Non-uniform Sampling Grid for Seismic Data Acquisition
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Solution Overview
Problem
Traditional seismic data acquisition methods require more measurements than necessary, leading to inefficiencies and challenges in data reconstruction and processing, particularly in marine seismic imaging, due to the limitations of the Nyquist-Shannon sampling criterion.
Innovation Solution
The implementation of compressive sensing principles, specifically non-uniform optimal sampling design and blended source acquisition, allows for the acquisition and reconstruction of seismic data with fewer measurements, utilizing a mathematical framework to optimize sampling grids and source configurations, and employing randomized greedy algorithms for data reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional Nyquist-Shannon sampling criterion is used, then sufficient information is captured, but measurement quantity increases and acquisition efficiency decreases
Solution Approach 1:
The patent changes the sampling parameters from uniform Nyquist-Shannon spacing to non-uniform compressive sensing spacing. By transforming the sampling strategy from regular intervals to optimized non-uniform intervals based on compressive sensing theory, the system achieves sufficient signal reconstruction with fewer measurements, directly resolving the contradiction between measurement precision and acquisition efficiency
Solution Approach 2:
The patent applies partial sampling by acquiring only the most informative measurements rather than complete uniform sampling. By selecting specific sample points that maximize information content according to compressive sensing criteria, the system obtains sufficient data for accurate reconstruction without performing excessive measurements, thus improving productivity while maintaining measurement precision
2Ease of manufacture
If uniform sampling grid is used, then implementation is simple, but data bandwidth and resolution are limited
Solution Approach 1:
The patent transforms the sampling grid parameters from uniform to non-uniform distribution. By optimizing the sampling intervals according to compressive sensing principles, the system achieves higher data bandwidth and resolution capabilities while maintaining practical implementability through standardized acquisition protocols and processing workflows
3Loss of information
If more measurements are acquired, then data completeness improves, but acquisition time increases
Solution Approach 1:
The patent implements partial sampling by acquiring only the essential measurements needed for accurate signal reconstruction. By identifying and measuring only the most informative sample points according to compressive sensing theory, the system maintains data completeness for practical purposes while significantly reducing the total number of measurements and corresponding acquisition time
Solution Approach 2:
The patent changes the measurement parameters from comprehensive uniform sampling to optimized non-uniform sampling. This parameter transformation enables the system to capture sufficient information content with fewer measurements, directly reducing acquisition time while preserving the necessary data completeness for accurate seismic imaging
Data Source
Figure 1A~1B
Figure 2A~2B
Figure 3
AI summary
Method for acquiring seismic data is described. The method includes obtaining undersampled seismic data acquired from a non-uniform sampling grid. Attenuating multiples from the undersampled seismic data.