부산대학교 도서관

In the oil and gas exploration, delineating the tectonic structure of geological targets and predicting physical properties are paramount. As we all know, seismic velocity modeling is crucial for characterizing reservoirs. Enhancing seismic inversion accuracy to obtain quantitative parameters is vital in oil reservoir exploration. Current seismic waveform inversion utilizes the amplitude and timing of seismic records to reconstruct the velocity model, thereby refining the model for seismic migration. This article addresses the convergence and computational challenges in conventional inversion by proposing an adaptive iteration method for step updates. This method outperforms inexact linear searches and empirical formulas by updating the model effectively without additional seismic simulations. In the past, gradient optimization typically requires three seismic modeling in a single iteration, whereas adaptive iteration only requires two, offering practical cost savings. To address stability issues in complex scenarios, we combine parabolic search with adaptive iteration, which is the modified optimization technique, to enhance the convergence rate of objective function. Furthermore, by employing gradient preconditioning based on illumination and multigrid strategy, we achieve full strata velocity modeling from shallow to deep layers. Numerical tests on the theoretical anomaly model and BP model validate the effectiveness and precision of the adaptive iteration, significantly reducing the computational load of seismic inversion and accurately estimating deep strata information.