Hybrid Full Waveform Inversion for Geophysical Data Processing
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Solution Overview
Problem
Current geophysical inversion methods, particularly iterative inversion, are computationally expensive due to the need for numerous simulations, making them impractical for many applications, and simultaneous source inversion methods often result in reduced accuracy due to information loss during data combination.
Innovation Solution
A hybrid method combining simultaneous encoded source inversion for shallow time windows with sparse sequential source inversion for deeper time windows, using encoding techniques to preserve information and reduce computational costs, while maintaining accuracy by updating models iteratively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If iterative inversion is used to improve model accuracy, then manufacturing precision is improved, but productivity deteriorates due to computational expense
Solution Approach 1:
The patent segments the inversion process into two distinct stages: a fast non-iterative inversion stage that processes all sources simultaneously to obtain an initial model, followed by a focused iterative inversion stage that processes only a sparse subset of sources to refine the model. This segmentation allows the computationally expensive iterative process to be applied selectively rather than to all sources, thereby maintaining model accuracy while significantly improving computational efficiency.
Solution Approach 2:
The patent applies partial action by performing iterative inversion on only a sparse subset of sources rather than all sources. The initial model obtained from simultaneous non-iterative inversion of all sources provides a sufficient foundation, and iterative refinement is applied partially to key sources that most benefit from the enhanced accuracy, thus avoiding the excessive computational cost of applying iterative methods universally.
2Productivity
If simultaneous source inversion is used to improve productivity, then computational expense is reduced, but manufacturing precision deteriorates due to information loss
Solution Approach 1:
The patent segments the source set into two groups: all sources are processed simultaneously in the first non-iterative stage to gain computational efficiency, while a sparse subset of sources is processed iteratively in the second stage to recover and maintain accuracy. This segmentation allows the system to leverage the speed of simultaneous processing while compensating for information loss through selective iterative refinement.
Solution Approach 2:
The patent performs preliminary non-iterative inversion of all sources simultaneously to obtain an initial model before applying iterative refinement. This preliminary action provides a computationally efficient foundation that captures the bulk of the information, allowing subsequent iterative processing to focus only on refining specific aspects rather than processing all sources from scratch.
3Manufacturing precision
If iterative inversion is applied to all sources to maintain accuracy, then manufacturing precision is maintained, but loss of time increases due to computational burden
Solution Approach 1:
The patent segments the inversion workflow into two time-efficient stages: a rapid simultaneous non-iterative inversion stage that processes all sources quickly to establish an initial model, followed by a targeted iterative inversion stage that processes only a sparse subset of sources to achieve final accuracy. This segmentation dramatically reduces total computational time compared to applying iterative methods to all sources while maintaining the necessary model accuracy.
Solution Approach 2:
The patent applies iterative inversion partially to only the most critical sources rather than all sources. The simultaneous non-iterative processing of all sources provides sufficient initial accuracy, and iterative refinement is applied selectively to a sparse subset of sources that require enhanced precision, thereby minimizing time loss while maintaining overall model accuracy.
Data Source
AI summary
Method for simultaneous full-wavefield inversion of gathers of source (or receiver) encoded geophysical data to determine a physical properties model for a subsurface region, especially suitable for surveys where fixed receiver geometry conditions were not satisfied in the data acquisition. First, a shallow time window of the data (202) where the fixed receiver condition is satisfied is inverted by simultaneous encoded (203) source inversion (205). Then, the deeper time window of the data (208) is inverted by sparse sequential source inversion (209), using the physical properties model from the shallow time window (206) as a starting model (207). Alternatively, the shallow time window model is used to simulate missing far offset data (211) producing a data set satisfying the stationary receiver assumption, after which this data set is source encoded (212) and inverted by simultaneous source inversion (214).