence estimation using total energy quotient is used, then computational framework is established, but artifacts are produced at low trace energies
Solution Approach 1:
The patent changes the mathematical parameters of coherence estimation from eigenvalue-based total energy quotient to a method using sum of variances of trace differences. This parameter change eliminates the denominator that causes division by near-zero values, thereby removing artifacts at low trace energies while maintaining coherence estimation accuracy.
2Measurement precision
If SVD-based coherence estimation is used, then comprehensive subsurface imaging is achieved, but computational expense increases
Solution Approach 1:
The patent replaces the computationally expensive SVD operation with a simpler, faster calculation method that uses sum of variances of trace differences. This simpler method achieves comparable coherence estimation results without the heavy computational burden of SVD, thereby improving processing speed and productivity.
3Ease of manufacture
If traditional coherence estimation is used, then processing framework is established, but subtle discontinuities are missed
Solution Approach 1:
The patent enhances local quality of coherence estimation by using sum of variances of trace differences, which is more sensitive to subtle variations in seismic traces. This local improvement in estimation quality allows detection of subtle geological discontinuities that were previously missed, while maintaining ease of implementation through straightforward computational steps.
Data Source
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 seismic traces 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 estimating a coherence between the predetermined number of traces of the data matrix by performing a sum of a variance of the predetermined number of samples of the data matrix, and assigning the estimated coherence to a location in the geophysical image.