Wave-equation dispersion spectrum inversion for near-surface characterization using fiber-optics acquisition

Zhendong Zhang, Mamdoh Alajami, Tariq Ali Alkhalifah

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Summary Distributed acoustic sensing (DAS) acquisition is becoming more and more popular for its dense sampling at a lower cost than seismometers. However, data processing for DAS data is challenging, especially for surface-deployed fibers, in which only the horizontal component of strain variation is effectively recorded. Also, the coupling between the fiber and the Earth is usually poor and the recorded single-component data are noisy. Thus, we introduce data processing strategies dedicated to enhancing the ambient-noise and active-source seismic data recorded by a horizontally-deployed tactical fiber optics cable buried in a sand dune area in Saudi Arabia. We propose a similarity-weighted stacking of randomly selected short-time duration windows to generate virtual common-shot-gathers (CSG) from the recorded ambient noise. The similarity-weighted stacking only counts the primary contributions of coherent events, while a short-time correlation can suppress the crosstalk usually present in late arrivals. The stacking fold is preserved or even can be increased by generating plenty of random time segments compared to stacking the full recording time. For the recorded active-source data, we skip the interferometric step, but use the envelope of the common-shot gathers. The envelope is needed to mitigate the complexity of waveforms, while preserving the slopes of arrivals. Then, we use the wave-equation based Rayleigh-wave dispersion-spectrum inversion, which utilizes all the dispersion modes available and does not require picking the dispersion curve, in estimating the shallow S-wave velocities. The local-crosscorrelation objective function allows for additional freedom in matching the modeled and observed data, and thus, helps us avoid falling into a local minimum when starting with kinematically-poor velocity models.
Original languageEnglish (US)
Pages (from-to)907-918
Number of pages12
JournalGeophysical Journal International
Issue number2
StatePublished - Apr 28 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We want to thank Lapo Boschi, Jacopo Boaga and the anonymous reviewer, for the effort put into the review of this manuscript. We thank KAUST for its support and specifically the seismic wave analysis group members for their valuable insights. We thank KACST for
acquiring the data and we also thank Roman Pevzner and Guanchao Wang for discussions. We would like to extend our thanks to Silixa Co. team Michael Mondanos and Stoyan Nikolov for valuable discussions and sharing their experiences and knowledge during the field work. For computer time, this research used the resources of the Supercomputing Laboratory at King Abdullah University of Science & Technology (KAUST) in Thuwal, Saudi Arabia.


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