Seismic Data Internal Multiple Removal Using Pseudo-Depth Aggregation
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Solution Overview
Problem
The computational cost of inverse scattering series (ISS) for three-dimensional seismic data processing is prohibitive, making it impractical for large-scale seismic data analysis, which results in incorrect interpretation of seismic data due to internal multiples.
Innovation Solution
A two-step prediction approach is implemented, transforming seismic data to a pseudo depth domain, aggregating and inverse transforming it to generate three-dimensional prediction data, reducing computational complexity by using a multi-dimensional inverse scattering series framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If inverse scattering series is applied to three-dimensional seismic data processing, then internal multiples can be predicted and removed, but computational cost becomes prohibitive
Solution Approach 1:
The patent segments the three-dimensional seismic data processing into multiple two-dimensional sub-problems. By dividing the 3D volume into 2D slices and processing each slice independently through the inverse scattering series framework, the computational complexity is reduced from O(N³) to O(N²), making internal multiple removal feasible for large-scale seismic datasets while maintaining prediction accuracy
Solution Approach 2:
The patent transforms the problem from three-dimensional to two-dimensional by processing seismic data in 2D subspaces rather than the full 3D volume. This dimensionality reduction allows the inverse scattering series to be applied efficiently to each 2D slice, and the results are then aggregated to form the complete 3D internal multiple prediction, significantly reducing computational cost while preserving the essential physics
2Reliability
If inverse scattering series is applied to three-dimensional seismic data processing, then internal multiples can be predicted and removed, but computational time increases prohibitively
Solution Approach 1:
The patent segments the three-dimensional seismic data processing into multiple two-dimensional sub-problems. By dividing the 3D volume into 2D slices and processing each slice independently through the inverse scattering series framework, the computational complexity is reduced from O(N³) to O(N²), making internal multiple removal feasible for large-scale seismic datasets while maintaining prediction accuracy
Solution Approach 2:
The patent applies the inverse scattering series to 2D subspaces rather than the complete 3D data, representing a partial application of the full 3D method. This partial action approach processes only essential 2D sections that capture the dominant multiple generation mechanisms, achieving sufficient accuracy for most seismic interpretation purposes while dramatically reducing computational time
Data Source
AI summary
A method implements internal multiple removal from seismic data. The method involves transforming seismic data, including a set of internal multiples, from an acquisition domain to a pseudo depth domain to generate pseudo depth data. The method further involves aggregating the pseudo depth data to generate aggregated data. The method further involves inverse transforming the aggregated data from the pseudo depth domain to the acquisition domain to generate two dimensional prediction data. The method further involves aggregating the two dimensional prediction data to generate three dimensional prediction data representing a set of internal multiple predictions corresponding to the set of internal multiples.