Passive seismic full waveform inversion using reconstructed body-waves for subsurface velocity construction

ABSTRACT

Full waveform inversion (FWI) using passive seismic data can use amplitude, phase and travel time information from the data simultaneously. However, at least three challenges are involved in passive seismic full waveform inversion (PSFWI): a low signal-to-noise ratio (SNR), source location uncertainty and an unknown source wavelet. In this study, we propose a method that combines seismic interferometry and a source-independent inversion algorithm to solve these problems. Using seismic interferometry, the original passive seismic data recorded on the surface can be reconstructed into new virtual source records that have a relatively high SNR and certain source location. The source-independent algorithm eliminates the influence of source wavelet error on the final inversion results. Through numerical tests, we discuss the effects of passive source number and recording time on the inversion results and find that increasing the source number or recording time can improve inversion quality. We extract the background velocity model from the results of PSFWI and use it as the initial model of active source FWI. Least square reverse time migration (LSRTM) is then conducted to verify the accuracy of the inverted velocity models. The final results demonstrate that our PSFWI method can construct accurate long-wavelength velocity structures for subsequent active source FWI. The velocity model constructed using our successive inversion strategy can improve the LSRTM results.

KEYWORDS:

• Passive seismic
• full waveform inversion
• seismic interferometry
• source-independent
• noise sources

Acknowledgements

We would like to thank the Associate Editor Jianxiong Chen, and the four anonymous reviewers for their insightful suggestions and contributions to this manuscript. We thank the financial support of the National Natural Science Foundation of China (No. 41674124) and the National Key Research and Development Project of China (No. 2016YFC0600301).

Additional information

Funding

We thank the financial support of the National Key Research and Development Project of China (No. 2016YFC0600301) and the National Natural Science Foundation of China (No. 41674124).