Irregular Seismic Data Acquisition via Forward Modeling Optimization
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Solution Overview
Problem
Existing irregular seismic data collection methods do not fully consider actual underground structure information, leading to incomplete data collection and spatial aliasing issues, which affect the accuracy of seismic data processing and interpretation.
Innovation Solution
An irregular seismic data collection method based on forward modeling is developed, where a high-precision geological model is built by fully collecting geological information, and an optimized irregular sparse observation system is designed using a greedy strategy to arrange sampling points, minimizing spatial aliasing and maximizing data collection with fewer sampling points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-density seismic data collection is implemented, then data collection completeness and processing accuracy are improved, but collection cost and collection time increase significantly
Solution Approach 1:
The patent performs preliminary actions by building a high-precision geological model before data collection, and pre-optimizing the observation system design using forward modeling. This allows the actual field collection to be more efficient and targeted, reducing the time needed while maintaining data quality.
Solution Approach 2:
The patent changes the sampling parameters from regular high-density to irregular sparse sampling. By optimizing the spatial distribution of sampling points based on geological model information, the system achieves complete data collection with fewer samples, reducing collection time while maintaining measurement precision.
2Measurement precision
If regular high-density seismic data collection is implemented, then data completeness is improved, but collection cost increases significantly
Solution Approach 1:
The patent applies local quality by tailoring the sampling density and distribution to local geological characteristics. Areas with complex subsurface structures receive more sampling density, while simpler areas receive less, optimizing the balance between data completeness and collection cost.
Solution Approach 2:
The system changes from uniform high-density sampling to optimized sparse sampling. By adjusting sampling parameters based on geological model predictions, the patent reduces the total number of samples needed while maintaining data completeness, thereby reducing collection cost.
3Quantity of substance
If irregular sparse observation system is designed without considering underground structure information, then collection cost is reduced, but spatial aliasing occurs and data processing accuracy deteriorates
Solution Approach 1:
The patent performs preliminary forward modeling using the geological model to predict the optimal sampling configuration before actual data collection. This preliminary action ensures that the sparse observation system is designed to avoid spatial aliasing while maintaining low cost.
Solution Approach 2:
The system uses feedback from forward modeling results to optimize the sampling design. By iteratively adjusting the sampling configuration based on modeling predictions, the patent achieves a balance between collection cost and data processing accuracy, preventing spatial aliasing.
4Measurement precision
If optimized irregular sparse observation system is designed using forward modeling, then spatial aliasing is suppressed and data reconstruction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the complex optimization problem into smaller manageable parts. The forward modeling is performed for individual sampling points or small groups, and the geological model is divided into relevant parameters. This segmentation reduces the computational complexity while maintaining reconstruction accuracy.
Solution Approach 2:
The system applies computational resources locally based on geological complexity. Areas with complex subsurface structures receive more computational attention in the forward modeling, while simpler areas receive less, optimizing the balance between accuracy and computational complexity.
Data Source
AI summary
An irregular seismic data collection method is based on forward modeling. The method includes: fully collecting geological information of a work area to build a geological model, determining arrangement parameters of receiver points and shot points for regular high-density collection and irregular sparse collection, performing irregular optimization design on positions of the shot points based on forward modeling, performing irregular optimization design on positions of the receiver points based on forward modeling, and outputting a preferred irregular sparse observation system. Compared with the existing technologies, an arrangement scheme of shot points and receiver points in the present invention, including the irregular sparse observation system, implements irregular optimization design of the shot points and the receiver points based on a forward modeling technology by using a greedy strategy, so that the designed irregular observation system can suppress to the greatest extent spatial aliasing caused by irregular sparse collection.