Ocean Bottom Node Survey Layout With Constrained Random Sampling
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Solution Overview
Problem
Implementing compressive sensing (CS)-based random sampling design for seismic data acquisition in marine and ocean bottom node (OBN) surveys is challenging due to constraints from the movement of sources and receivers in the field.
Innovation Solution
A system and method that introduces randomness in the movement of vessels, such as sinusoidal arcs, to enable CS-based data acquisition, using spatial and temporal constraints to generate off-the-grid locations with controlled sub-sampling ratios and jittered sampling parameters, and applies multidimensional transforms to optimize seismic survey design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional seismic survey design is used with regular grid sampling, then the acquisition process is simple and reliable, but the data quality suffers from aliasing and poor reconstruction
Solution Approach 1:
The patent applies parameter changes by transforming the regular grid sampling into compressed sensing-based irregular sampling. Specifically, it uses random sampling patterns, jittered sampling, and sinusoidal arc movements to create variability in the acquisition geometry. This changes the sampling parameters from fixed and regular to random and adaptive, thereby improving data quality and reducing aliasing while maintaining manageable system complexity through algorithmic optimization.
2Measurement precision
If random sampling design is used to improve data quality, then aliasing is reduced and reconstruction improves, but the acquisition becomes constrained by vessel movement and source-receiver positioning
Solution Approach 1:
The patent applies dynamics by introducing dynamic vessel movement patterns, specifically sinusoidal arcs, to replace static regular grid positioning. The vessels move along predetermined sinusoidal paths during data acquisition, creating dynamic sampling patterns that satisfy compressed sensing requirements. This dynamic approach enables random sampling benefits while providing operational guidance through structured movement patterns, balancing reconstruction quality with operational feasibility.
Solution Approach 2:
The patent utilizes periodic action through sinusoidal arc movements of vessels during survey operations. The periodic sinusoidal paths create predictable yet varied sampling patterns that satisfy compressed sensing criteria. This periodic motion introduces randomness in the sampling geometry while maintaining a structured acquisition process, thereby improving reconstruction quality without completely complicating operational procedures.
3Measurement precision
If higher sampling density is used to reduce aliasing, then data quality improves, but the acquisition cost and time increase
Solution Approach 1:
The patent applies parameter changes by optimizing the sampling density through compressed sensing theory. Instead of uniformly increasing sampling density throughout the survey area, it strategically places samples according to the singular value decomposition of the survey design matrix. This changes the sampling distribution from uniform high density to optimized variable density, reducing total number of samples needed while maintaining sufficient sampling to prevent aliasing and ensure good reconstruction quality.
Data Source
AI summary
A method for designing a seismic survey including (a) selecting a seismic survey grid as a basis for a seismic survey design, (b) generating off-the-grid locations by imposing spatial/temporal constraints on on-the-grid locations of the seismic survey grid, (c) mapping the off-the-grid locations from a physical domain to a pre-selected domain by applying a multidimensional transform to the off-the-grid locations, (d) mapping the pre-selected domain to a rank-revealing domain using a pre-selected operator, (e) applying a pre-selected process to minimize a rank of the off-the-grid locations in the pre-selected domain, (f) updating the seismic survey design based on which of the off-the-grid locations has the minimum rank, (g) repeating steps (b)-(f) for a number of iterations until a pre-selected threshold is met indicating an optimal seismic survey design, and (h) acquiring seismic data using the optimal seismic survey design.