Marine Seismic Deghosting With Adaptive Operators
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Solution Overview
Problem
Current seismic data processing methods face challenges in effectively deghosting marine seismic data, especially when the ghost model is uncertain, leading to difficulties in removing ghost signals that cause interference and artifacts in seismic images.
Innovation Solution
The method involves estimating the ghost model and upward-going wavefield simultaneously using adaptive operators, with a simple model to generate adaptive filters, and solving an optimization problem with a weighted sum of Lp and Lq norms to derive desired wavefield characterizations, allowing for effective deghosting even in uncertain conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional ghost removal methods are used, then processing is simpler, but deghosting effectiveness deteriorates when ghost model is uncertain
Solution Approach 1:
The system uses the seismic data itself to adaptively estimate the ghost model and upward-going wavefield through optimization, rather than relying on external or predetermined models. The data-driven approach allows the processing to self-adjust to uncertain conditions while maintaining effectiveness.
Solution Approach 2:
The method transforms the deghosting problem into an optimization problem where parameters (ghost model coefficients and wavefield coefficients) are adjusted to minimize an objective function. This parameter optimization approach enables adaptive deghosting that maintains reliability under uncertain conditions.
2Reliability
If adaptive operators are used to handle uncertainty, then deghosting robustness improves, but computational complexity increases
Solution Approach 1:
The optimization focuses on estimating only the necessary components (ghost model and upward-going wavefield) rather than fully reconstructing the entire wavefield. This partial action approach achieves robust deghosting while reducing computational requirements.
Solution Approach 2:
By formulating the problem as parameter optimization with a carefully designed objective function, the method achieves adaptive robustness without requiring excessive computational resources. The parameter-based approach allows efficient numerical optimization.
3Productivity
If simple models are used to generate adaptive filters, then computational efficiency improves, but measurement precision of ghost delay estimation deteriorates
Solution Approach 1:
The method uses a simple parametric model for the ghost operator but estimates its parameters adaptively through optimization. This allows the use of computationally efficient simple models while achieving precise ghost delay estimation through data-driven parameter adjustment.
Solution Approach 2:
The optimization process provides feedback by iteratively adjusting the model parameters to minimize the difference between modeled and observed data. This feedback mechanism allows simple models to achieve high estimation precision through adaptive parameter tuning.
Data Source
AI summary
Methods and apparatuses for processing marine seismic data with a process of combined deghosting and sparse τ-p transformation. The process is formulated as an optimization problem. The optimization problem has an objective function that is a weighted sum of two norms: one norm is an Lp norm of the differences between the modeled data and acquired survey wherein the modeled data are derived from a model and a set of adaptive filters; the other norm is an Lq norm of the model; and the optimization variables and solutions are the coefficients of the model and coefficients of the adaptive filters.