Seismic Multiple Attenuation via Convolved Pegleg Beam Prediction
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Solution Overview
Problem
Current seismic data processing methods struggle to accurately predict and remove multiples, especially in complex 3D geometries and deep-water environments, leading to interference with primary reflections and contamination of structural imaging.
Innovation Solution
A method combining model-driven and data-driven methodologies to predict multiples by initializing an earth model, selecting a beam dataset, determining a stationary pegleg, and convolving the primary beam with a modeled pegleg beam to generate a convolved multiples beam, which is then used for accurate subtraction of multiples from seismic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional single-method multiple attenuation techniques are used, then the processing is simpler, but the accuracy of multiple prediction and removal is insufficient
Solution Approach 1:
The patent combines model-driven methodology (using earth model and ray tracing to predict stationary multiples) with data-driven methodology (using seismic data to predict remaining multiples through convolution) into a unified hybrid approach. This integration allows the system to leverage the strengths of both methods: the physical accuracy of model-driven prediction and the adaptability of data-driven prediction, thereby improving overall multiple attenuation accuracy while managing complexity through systematic integration.
2Measurement precision
If model-driven methodology is used alone, then the processing is faster, but it fails to accurately predict remaining multiples in complex geometries
Solution Approach 1:
The patent segments the multiple prediction task into two distinct components: stationary multiples predicted by model-driven methodology using earth model and ray tracing, and remaining multiples predicted by data-driven methodology using seismic data convolution. This segmentation allows each method to focus on its strengths while the results are integrated to provide comprehensive multiple coverage, improving adaptability to complex geometries without sacrificing processing efficiency.
3Adaptability or versatility
If data-driven methodology is used alone, then the adaptability to complex geometries is better, but the processing is more computationally intensive
Solution Approach 1:
The patent applies preliminary action by using model-driven methodology to predict and remove stationary multiples first, based on the earth model and ray tracing. This preliminary removal reduces the complexity and computational load for the subsequent data-driven methodology, which then only needs to handle the remaining multiples. This sequential approach significantly reduces computational resources required while maintaining adaptability to complex geometries.
4Object-affected harmful factors
If multiples are not adequately removed, then the primary reflections remain clear, but multiples interfere with and contaminate the seismic data
Solution Approach 1:
The patent converts the harmful effect of multiples into a beneficial prediction process by using the same seismic data that contains multiples to train and refine the multiple prediction models. The data-driven methodology learns the multiple patterns from the actual seismic data, and this learned information is then used to accurately predict and remove multiples, thereby improving the clarity of primary reflections while utilizing the previously harmful multiple energy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides a more accurate prediction and removal of multiples, improving the clarity of primary reflections and reducing noise in seismic data, particularly in challenging 3D and deep-water settings.
Implementation Method 1
multiples can be predicted by cross-convolving the relevant primaries thought to contain the stationary contributions for multiples
Implementation Method 2
A stationary pegleg is determined utilizing the input beam, the multiple generating surface and the time gate
Implementation Method 3
The convolved multiples beam is compared with the input beam to remove the multiples in the input beam by matched filtering
Implementation Method 4
The convolved multiples beam is compared with the input beam to remove the multiples in the input beam by matched filtering
Data Source
AI summary
The present invention incorporates the use of model-driven and data-driven methodologies to attenuate multiples in seismic data utilizing a prediction model which includes multiply-reflected, surface-related seismic waves. The present invention includes beam techniques and convolving a predicted multiples beam with a segment of a modeled pegleg beam to obtain a convolved multiples beam. The convolved multiples beam can then he deconvolved to attenuate the multiples that are present in the original input beam.