Wave-equation traveltime inversion: Comparison of three numerical optimization methods

Compared to traditional traveltime inversion methods, no traveltime picking, high-frequency assumption or ray tracing is necessary in wave-equation traveltime inversion (WT). Another merit of WT is that it is insensitive to the starting model. Although WT offers less detailed parts of velocity model than the full waveform inversion (FWI), it can provide a good initial velocity model for FWI. In this paper, the steepest descent, conjugate gradient and limited-memory BFGS (L-BFGS) optimization methods are used in the implementation of acoustic WT. We use synthetic crosswell data for testing and the numerical results show that L-BFGS has a faster rate of convergence and offers a reconstructed velocity model with better resolution compared to the other two gradient methods.