A new data-domain reflection full-waveform inversion method based on common-image-point gathers

Abstract

The migration velocity analysis (MVA) based on the common-image gathers (CIGs) in the image domain is an effective approach for evaluating the seismic background velocity, a crucial index for the full-waveform inversion (FWI) and migration. Compared with cumbersome conventional MVA methods, differential semblance optimization (DSO) is a variation of image-domain wavefield tomography that avoids interactive manual picking but increases the computation cost tremendously. In recent years, the reflection waveform inversions have been studied in depth to construct the background velocity, matching reflection travel time or waveform in the data domain, which requires expensive Born simulations based on accurate imaging results. This study performs a new reflection full-waveform inversion method in the image domain based on the flatness of arbitrarily selected common-image-point (CIP) gathers and their Born simulations. According to the relationship between imaging and background model, the flatness of CIP gathers under the L1 norm is measured. Afterwards, we construct the objective function to update the background model without manual picking or wave-equation migration in an extended dimension. The inversion accuracy is controlled by balancing the density of the imaging points and the computation capability. The whole procedure is implemented in the frequency domain, so the main computation comes from the LU decomposition in the frequency-domain wave-equation simulation, in which way, the proposed data-domain reflection full-waveform inversion achieves complete automation with lower costs. The objective function analysis shows a better convex, proving the weak dependence on the starting model of this method. Besides, synthetic experiments verify the validity and the field data test demonstrates its effectiveness.