Decision Algorithm for Surface Wave Dispersion Curve Calculation
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Solution Overview
Problem
Seismic data acquisition systems face challenges in accurately separating and analyzing surface waves, which carry significant energy and are dispersive, making it difficult to obtain accurate subsurface images and characterize elastic properties of the near-surface region.
Innovation Solution
A method and device for calculating surface wave dispersion curves by receiving seismic data, selecting region units, processing traces to obtain candidate measurements, training a decision algorithm on confirmed measurements, and applying it to ambiguous measurements to disambiguate propagation velocities as a function of frequency, thereby generating final dispersion curves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If surface waves are separated and analyzed from seismic data, then near-surface elastic properties can be characterized, but the complexity of separating and analyzing dispersive surface waves increases significantly
Solution Approach 1:
The patent introduces an intermediary decision algorithm that acts as a mediator between the complex surface wave separation process and the final dispersion curve extraction. This algorithm automatically selects the most appropriate surface wave separation technique based on the characteristics of the seismic data, thereby reducing the overall system complexity while maintaining measurement precision for near-surface elastic properties characterization.
Solution Approach 2:
The patent employs parameter changes by adjusting the dispersion curve extraction parameters and surface wave separation parameters dynamically based on the seismic data characteristics. This allows the system to adapt to different near-surface conditions without requiring a completely different analysis approach, thus managing complexity while achieving precise elastic property characterization.
2Productivity
If automated decision algorithms are used to select dispersion curves, then productivity increases, but the complexity of the processing system increases
Solution Approach 1:
The patent applies preliminary action by pre-training the decision algorithm with a comprehensive database of surface wave separation results and dispersion curves before actual processing. This pre-computed knowledge base allows the algorithm to make rapid, accurate decisions during processing without requiring complex real-time calculations, thereby increasing productivity while keeping the processing system relatively simple.
Solution Approach 2:
The patent uses copying by creating a simplified model or representation of the complex surface wave phenomena through the decision algorithm. Instead of directly processing the full complexity of surface wave equations in real-time, the system uses pre-computed patterns and relationships that replicate the essential behavior, enabling automated selection without proportionally increasing system complexity.
3Measurement precision
If multiple candidate measurements are generated for each region unit, then measurement precision improves, but the time required to process and select final dispersion curves increases
Solution Approach 1:
The patent applies partial action by generating multiple candidate measurements for each region unit but then using the decision algorithm to select only the most relevant subset for final dispersion curve construction. This approach maintains measurement precision through multiple candidates while reducing processing time by not exhaustively analyzing all possible combinations, instead focusing on the most promising candidates identified by the algorithm.
Data Source
AI summary
Device and method for calculating a set of surface wave dispersion curves. The method includes receiving seismic data recorded with seismic sensors over an area to be surveyed; selecting region units that cover the area to be surveyed; gathering traces for the region units; processing in a computing device the traces to obtain a set of candidate measurements for each region unit; teaching a decision algorithm based on a first subset of the set of candidate measurements; and calculating the set of surface wave dispersion curves by running the decision algorithm on a second subset of the set of candidate measurements.