Compressed-Sensing Seismic Acquisition with Constrained Source Paths
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Solution Overview
Problem
Classical seismic data acquisition designs face spatial aliasing issues due to regular grids, leading to inefficiencies in removing backscatter signals, and implementing compressed sensing with random sampling introduces operational constraints that need to be addressed.
Innovation Solution
A method and system for designing seismic data acquisition that combines random spatial sampling with non-overlapping pavements and operational constraints, optimizing the path of seismic sources to minimize curvature and tilt limitations while maintaining randomness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If regular grids are used for seismic data acquisition, then data can be sorted in cross-spread gathers and ground-roll can be denoised, but spatial aliasing issues occur preventing efficient removal of backscatter signals
Solution Approach 1:
The patent transforms the regular grid parameter into a random sampling pattern parameter, changing the fundamental sampling approach from deterministic to stochastic while maintaining operational feasibility through constraint-based randomization
Solution Approach 2:
The patent divides the survey area into multiple non-overlapping pavements and assigns different random sampling patterns to each pavement, segmenting the overall acquisition into manageable sections that can be independently optimized
2Reliability
If thin carpet shooting is used to resolve spatial aliasing, then backscatter signals can be efficiently removed, but the cost becomes very expensive
Solution Approach 1:
The patent changes the sampling density parameter from uniformly fine (thin carpet) to non-uniform random sampling, achieving equivalent or superior spatial coverage with reduced total shot points through compressed sensing theory
Solution Approach 2:
The patent uses random under-sampling that captures only the essential seismic information needed for high-quality imaging, avoiding the excessive sampling of thin carpet shooting while maintaining reconstruction quality through iterative sparse solvers
3Measurement precision
If random sampling is used for compressed sensing acquisition, then Fourier processing properties improve and aliasing is limited, but operational constraints related to source maneuverability and terrain features must be addressed
Solution Approach 1:
The patent applies different random sampling patterns to different local regions (pavements) rather than using a single global pattern, allowing each region to be optimized for its specific operational constraints while maintaining overall random sampling benefits
Solution Approach 2:
The patent pre-defines the random sampling pattern and pavements before field acquisition, allowing operational constraints to be incorporated into the design phase rather than requiring real-time adjustments during source movement
4Ease of operation
If non-overlapping pavements with random sampling are used, then operational constraints can be minimized, but survey time may increase
Solution Approach 1:
The patent optimizes the random sampling pattern dynamically to minimize source path length and curvature while maintaining the randomness required for compressed sensing, balancing operational ease with survey efficiency
Solution Approach 2:
The patent adjusts the random sampling density parameter within pavements to optimize the trade-off between maintaining randomness for quality data and minimizing total shot points to reduce survey time
Data Source
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AI summary
Methods and systems for seismic data acquisition in a survey area use compressed sensing and take into consideration operational limitations. The operational limitations may be related to the equipment used for the survey, the topography of the surveyed area or limitations that otherwise optimize the survey path.