Composite Source Selection for Seismic FWI Cross-Talk Reduction
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Solution Overview
Problem
Full Waveform Inversion (FWI) in seismic data processing is hindered by high computational costs due to the need for repeated solutions of the wave equation for each source in the seismic data, especially in iterative updates of the earth model.
Innovation Solution
Combining multiple sources into composite or simultaneous sources, selected randomly with a minimum spatial separation to reduce cross-talk and perform forward and reverse modeling collectively, thereby reducing the number of wave equation solutions required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual sources are modeled separately in sequential source FWI, then measurement precision of the earth model is improved, but computational cost increases significantly
Solution Approach 1:
Multiple individual sources are combined into a single composite source by summing their wavefields. This merging reduces the number of separate forward and reverse modeling operations required, thereby significantly reducing computational cost while maintaining earth model accuracy through the use of multiple composite sources and iterations.
Solution Approach 2:
The FWI process is performed iteratively using multiple composite sources rather than sequentially processing every individual source. This periodic application of composite source modeling achieves convergence to an accurate earth model more efficiently than continuous sequential source processing.
2Use of energy by stationary object
If multiple sources are combined into composite sources, then computational cost is reduced, but cross-talk between sources may contaminate the earth model
Solution Approach 1:
The wavefield from each individual source within the composite source has distinct local characteristics and spatial distribution. By combining sources with sufficiently different local qualities (spatially separated sources), the cross-talk between them is reduced, allowing the composite source to maintain accuracy while reducing computational cost.
Solution Approach 2:
Instead of using every individual source, a subset of sources is selected to create composite sources that copy the essential information content. Multiple such composite sources are then used to achieve complete coverage and accurate earth model estimation without processing every individual source.
3Loss of time
If the number of wave equation solutions is reduced by combining sources, then processing time is decreased, but the complexity of source selection and combination increases
Solution Approach 1:
The complete set of sources is segmented into multiple composite sources, each composed of a subset of individual sources. This segmentation allows the processing to be divided into manageable composite source operations, reducing overall processing time while the systematic method for creating composites manages the complexity.
Solution Approach 2:
The source combination process utilizes parameter changes such as random selection of source subsets and varying the number of composite sources. These parameter variations allow optimization of the balance between processing time and complexity, enabling efficient processing without excessive complexity through systematic parameter management.
Data Source
AI summary
Recorded seismic data are obtained at a plurality of receivers from a plurality of sources, and a set of sources from the plurality of sources is selected using spatial criteria based on a location of each source such that any two sources in the set of sources are separated by a predefined minimum distance of separation sufficient to reduce cross talk between sources. The set of sources is combined in a non-encoded manner into a composite source, and forward modeling for the composite source is performed to generate a synthetic seismic data set. A composite recorded seismic data set for the set of sources is determined, and the synthetic seismic data set and composite recorded seismic data set are used to determine a residual seismic data set. Backward modeling generates a gradient update used to generate an updated earth model.