Compressive Radon Transform for Seismic Multiple Diffraction Removal
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Solution Overview
Problem
The removal of multiple diffractions in marine seismic surveys is challenging due to their steeply dipping, heavily aliased tails, making them indistinguishable from other seismic events and difficult to remove effectively.
Innovation Solution
The use of a compressive domain transform, such as the Asymptote and Apex Shifted Hyperbolic Radon Transform (AASHRT) or a dictionary of Green's functions, to separate and predict multiple diffractions in the transformed domain, followed by adaptive subtraction in the time-space domain to remove them from seismic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to remove multiple diffractions, then processing simplicity is maintained, but detection precision and removal effectiveness deteriorate due to steeply dipping, heavily aliased tails making diffractions indistinguishable from other seismic events
Solution Approach 1:
The patent transforms the seismic data from the time-space domain to the Radon domain using a compressive Radon transform. This dimensionality change allows multiple diffractions to be separated from other seismic events in the transformed domain, where they exhibit distinct characteristics (steeply dipping tails) that enable precise detection and removal. After processing in the Radon domain, the data is transformed back to the time-space domain, achieving both improved detection precision and effective removal while managing processing complexity through efficient transform algorithms.
2Reliability
If multiple diffractions are not removed, then data processing time is reduced, but seismic image quality deteriorates due to noise and reduced clarity
Solution Approach 1:
The patent extracts multiple diffractions from the seismic data by transforming to the Radon domain, where they can be identified and separated from primary reflections and other seismic events. The extraction process involves isolating the multiple diffraction components in the Radon domain, then removing them through subtraction or muting operations before transforming back to the time-space domain. This targeted extraction improves seismic image quality by eliminating noise while maintaining reasonable processing time through efficient implementation.
3Reliability
If compressive Radon transform is used to separate and remove multiple diffractions, then seismic image quality is improved, but processing complexity and computational requirements increase
Solution Approach 1:
The patent applies a compressive Radon transform that changes the parameter representation of seismic data from time and space coordinates to Radon domain parameters (apex time, apex position, slope). This parameter transformation enables multiple diffractions to be distinguished from other events based on their unique characteristics in the transformed parameter space. The compressive nature of the transform reduces the dimensionality and computational burden compared to traditional Radon transforms, managing processing complexity while achieving improved seismic image quality.
Data Source
AI summary
Prediction and subtraction of multiple diffractions may include transforming previously acquired seismic data from a time-space domain to a transformed domain using a dictionary of compressive basis functions and separating, within the transformed previously acquired seismic data, a first portion and a second portion of the transformed previously acquired seismic data. Prediction and subtraction of multiple diffractions may also include predicting a plurality of multiple diffractions based on the separated first and second portions and adaptively subtracting the predicted multiple diffractions from the previously acquired seismic data. Prediction and subtraction of multiple diffractions may also include inverse transforming a particular seismic data set from the transformed domain to the time-space domain.