Multispectral Variance Seismic Interpretation for Fault Delineation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for delineating edges in seismic images suffer from poor signal-to-noise ratios and contamination by high-dipping events, making it difficult to accurately interpret geologic subsurface features.
Innovation Solution
A method is developed to generate multispectral variance volumes from seismic images by analyzing frequency content and vertical seismic resolution, performing structural oriented filtering, and combining covariance matrices to enhance edge delineation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional variance methods are used to delineate edges in seismic images, then processing is simpler, but the signal-to-noise ratio deteriorates and contamination by high-dipping events increases
Solution Approach 1:
The patent segments the seismic signal into multiple frequency components using spectral decomposition (e.g., Fourier transform or wavelet transform). By dividing the broadband signal into narrow frequency bands, the method processes each frequency component separately to calculate variance, thereby reducing noise and high-dipping event contamination while maintaining accurate edge delineation.
2Measurement precision
If spectral decomposition is performed to improve edge delineation, then measurement precision improves, but computational complexity increases
Solution Approach 1:
The patent applies partial spectral decomposition by selecting specific frequency bands of interest rather than processing the entire frequency spectrum. This approach focuses computational resources on the frequency ranges most relevant for detecting faults and discontinuities, thereby improving edge delineation accuracy while reducing overall computational complexity compared to full-spectrum analysis.
Data Source
AI summary
A method is described for generating multispectral variance volumes from seismic data. The method may include receiving, at a computer processor, a seismic image; analyzing the seismic image to determine frequency content and vertical seismic resolution; performing structural oriented filtering to generate a filtered seismic volume; performing spectral enhancement of the filtered seismic volume using the determined frequency content and vertical seismic resolution to generate a plurality of frequency-dependent seismic volumes; calculating covariance of the frequency-dependent seismic volumes to generate a plurality of covariance matrices; combining the covariance matrices to create a multispectral variance volume. The method is executed by a computer system.