Seismic Survey Layout Using Rank-Minimized Off-Grid Sampling

The patent replaces manual mechanical survey design processes with an automated computer system that uses mathematical optimization. The system automatically generates optimal source and receiver locations by solving complex mathematical problems involving rank minimization and simulated annealing algorithms, eliminating manual intervention while handling the increased computational complexity.

The patent transforms the survey design from fixed periodic grid parameters to optimized off-the-grid locations. By changing the fundamental parameter from grid-based positioning to rank-minimized spatial distribution, the system achieves superior data collection efficiency while the computational complexity is managed through algorithmic optimization rather than manual adjustment.

The optimization system is self-service in nature, automatically generating optimal survey designs without requiring expert manual intervention. The simulated annealing algorithm and rank minimization process autonomously explore the solution space, evaluate data quality metrics, and converge on optimal configurations, handling the complexity internally while delivering high-quality results.

The system incorporates feedback mechanisms where the rank of the survey design matrix is continuously evaluated during the optimization process. The simulated annealing algorithm uses acceptance criteria based on rank changes to guide the search for optimal solutions, and the process iterates until convergence, ensuring high data quality while managing complexity through systematic feedback-driven optimization.

The system performs preliminary actions by pre-defining the optimization criteria, constraints, and evaluation metrics before initiating the rank minimization process. The simulated annealing algorithm uses pre-established acceptance criteria and temperature scheduling to efficiently navigate the solution space, and the entire optimization process is structured to converge on optimal solutions in a controlled manner, managing computational time through proactive planning.

The optimization process applies partial action by iteratively improving the survey design through a controlled number of iterations driven by simulated annealing temperature decay. The process stops when convergence criteria are met or a maximum iteration count is reached, avoiding excessive computational effort while achieving sufficient optimization. This balanced approach ensures high survey design quality without unnecessary computational expenditure.