New weighted coefficients of the average-derivative modeling method based on global optimization algorithms

The average-derivative optimal method (ADM) is widely applied in frequency-domain forward modeling for its high accuracy and simplicity. Since tuning weighted coefficients can suppress the numerical dispersion, it is extremely important to adopt a suitable optimization algorithm to determine the ADM coefficients. To date, most schemes associated with the ADM have adopted the conventional local optimization algorithms, which are sensitive to the initial value and easy to converge on local optimum. The motivation of this paper is to derive new and more accurate ADM coefficients for 2D frequency-domain elastic-wave equation by the global optimization algorithms, which can escape from the local optimum with a certain probability. We adopt simulated annealing (SA) and particle swarm optimization (PSO) algorithms for global optimization and numerical modeling. Compared with the conventional local optimization algorithm, the global optimization algorithms have smaller phase errors, especially for S-wave phase velocity. Numerical examples demonstrate that the global optimization algorithms produce more accurate results than the local optimization algorithm.