Abstract
An improved Least Squares Reverse Time Migration (LSRTM) method is proposed in the paper, which can effectively improve convergence speed and imaging accuracy. Firstly, the key techniques in the implementation of LSRTM are discussed. Secondly, a condition factor is introduced in the iteration process of conjugate gradient method. Finally, the imaging effect and performance of the algorithm are analyzed. The experiment results indicate that it can speed up the convergence speed and improve the convergence accuracy, so as to improve the imaging effect. Compared with the conventional LSRTM, the data residual of improved LSRTM can be reduced by about 5%.
Highlights
With the deepening of oil and gas exploration, the least squares migration method based on the inversion idea is optimized to find the reflection coefficient model that best matches the observed seismic data, correct the amplitude errors in the migration results, and further improve the solution of seismic imaging, so as to better perform lithologic reservoir imaging and reservoir parameter inversion [1,2,3].The idea of least squares migration inversion was first proposed by Lebras et al [4], which is based on the method of data fitting to obtain the optimal imaging value through successive iterations
Dai et al [7,8] proposed Least Squares Reverse Time Migration (LSRTM) for synchronized source data, which employs a defuzzification filter as a preconditioning operator to speed up the convergence
The energy in deep regions is compensated, and the effect is better than LSRTM, which proves the validity of the improved CG method
Summary
With the deepening of oil and gas exploration, the least squares migration method based on the inversion idea is optimized to find the reflection coefficient model that best matches the observed seismic data, correct the amplitude errors in the migration results, and further improve the solution of seismic imaging, so as to better perform lithologic reservoir imaging and reservoir parameter inversion [1,2,3]. The idea of least squares migration inversion was first proposed by Lebras et al [4], which is based on the method of data fitting to obtain the optimal imaging value through successive iterations. Dai et al [7,8] proposed LSRTM for synchronized source data, which employs a defuzzification filter as a preconditioning operator to speed up the convergence. Wang Liankun et al [14] suppressed migration artifacts by constructing cross-correlation error functionals, thereby improving the fidelity and resolution of imaging results
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