No abstract

Internal particle velocity history measurements are performed on [100] LiF single crystals under plate impact to 2–152 GPa, to investigate the Gruneisen equation of state and shock-induced melting. Hugoniot and sound velocities of LiF are obtained via the Lagrangian analysis. The drop in the longitudinal sound velocity to bulk sound velocity between 134 and 152 GPa, suggests that shock-induced melting initiates at 134–152 GPa. The Gruneisen parameter as a function of shock-state density is determined. Given high-pressure Gruneisen parameters, shock temperature and melting curve of B1 phase LiF are calculated, and consistent with previous molecular dynamics and ab initio calculations, as well as diamond-anvil cell and shock wave measurements. Our calculation suggests that shock-induced melting initiates at 142 GPa, in agreement with our sound velocity measurements.

No abstract

Least-squares reverse-time migration (LSRTM) is an iterative inversion algorithm for estimating the broadband-wavenumber reflectivity model. Although it produces superior results compared to conventional reverse-time migration (RTM), LSRTM is computationally expensive. Here, we introduce a one-step LSRTM method by considering the demigrated and observed data to design a deblurring preconditioner in the data domain using the Wiener filter. For the Wiener filtering, we further employ a stabilized division algorithm via Taylor expansion. The preconditioned observed data is then remigrated to obtain a deblurred image. The total cost of this method is about two RTMs. Through synthetic and real data experiments, we see that the one-step LSRTM is able to enhance image resolution and balance source illumination at low computational costs.

Summary Full Waveform Inversion (FWI) has become an essential technique for mapping geophysical subsurface structures. However, proper uncertainty quantification is often lacking in current applications. In theory, uncertainty quantification is related to the inverse Hessian (or the posterior covariance matrix). Even for common geophysical inverse problems its calculation is beyond the computational and storage capacities of the largest high-performance computing systems. In this study, we amend the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm to perform uncertainty quantification for large-scale applications. For seismic inverse problems, the limited-memory BFGS (L-BFGS) method prevails as the most efficient quasi-Newton method. We aim to augment it further to obtain an approximate inverse Hessian for uncertainty quantification in FWI. To facilitate retrieval of the inverse Hessian, we combine BFGS (essentially a full-history L-BFGS) with randomized singular value decomposition to determine a low-rank approximation of the inverse Hessian. Setting the rank number equal to the number of iterations makes this solution efficient and memory-affordable even for large-scale problems. Furthermore, based on the Gauss-Newton method, we formulate different initial, diagonal Hessian matrices as preconditioners for the inverse scheme and compare their performances in elastic FWI applications. We highlight our approach with the elastic Marmousi benchmark model, demonstrating the applicability of preconditioned BFGS for large-scale FWI and uncertainty quantification.