Model-Based Deblending for Seismic Cross-Talk Noise Attenuation
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Solution Overview
Problem
Current seismic data processing methods struggle to effectively remove shot-to-shot coherent seismic interference noise, particularly in multiple-source acquisition systems, where low-frequency broadband noise is prevalent and deblending algorithms rely on randomness that is not always achievable, leading to challenges in separating energy from different sources and degrading seismic data quality.
Innovation Solution
A method is introduced that generates a cross-talk noise model by reconstructing shot gathers using neighboring data, reducing noise coherency and allowing for effective subtraction of coherent noise, followed by deblending with existing algorithms to recover the seismic signal, which can be integrated with existing deblending schemes to enhance seismic data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple-source acquisition is used to increase data sampling density, then productivity is improved, but seismic interference noise increases and degrades measurement precision
Solution Approach 1:
The patent segments the seismic interference noise into different coherency components (coherent and incoherent) and applies different processing strategies to each. The model-based deblending method specifically targets coherent noise by reconstructing shot gathers and identifying coherent patterns, while incoherent noise is handled by traditional deblending algorithms. This segmentation allows effective noise attenuation while preserving signal quality.
Solution Approach 2:
The patent changes the coherency parameter of the noise by reconstructing shot gathers and replacing original shots with reconstructed ones. This transformation converts coherent noise into incoherent noise, which can then be effectively removed by existing deblending algorithms. The parameter change from coherent to incoherent makes the noise removable without sacrificing the underlying signal.
2Manufacturing precision
If deblending algorithms rely on noise randomness, then manufacturing precision is improved, but reliability deteriorates when randomness is low
Solution Approach 1:
The patent performs preliminary action by reconstructing shot gathers before applying deblending algorithms. This reconstruction step creates artificial randomness in the noise pattern by replacing original shots with synthesized versions. The preliminary reconstruction ensures that subsequent deblending algorithms can effectively separate signal from noise even when the original data lacks sufficient randomness.
Solution Approach 2:
The patent introduces an intermediary step (shot gather reconstruction) between data acquisition and deblending. This intermediary process transforms the noise characteristics and creates the randomness needed for effective deblending. The reconstruction acts as a mediator that bridges the gap between low-randomness acquired data and deblending algorithms that require randomness for optimal performance.
3Loss of information
If coherent noise is present in seismic data, then loss of information increases, but device complexity remains unchanged
Solution Approach 1:
The patent replaces complex mechanical noise filtering systems with a computational approach. Instead of using hardware-based filtering that would increase device complexity, the method uses software-based shot gather reconstruction and coherent noise identification. This substitution reduces device complexity while effectively improving signal-to-noise ratio by removing coherent noise through algorithmic processing.
Data Source
AI summary
Computing device, computer instructions and method for removing cross-talk noise from seismic data and generating an image of a surveyed subsurface. The method includes receiving input seismic data D generated by firing one or more seismic sources so that source energy is overlapping, and the input seismic data D is recorded with seismic sensors over the subsurface; generating a cross-talk noise model N by replacing at least one original shot gather with a reconstructed shot gather; subtracting the cross-talk noise model N from the input seismic data D to attenuate coherent cross-talk noise to obtain processed seismic data Dp; deblending the processed seismic data Dp with a deblending algorithm to attenuate a residual noise to obtain deblended seismic data Dd; and generating the image of the subsurface based on the deblended seismic data Dd.