Fourier Anti-Leakage Seismic Interpolation via Iterative Spectrum Refinement
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Solution Overview
Problem
Existing anti-leakage Fourier transform (ALFT) methods for seismic data interpolation are computationally costly, especially for large seismic datasets, necessitating an efficient implementation to reduce computational resources while maintaining high-quality seismic image generation.
Innovation Solution
The method involves transforming seismic data from a first domain to a second domain, iteratively estimating a weight function, generating a weighted input spectrum, identifying and removing the strongest attribute, and updating the input spectrum until a condition is met, followed by inverse transformation to generate an output seismic dataset and image for hydrocarbon reservoir location determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ALFT methods are used for seismic data interpolation, then high-quality seismic images can be generated, but computational resources are excessively consumed
Solution Approach 1:
The patent divides the seismic data processing into distinct stages: forward transformation to frequency domain, iterative spectrum refinement with weight function estimation, and inverse transformation. Each stage processes specific aspects of the data independently, allowing computational tasks to be segmented and optimized separately rather than processed as a monolithic operation.
Solution Approach 2:
The patent performs preliminary transformation of seismic data to the frequency domain before interpolation, and pre-estimates weight functions based on initial spectrum analysis. These preliminary actions prepare the data in an optimized state that reduces the computational burden of subsequent iterative refinement steps.
2Manufacturing precision
If traditional ALFT methods are used for large seismic datasets, then accurate interpolation is achieved, but processing time increases significantly
Solution Approach 1:
The patent implements periodic iterative refinement where the weight function is re-estimated and the spectrum is updated in repeated cycles until convergence criteria are met. This periodic action allows the algorithm to progressively improve interpolation accuracy through multiple passes, with each iteration building upon previous results rather than requiring complete reprocessing.
Solution Approach 2:
The patent incorporates feedback mechanisms where the estimated weight function is used to update the input spectrum, which then feeds into the next weight function estimation. This closed-loop feedback system continuously refines the interpolation based on the evolving spectral information, improving accuracy while avoiding redundant computations through intelligent convergence checking.
Data Source
AI summary
Methods and systems for improving the efficiency of ALFT are disclosed. The methods include obtaining a seismic dataset of a subterranean region, transforming the seismic dataset from a first domain to a second domain using a transform function, and generating an input spectrum from the transformed seismic dataset. The methods also include iteratively, or recursively, until a condition is met, for each slice, estimating a weight function using the input spectrum, generating a weighted input spectrum using the weight function and the input spectrum, identifying a strongest attribute from the weighted input spectrum, determining an output spectrum based on the identified strongest attribute, and updating the input spectrum by removing the identified strongest attribute from the input spectrum. The methods further include determining an output spectrum volume by combining the output spectrum for each slice, generating an output seismic dataset using an inverse transform function and the output spectrum volume.