Seismic Multiple Prediction With Sparse Trace Sampling Apertures
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Solution Overview
Problem
Existing seismic data processing techniques struggle to form reliable and accurate images of the subsurface due to inefficiencies in identifying, isolating, and processing seismic signals, particularly in constructing free surface multiple models, which is computationally intensive and often results in migration artifacts.
Innovation Solution
The method involves sparse sampling seismic data to create a broad area map with downward reflection points (DRPs) on a grid, convolving trace pairs, calculating root mean square (RMS) and semblance attributes, determining asymmetric apertures, and performing 3D SRME seismic processing to enhance data processing efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional seismic data processing techniques are used to construct free surface multiple models, then comprehensive coverage of all seismic traces is achieved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent divides the seismic data volume into multiple subsets and processes only representative traces from each subset. This segmentation approach maintains imaging accuracy by ensuring adequate sampling while dramatically reducing computational load and processing time compared to processing all traces.
Solution Approach 2:
The patent applies partial action by processing a sparse subset of representative traces rather than the complete data volume. This partial processing is sufficient to determine optimal parameters and construct accurate multiple models, avoiding the excessive computational effort of processing all traces.
2Measurement precision
If dense sampling of all seismic traces is performed, then processing accuracy is improved, but computational resources and processing time are excessively consumed
Solution Approach 1:
The patent segments the complete seismic data volume into multiple subsets and selects representative traces from each subset. This approach maintains measurement precision for parameter determination while significantly improving processing efficiency by avoiding dense sampling of all traces.
Solution Approach 2:
The patent changes the sampling parameter from dense (all traces) to sparse (representative subset). This parameter change in sampling density maintains sufficient precision for determining processing parameters while dramatically improving computational efficiency.
3Ease of manufacture
If symmetric apertures are used in seismic processing, then simplicity of implementation is maintained, but accuracy in complex subsurface structures deteriorates
Solution Approach 1:
The patent determines asymmetric apertures tailored to the specific geological conditions and trace characteristics. This asymmetric approach improves the accuracy of multiple elimination in complex subsurface structures while maintaining reasonable implementation complexity through automated determination methods.
Solution Approach 2:
The patent applies local quality by determining aperture parameters specific to each representative trace based on local geological conditions. This localized optimization of aperture symmetry/asymmetry improves multiple elimination accuracy for different trace types while maintaining overall implementation feasibility.
Data Source
AI summary
A method includes receiving a seismic data volume including target traces. The method also includes sparse sampling the target traces to produce a subset of representative target traces. The method also includes generating a broad area map for each representative target trace. The area map includes multiple downward reflection points (DRPs) laid out as a grid and multiple blocks. The method also includes convolving a seismic trace pair for each DRP to produce a convolved trace. The method also includes calculating a contribution weight based on a root mean square (RMS) and a semblance attribute for each block at each time window. The method also includes summing the contribution weight for each block. The method also includes selecting a set of blocks that have summed contribution weight above a threshold value. The method also includes determining one or more apertures that encompass the set of blocks.