Dual-Scale Seismic Data Interpolation for Fine-Feature Preservation
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Solution Overview
Problem
Current 5D interpolation methods in seismic data processing struggle to accurately reconstruct fine-scale features while maintaining the integrity of coarse-scale details, often diluting fine-scale energy during migration and failing to efficiently differentiate between signal and noise.
Innovation Solution
A dual-scale interpolation technique is applied, separating seismic data into fine-scale and coarse-scale datasets and using different interpolation methods for each, such as 3D or 5D interpolation, to reconstruct traces and improve pre-stack migration, thereby enhancing the accuracy of seismic imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 5D interpolation is applied to seismic data, then coarse-scale features are reconstructed, but fine-scale features are diluted and lost
Solution Approach 1:
The patent segments the seismic data processing into two distinct components: a low-pass filtered dataset containing coarse-scale features and a high-pass filtered dataset containing fine-scale features. Each component is interpolated separately using 5D interpolation, allowing coarse features to be reconstructed without diluting fine-scale information. This segmentation resolves the contradiction by treating different scale features independently rather than processing the full bandwidth data as a single unit.
2Ease of manufacture
If single-scale interpolation is used, then processing is simple, but both fine and coarse scale features cannot be optimized simultaneously
Solution Approach 1:
The patent divides the interpolation task into two separate processing streams: one for low-pass (coarse-scale) data and one for high-pass (fine-scale) data. Each stream can use standard 5D interpolation techniques independently, maintaining the simplicity and familiarity of existing methods while achieving superior multi-scale results through the combination of separately processed components.
Solution Approach 2:
After separate interpolation of the low-pass and high-pass datasets, the patent merges the interpolated results by summing the two datasets. This combining step integrates the benefits of both processing streams, producing a final result that preserves both coarse-scale structure and fine-scale details, thereby achieving high manufacturing precision without sacrificing processing simplicity.
3Quantity of substance
If fine-scale data is interpolated with coarse-scale data, then signal redundancy is increased, but fine-scale energy is diluted during migration
Solution Approach 1:
The patent applies frequency-domain segmentation using low-pass and high-pass filtering to separate fine-scale and coarse-scale signals before interpolation. This ensures that fine-scale energy is preserved in its own dedicated dataset during the interpolation process, preventing the energy dilution that occurs when fine and coarse scale data are mixed in a single interpolation operation.
Data Source
AI summary
Systems and methods are provided for processing seismic data and displaying an output associated with the seismic data. A method includes: separating the seismic data into a fine-scale dataset and a coarse-scale dataset, wherein each dataset includes a non-zero portion of the data; applying a first interpolation to the coarse-scale dataset which results in an interpolated coarse-scale dataset; applying a second interpolation to the fine-scale dataset which results in an interpolated fine-scale dataset, wherein the first and second interpolation are different interpolations; summing together the interpolated coarse-scale dataset and the interpolated fine-scale dataset which results in a summed interpolated dataset; and displaying at least one image based on the summed interpolated dataset.