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Automated Data Selection in the Tau–p Domain: Application to Passive Surface Wave Imaging

  • Feng Cheng
  • Jianghai XiaEmail author
  • Michael Behm
  • Yue Hu
  • Jingyin Pang
Article
  • 123 Downloads

Abstract

In the recent decades, passive surface wave methods have gained much attention in the near-surface community due to their ability to retrieve low-frequency surface wave information. Temporal averaging over a sufficiently long period of time is a crucial step in the workflow to fulfill the randomization requirement of the stationary source distribution. Because of logistical constraints, passive seismic acquisition in urban areas is mostly limited to short recording periods. Due to insufficient temporal averaging, contributions from non-stationary sources can smear the stacked dispersion measurements, especially for the low-frequency band. We formulate a criterion in the tau–p domain for selective stacking of dispersion measurements from passive surface waves and apply it to high-frequency (> 1 Hz) traffic noise. The criterion is based on the automated detection of input data with a high signal-to-noise ratio in a desired velocity range. Modeling tests demonstrate the ability of the proposed criterion to capture the contributions from the non-stationary sources and classify the passive surface wave data. A real-world application shows that the proposed data selection approach improves the dispersion measurements by extending the frequency band below 5 Hz and attenuating the distortion between 6 and 13 Hz. Our results indicate that significant improvements can be obtained by considering tau–p-based data selection in the workflow of passive surface wave processing and interpretation.

Keywords

Tau–p domain Dispersion measurement Passive surface wave survey Data selection Traffic noise 

Notes

Acknowledgements

This study is supported by the National Natural Science Foundation of China under Grant No. 41830103, the China Scholarship Council (CSC), and the Ph.D. Innovation Fund of China University of Geosciences. The first author thanks crews of AoCheng Technology for their help in data collection.

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© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Subsurface Imaging and Sensing Laboratory, Institute of Geophysics and GeomaticsChina University of GeosciencesWuhanChina
  2. 2.Lawrence Berkeley National LaboratoryBerkeleyUSA
  3. 3.School of Earth SciencesZhejiang UniversityHangzhouChina
  4. 4.ConocoPhillips School of Geology and GeophysicsUniversity of OklahomaNormanUSA

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