New convolutional differentiator method and 2.5-D seismic waves modeling

For improving the precision and efficiency of seismic modeling, one aim of this paper is to develop a generalized and high precision forward method. The authors introduce convolutional Forsyte polynomial differentiator to calculate the spatial derivative of seismic wave equation, and apply the time staggered grid finite-difference which can better meet the high precision of the convolutional differentiator to substitute the conventional finite-difference to calculate the time derivative of seismic wave equation, then creating a new forward method to modeling the wave-field in complex inhomogeneous media. Another aim of this paper is to study 2.5 dimension (2.5-D) seismic wave-field. The authors apply CFPD method to simulate the seismic wave-field in 2.5-D inhomogeneous media. The results indicate that 2.5-D numerical modeling is efficient to simulate one of the sections of 3D media, and 2.5-D calculation is much less time-consuming than 3D calculation. The theory and computation example of 2.5-D seismic wave field mentioned in this paper prove that the new algorithm can efficiently calculate the seismic wave-field in complex media. Proposing and developing this new forward method can provide more choices to study the seismic wave-field modeling, seismic wave migration, seismic inversion, and seismic wave imaging.