Multi-Survey Graph Optimization for Seismic Horizon Extraction
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Solution Overview
Problem
Existing methods for extracting seismic horizons from complex geological environments are time-consuming and computationally intensive, making it difficult to efficiently interpret large numbers of horizons from seismic datasets.
Innovation Solution
A multi-scale optimization approach using global sparse grids and constrained linear optimization to separate horizon interpretation into pre-processing, global optimization on sparse grids, and local optimization, allowing for efficient and automated extraction of seismic horizons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used for extracting seismic horizons from complex geological environments, then interpretation accuracy can be maintained, but the process becomes time-consuming and computationally intensive
Solution Approach 1:
The patent segments the continuous horizon extraction problem into discrete graph-based representations where seismic traces are divided into nodes and connections are represented as edges. This segmentation allows for efficient computational processing while maintaining interpretation accuracy by preserving the topological relationships between seismic features.
Solution Approach 2:
The patent replaces traditional mechanical/numerical optimization methods with graph theory-based algorithms. By substituting the mechanical system of continuous optimization with a discrete graph representation and associated algorithms, the method achieves faster computational performance while maintaining the precision needed for accurate horizon interpretation.
2Measurement precision
If traditional optimization methods are used for horizon interpretation, then accuracy can be maintained, but computational intensity increases
Solution Approach 1:
The patent segments the optimization problem into graph-based discrete units where seismic data is represented as nodes and edges. This segmentation reduces computational intensity by transforming the continuous optimization problem into a discrete graph problem that can be solved more efficiently while maintaining interpretation accuracy.
Solution Approach 2:
The patent substitutes traditional numerical optimization methods with graph theory algorithms. This mechanics substitution replaces computationally intensive continuous optimization with discrete graph-based algorithms that require less computational energy while preserving the precision needed for accurate horizon interpretation.
3Measurement precision
If detailed horizon interpretation is performed on large seismic datasets, then interpretation quality improves, but processing scalability decreases
Solution Approach 1:
The patent segments large seismic datasets into graph-based representations that can be processed independently. This segmentation enables scalable processing of large datasets while maintaining interpretation quality by preserving the essential topological relationships in a computationally efficient format.
Solution Approach 2:
The patent replaces traditional processing methods with graph theory algorithms that scale better with dataset size. This substitution improves processing scalability while maintaining interpretation quality by using discrete mathematical structures that are more efficient for large-scale data processing.
Data Source
AI summary
A method for processing seismic data by a seismic data system. The method comprises acquiring a plurality of first traces each corresponding to a respective first trace location. The method comprises expressing the first traces as first vertices in a first graph in which first edges connect the first vertices, wherein the first edges indicate positioning of the first vertices.