Seismic Imaging Amplitude Correction via Curvelet Matched Filter
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Solution Overview
Problem
Current seismic imaging technologies, such as Reverse Time Migration (RTM) and Least Squares RTM, produce blurred and amplitude-imbalanced subsurface reflectivity models due to the computational intensity and difficulty in obtaining the inverse Hessian operator, which affects the resolution and accuracy of seismic imaging.
Innovation Solution
A method involving RTM, Born forward modeling, curvelet transformation, and a Wiener solution as a matched filter to estimate an inverse Hessian operator, applied to the initial imaging result to enhance resolution and amplitude balance, using curvelet coefficients for pointwise estimation and inverse curvelet transformation to achieve high-resolution amplitude-preserving seismic imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Least Squares RTM is iteratively performed to obtain an optimal imaging section, then imaging quality and amplitude accuracy are improved, but computational cost and time consumption increase significantly
Solution Approach 1:
The patent performs preliminary RTM imaging to obtain an initial imaging result before the least squares iteration. This initial result serves as a starting point that guides subsequent iterative refinement, allowing the algorithm to converge faster to an optimal solution without requiring as many iterative steps, thus reducing overall computational time while maintaining imaging quality.
Solution Approach 2:
The patent introduces an intermediate step of performing RTM on the initial imaging result to generate a second imaging result. This intermediate result acts as a mediator that captures propagation effects and illumination variations, which are then used to construct the Hessian operator. This intermediary approach enables more efficient amplitude correction during the least squares iteration, improving convergence speed and reducing computational burden.
2Manufacturing precision
If the Hessian operator is computed to correct propagation effects, then imaging resolution and amplitude balance are improved, but device complexity and computational burden increase
Solution Approach 1:
The patent uses the second imaging result (obtained by RTM on the initial imaging result) as an intermediary to estimate the Hessian operator. Instead of directly computing the complex Hessian operator from scratch, the method uses the relationship between the initial imaging result and the second imaging result to derive propagation effects. This intermediary approach simplifies the computational process while still capturing the necessary information for amplitude correction and resolution improvement.
Solution Approach 2:
The patent creates a copy of the imaging process by performing RTM on the initial imaging result to generate the second imaging result. This copied process serves as a proxy for understanding propagation effects without requiring direct computation of the full Hessian operator. The second imaging result acts as a simplified representation that captures essential propagation characteristics, reducing computational complexity while maintaining the ability to correct imaging artifacts.
3Productivity
If conventional RTM is used for imaging, then computational efficiency is maintained, but imaging results exhibit blurring and unbalanced amplitude
Solution Approach 1:
The patent performs preliminary RTM imaging to obtain an initial imaging result that captures the basic subsurface structure. This preliminary result is then used as input for subsequent amplitude correction steps. By separating the imaging process into a preliminary efficient RTM step followed by a targeted amplitude correction step, the method maintains computational efficiency while improving imaging quality to eliminate blurring and amplitude imbalance.
Solution Approach 2:
The patent introduces the second imaging result as an intermediary that encodes propagation effects and illumination variations. This intermediary is used to construct an approximate Hessian operator that captures the physical effects causing blurring and amplitude imbalance. By using this intermediary rather than directly applying complex iterative least squares methods, the patent achieves amplitude correction and resolution improvement while maintaining computational efficiency closer to conventional RTM.
Data Source
AI summary
The present disclosure provides a method of high-resolution amplitude-preserving seismic imaging for a subsurface reflectivity model, including: performing reverse time migration (RTM) to obtain an initial imaging result, performing Born forward modeling on the initial imaging result to obtain seismic simulation data, and performing RTM on the seismic simulation data to obtain a second imaging result; performing curvelet transformation on the two imaging results, performing pointwise estimation in a curvelet domain, and using a Wiener solution that matches two curvelet coefficients as a solution of a matched filter; and applying the estimated matched filter to the initial imaging result to obtain a high-resolution amplitude-preserving seismic imaging result.