Seismic Signal Reconstruction Using Predicted Energy Distribution
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Solution Overview
Problem
Seismic data processing techniques, such as matching pursuit-based methods, face limitations in accurately reconstructing high-frequency components due to susceptibility to aliasing, especially in single-channel applications, where assumptions about non-overlapping spectral replicas are restrictive, and the ability to describe signals with correct basis functions is weakened as aliasing order increases.
Innovation Solution
The proposed technique involves processing seismic data to determine basis functions using a matching pursuit-based method, where a predicted energy distribution is estimated and used to interpret a cost function, aiding in the selection of basis functions and reducing the likelihood of selecting false minima caused by aliasing or spectral leakage, thereby enhancing the robustness against high-order aliasing and improving signal reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If matching pursuit-based methods are used to reconstruct seismic signals, then signal representation accuracy is improved, but susceptibility to aliasing increases, especially in single-channel applications
Solution Approach 1:
The method performs preliminary actions by estimating the energy distribution of the seismic signal before the matching pursuit reconstruction process. This energy distribution estimate is used to guide the selection of basis functions during iterative reconstruction, allowing the algorithm to anticipate and avoid regions prone to aliasing and spectral leakage, thereby improving reliability while maintaining accuracy
Solution Approach 2:
The method implements feedback by continuously using the estimated energy distribution to interpret the cost function during each iteration of the matching pursuit algorithm. The energy distribution information feeds back into the basis function selection process, creating a closed-loop system that adapts to avoid aliasing artifacts and improves robustness against high-order aliasing
2Device complexity
If conventional matching pursuit methods are used, then computational simplicity is maintained, but ability to describe signals with correct basis functions weakens as aliasing order increases
Solution Approach 1:
The method introduces an intermediary element - the estimated energy distribution - that mediates between the simple matching pursuit framework and the complex task of accurate basis function selection in aliased data. This intermediary provides additional guidance information that improves basis function selection accuracy without fundamentally complicating the overall algorithm structure
Solution Approach 2:
The method changes parameters by incorporating energy distribution estimates into the cost function interpretation process. This parameter change allows the algorithm to adapt its basis function selection criteria based on the estimated signal characteristics, improving accuracy for high-order aliased signals while maintaining computational feasibility
Data Source
AI summary
A technique includes processing seismic data indicative of samples of at least one measured seismic signal in a processor-based machine to, in an iterative process, determine basis functions, which represent a constructed seismic signal. The technique includes in each iteration of the iterative process, selecting another basis function of the plurality of basis functions. The selecting includes based at least in part on the samples and a current version of the constructed seismic signal, determining a cost function; and interpreting the cost function based at least in part on a predicted energy distribution of the constructed seismic signal to select the basis function.