Time-domain full waveform inversion using the gradient preconditioning based on transmitted waves energy

Abstract

SummaryThe gradient preconditioning approach based on seismic wave energy can effectively avoid the huge storage consumption in the gradient preconditioning algorithms based on Hessian matrices in time-domain full waveform inversion (FWI), but the accuracy is affected by the energy of reflected waves when strong reflectors are present in velocity model. To address this problem, we propose a gradient preconditioning method, which scales the gradient based on the energy of the "approximated transmitted wavefield" simulated by the nonreflecting acoustic wave equation. The method does not require computing or storing the Hessian matrix or its inverse. Furthermore, it can effectively eliminate the effects caused by geometric diffusion and non-uniformity illumination on gradient. The results of model experiments confirm that the time-domain FWI using the gradient preconditioning based on transmitted waves energy can achieve higher inversion precision for high-velocity body and the deep strata below when compared with using the gradient preconditioning based on seismic waves energy.

Source-independent elastic envelope inversion using the convolution method

“…Since FWI can make full use of the kinematic and dynamic information of seismic waves to estimate the propagation speed of seismic waves and theoretically can obtain a higher resolution velocity model, the idea of FWI has received extensive research interest since it was proposed and research progress has been made in the objective function optimization (Datta et al, 2016;Zhu et al, 2016), multi-scale inversion (Bunks et al, 1995;Boonyasiriwat et al, 2009;Xu et al, 2014), mixed domain inversion (Kim et al, 2013;Jun et al, 2014;Xu et al, 2014), envelope inversion (Wu et al, 2013;Wu et al, 2014;Ao et al, 2015), source wavelet inversion (Tarantola, 1984;Song et al, 1995;Hu et al, 2017), and inversion efficiency improvement (Krebs et al, 2009;Wang et al, 2011) of FWI. Currently, FWI has been widely used in field seismic data imaging and many application examples have significantly improved the imaging quality of seismic data (Sirgue et al, 2010;Lewis et al, 2014;Liu et al, 2014;Zhong et al, 2017). Tarantola (1986) and Pratt (1990) extended the compressional wave FWI to EFWI.…”