Hybrid frequency-domain full-waveform inversion using ray+Born sensitivity kernels

Ramzi Djebbi, Tariq Ali Alkhalifah

Research output: Contribution to journalArticlepeer-review

Abstract

Full-waveform inversion (FWI) using the scattering integral (SI) approach is an explicit formulation of the inversion optimization problem. The inversion procedure is straightforward, and the dependence of the data residuals on the model parameters is clear. However, the biggest limitation associated with this approach is the huge computational cost in conventional exploration seismology applications. Modeling from each of the source and receiver locations is required to compute the update at every iteration, and that is prohibitively expensive, especially for 3D problems. To deal with this issue, we have developed a hybrid implementation of frequency-domain FWI, in which forward modeling is combined with ray tracing to compute the update. We use the sensitivity kernels computed from dynamic ray tracing to build the gradient. The data residual is still computed using finite-difference wavefield modeling. With ray theory, the Green’s function can be approximated using a coarser grid compared to wave-equation modeling. Therefore, the memory requirements, as well as the computational cost, are reduced significantly. Considering that in transmission FWI long-to-intermediate wavelengths are updated during the early iterations, we obtain accurate inverted models. The inversion scheme captured the anomaly embedded in the homogeneous background medium. For more complex models, the hybrid inversion method helps in improving the initial model with little cost compared with conventional SI inversion approaches. The accuracy of the inversion results shows the effectiveness of the hybrid approach for 3D realistic problems.
Original languageEnglish (US)
Pages (from-to)R339-R347
Number of pages1
JournalGEOPHYSICS
Volume85
Issue number4
DOIs
StatePublished - Jun 5 2020

Fingerprint

Dive into the research topics of 'Hybrid frequency-domain full-waveform inversion using ray+Born sensitivity kernels'. Together they form a unique fingerprint.

Cite this