This repository corresponds to the following paper:

Ji, Q., B. Luo, and B. Biondi (2024). Exploiting the Potential of Urban DAS Grids: Ambient-Noise Subsurface Imaging Using Joint Rayleigh and Love Waves, Seismol. Res. Lett. XX, 1–18, doi: 10.1785/0220230104

Contents

• main.ipynb   Main Jupyter Notebook to reproduce figures in the paper.
• my_func/   Python functions called in the notebook.
• channel_info/   Channel locations and channel pair information.
• disp_maps/   Pre-computed dispersion maps. Can be reproduced by the notebook.
• noise_cc/   MATLAB codes for synthetic DAS ambient noise cross-correlation (fiber_profile.m).

Usage

The cross-correlation data can be downloaded from: http://dx.doi.org/10.5281/zenodo.7761930

Code blocks in main.ipynb include comments describing which figures to reproduce. For details of our analysis, please check the source codes under my_func/.

Reference

This project uses the following Python package (version 2.0.1):

• Luu, K. (2021). evodcinv: Inversion of dispersion curves using evolutionary algorithms, doi: 10.5281/zenodo.5785565.

Github page: https://github.com/keurfonluu/evodcinv/tree/v2.0.1

Citation

If you may find our work helpful in your publications, please consider citing our paper.

@article{Ji_2024_SRL,
  title = {Exploiting the {{Potential}} of {{Urban DAS Grids}}: {{Ambient-Noise Subsurface Imaging Using Joint Rayleigh}} and {{Love Waves}}},
  author = {Ji, Qing and Luo, Bin and Biondi, Biondo},
  year = {2024},
  journal = {Seismological Research Letters},
  doi = {10.1785/0220230104},
}

License

This repository is licensed under the Creative Commons Attribution 4.0 International.