Wavefield Reconstruction for Seismic Spatial Aliasing
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Solution Overview
Problem
Marine seismic surveys face challenges with spatial aliasing due to insufficient density of wavefield measurements, which affects the accurate reconstruction of seismic data and interpretation of subterranean structures.
Innovation Solution
The method involves wavefield reconstruction techniques that interpolate and extrapolate available wavefield measurements to create new data points, decompose the wavefield into upward and downward propagating constituents, and use spectral analysis and synthesis to overcome spatial aliasing, employing a dictionary of elementary wavefields and Green's functions to reconstruct seismic data on a uniform grid.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wavefield measurements are taken with insufficient density, then measurement cost and complexity are reduced, but spatial aliasing occurs and measurement precision deteriorates
Solution Approach 1:
The patent introduces an intermediary processing system that includes a measurement system with sensors and a processing system with memory and processor. This intermediary system captures raw wavefield measurements, stores them, and processes them through algorithms to reconstruct the wavefield, thereby achieving high measurement precision without requiring overly complex direct measurement hardware.
Solution Approach 2:
The patent performs preliminary actions by capturing and storing raw wavefield measurements before full processing occurs. The measurement system collects data with insufficient density initially, then the processing system later reconstructs the complete wavefield through computational methods, allowing precise reconstruction without requiring complex real-time measurement hardware.
2Loss of information
If wavefield measurements are taken with insufficient density, then measurement cost and complexity are reduced, but spatial aliasing occurs causing loss of information
Solution Approach 1:
The patent changes parameters by transforming the wavefield data from spatial domain to frequency-wavenumber domain through Fourier transforms. This parameter transformation allows the processing system to identify and remove aliased components, then reconstruct the wavefield with correct spectral content, recovering information that would be lost in the original spatial sampling.
Solution Approach 2:
The patent substitutes mechanical measurement density increases with computational processing. Instead of physically placing more sensors to capture complete wavefield information, the system uses algorithms including Fourier transforms, spectral analysis, and iterative reconstruction to computationally recover the full wavefield from sparse measurements, replacing the need for dense physical sampling.
3Reliability
If wavefield measurements are taken with insufficient density, then measurement cost and complexity are reduced, but spatial aliasing disrupts wavefield reconstruction
Solution Approach 1:
The patent implements feedback through an iterative reconstruction process. The processing system initially reconstructs the wavefield from sparse measurements, analyzes the reconstructed data for aliasing artifacts, then uses this analysis to refine the reconstruction in subsequent iterations. This feedback loop continues until the reconstruction converges to a reliable solution that accurately represents the original wavefield.
Solution Approach 2:
The patent introduces an intermediary processing system that acts as a mediator between the sparse measurements and the final reconstructed wavefield. This processing system includes multiple subsystems (Fourier transform, spectral analysis, aliasing removal, reconstruction) that work together to bridge the gap between insufficient measurements and reliable wavefield reconstruction, ensuring accuracy without requiring dense measurement arrays.
Data Source
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AI summary
Wavefield reconstruction may include reconstructing a wavefield at a location away from a seismic receiver based on seismic data sampled from the seismic receiver, a vector of model coefficients comprising a scattering potential, and at least one of a mapping matrix comprising a dictionary of Green's functions and an operator defined by a combination of a number of functions.