Seismic Data Stacking Using Subsets for Signal-to-Noise Ratio
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Solution Overview
Problem
Conventional seismic stacking methods are ineffective in enhancing the signal-to-noise ratio, particularly when dealing with high amplitude noise, as they simply sum all seismic traces without selecting consistent and relevant information.
Innovation Solution
The method involves identifying and selecting subsets of seismic image volumes that are consistent and similar to each other for optimized stacking, using a similarity matrix to determine the best combinations of volumes for improved signal-to-noise ratio, and producing multiple realizations of final image stacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional stacking methods sum all seismic traces, then the stacking process is simple and fast, but the signal-to-noise ratio is not enhanced and high amplitude noise remains
Solution Approach 1:
The patent segments the seismic trace set into multiple subsets based on similarity analysis. Each subset contains traces with consistent signal characteristics, allowing selective stacking of only those subsets that contribute to enhancing the signal-to-noise ratio while eliminating traces dominated by high amplitude noise
Solution Approach 2:
The patent performs preliminary similarity analysis and noise identification before the stacking operation. By pre-classifying traces into signal-consistent subsets and noise-dominated subsets, the method prepares the data in advance to ensure that only quality traces are stacked, thereby improving the signal-to-noise ratio without compromising processing efficiency
2Measurement precision
If all seismic traces are stacked together, then the processing is straightforward, but consistent and relevant information is not selectively enhanced
Solution Approach 1:
The patent divides the complete seismic trace dataset into multiple subsets based on similarity metrics. This segmentation allows the identification of traces that contain consistent subsurface information versus those contaminated by noise, enabling selective stacking that enhances measurement precision while maintaining processing efficiency through automated classification
Solution Approach 2:
The patent extracts and isolates subsets of traces that exhibit consistent signal characteristics and relevant subsurface information. By separating these quality traces from noise-dominated traces before stacking, the method ensures that only consistent and relevant information is combined, thereby improving measurement precision without significantly impacting processing productivity
Data Source
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AI summary
Systems and methods include seismic data stacking derived from a set of image volumes. Stacking includes finding a sub-set of seismic image volumes (and in some implementations their respective stacking weights) or multiple realizations of sub-set of seismic image volumes from a given set that are consistent and similar to each other. Some or all of the input seismic image volumes can be stacked together as they would be with a conventional stack. However, the signal-to-noise ratio can be enhanced by only stacking those volumes that contain consistent and relevant information. Optimal stacking can utilize an algorithm that can be implemented in a moving window fashion.