Dip-Based Trace Reconstruction for 3D Seismic Multiple Prediction
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Solution Overview
Problem
Current methods for 3D surface-related multiple prediction in marine seismic surveys face challenges due to under-sampling of source and receiver positions, leading to incomplete data sets and inaccuracies in noise attenuation, as they struggle to reconstruct desired traces effectively.
Innovation Solution
A dip-based correction method is employed for data reconstruction, calculating and applying corrections per sample for differences in azimuth, common midpoint coordinates, and offset between the best fitting trace and the desired trace to enhance 3D surface-related multiple prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional interpolation methods (differential NMO or 3D PEF) are used to reconstruct missing traces, then data coverage is improved, but manufacturing precision and reliability of multiple prediction remain insufficient due to under-sampling effects
Solution Approach 1:
The invention changes the parameter space by transforming seismic data into the dip-domain through dip-moveout (DMO) transformation. This parameter transformation allows for more effective interpolation of missing traces by exploiting dip continuity, thereby improving both data coverage and the accuracy of multiple prediction without being constrained by traditional sampling limitations.
Solution Approach 2:
The invention introduces an additional dimension by transforming the data from the conventional offset-CMP domain to the dip-domain. This dimensional transformation enables better reconstruction of missing traces by utilizing dip information as an additional constraint, improving the reliability of multiple prediction in under-sampled scenarios.
2Productivity
If marine seismic data acquisition using towed streamers is used, then ease of operation and productivity are improved, but measurement precision deteriorates due to under-sampling of source and receiver positions
Solution Approach 1:
The invention transforms the data into the dip-domain where missing traces can be more effectively reconstructed. By changing from the conventional time-offset parameterization to dip-angle parameterization, the method compensates for the under-sampling effects inherent in towed streamer acquisition, thereby recovering measurement precision without sacrificing productivity.
Solution Approach 2:
The invention replaces the mechanical positioning system (towed streamers with physical sensors) with a computational approach (dip-based interpolation and reconstruction). This substitution allows the system to achieve high measurement precision through signal processing rather than relying solely on precise physical positioning of sources and receivers.
3Manufacturing precision
If dip-based corrections are applied per sample for azimuth, CMP coordinates, and offset differences, then manufacturing precision of trace reconstruction is improved, but device complexity and computational requirements increase
Solution Approach 1:
The invention performs preliminary dip estimation and correction calculation before the main multiple prediction process. By pre-calculating the dip-based corrections for azimuth, CMP coordinates, and offset differences, the method reduces the computational burden during the actual prediction stage, making the complex corrections more manageable and efficient.
Solution Approach 2:
The invention segments the correction process into distinct components: azimuth correction, CMP coordinate correction, and offset correction. Each component is calculated and applied separately, which simplifies the overall complex correction process and allows for more efficient computational implementation while maintaining high reconstruction accuracy.
Data Source
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AI summary
A best fitting trace in seismic data is determined for a desired trace to be reconstructed. A dip-based correction is calculated per trace and per sample for differences in azimuth, common midpoint coordinates, and offset between the best fitting trace and the desired trace. The dip-based correction is applied to the best fitting trace to reconstruct the desired trace for 3D surface-related multiple prediction.