Well-Guided Multisource Elastic Full-Waveform Inversion

Qingqing Li, Qingchen Zhang, Qizhen Du, Shijun Cheng

Research output: Contribution to journalArticlepeer-review

Abstract

Full waveform inversion (FWI) has been considered one of the most promising approaches to estimating the high-resolution subsurface parameters, which takes advantage of the kinematics and dynamics information of seismic data. However, FWI is greatly dependent on the accuracy of the initial model and vulnerable to the issue of local minimum. Moreover, the multi-source and multi-parameter crosstalk artifacts make multi-source elastic FWI (MS-EFWI) more likely to trap into a suboptimal inversion result. To remedy this defect, this study proposes an efficient elastic FWI (EFWI) paradigm that combines the crosstalk-free MS-EFWI method and a well-guided initial model-building algorithm. Specifically, we apply a harmonic wavelet encoding technology to MS-EFWI, by which the multi-source wavefields can be completely deblended without crosstalk noise. The well-guided structure-oriented interpolation, with the aid of the dip information derived from the initial migration images, is designed to build a satisfactory initial model and therefore reduce the risk of cycle skipping. Numerical examples based on the 2D Overthrust model and Marmousi model further demonstrate the feasibility and robustness of the proposed method with a relatively little number of iterations.
Original languageEnglish (US)
Pages (from-to)1-14
Number of pages14
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume60
DOIs
StatePublished - Aug 23 2022
Externally publishedYes

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