Optimizing Seismic Source Receiver Locations via Mutual Coherence
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Solution Overview
Problem
Existing methods for determining optimal source and receiver locations in seismic data acquisition using compressive sensing often oversimplify optimization with a single mutual coherence value, leading to less accurate seismic data reconstruction, especially in complex surveys.
Innovation Solution
The method optimizes source and receiver locations by using a minimized multidimensional mutual coherence map, which includes mutual coherence values at each (x,y) location, to determine optimal positions from available locations in uniformly spaced target survey grids, allowing for non-uniform sampling and improved seismic data reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single minimized mutual coherence value is used to evaluate the level of incoherence, then the optimization process is simplified, but the seismic data reconstruction accuracy deteriorates
Solution Approach 1:
The patent divides the single mutual coherence evaluation into multiple directional components (azimuthal and radial directions). Instead of using one overall coherence value, the method calculates separate coherence values for different directional orientations, allowing the optimization to account for anisotropic sampling patterns while maintaining computational tractability.
Solution Approach 2:
The patent transitions from a single scalar mutual coherence value to a multidimensional evaluation by introducing directional dimensions. The coherence is evaluated across multiple azimuthal angles and radial directions, effectively adding angular dimensions to the previously one-dimensional coherence metric, thereby capturing the directional nature of seismic sampling patterns.
2Reliability
If misaligned sources and receivers are used to maintain sparse representation, then the sparsity is improved, but the transform domain is limited and local interpolation is required
Solution Approach 1:
The patent optimizes the geometric parameters of source and receiver locations systematically rather than using fixed misaligned patterns. By adjusting the spacing, offset, and angular relationships between sources and receivers based on mutual coherence minimization, the method achieves sparse representation while maintaining compatibility with standard transform domains and avoiding the need for local interpolation.
3Quantity of substance
If the number of sources and receivers is reduced using compressive sensing, then the acquisition cost is reduced, but the image resolution may deteriorate
Solution Approach 1:
The patent applies different sampling densities and geometric configurations to different spatial locations based on the local mutual coherence values. In regions where the coherence is higher, the optimization introduces more diverse source-receiver pairs with varying azimuths and offsets, thereby maintaining resolution locally even when the overall number of sources and receivers is reduced.
Data Source
AI summary
Source and receiver locations are optimized for acquiring seismic data used in compressive sensing reconstruction. A minimized multidimensional mutual coherence map, which includes a mutual coherence value at each (x,y) location in the mutual coherence map, is used to determine the optimal source and receiver locations from available source and receiver locations in respective, uniformly spaced, target survey grids.