Codes for Slowness-Enhanced Back-Projection (SEBP)
<p>Slowness-Enhanced MUSIC back-projection (SEBP) of teleseismic P waves implemented in Matlab. The Matlab Signal processing, Mapping and Imaging tool boxes are required.</p> <p>The objective of this package is to perform the Back-Projection Imaging on the seismograms of large earthquakes recorded by large-scale dense arrays and calibrate the spatial errors by aftershock. The instruction is performed on the 2021 Mw 7.4 Maduo Earthquake, and you can also practise Back-Projection Imaging on the 2011 Mw 9.0 Tohoku Earthquake on your own.</p> <p>Back-Projection is an earthquake-rupture imaging technique utilizing the coherent teleseismic P wavefield based on seismic array processing. Back-tracking of seismic waves recorded by dense arrays allows Back-Projection to determine the spatio-temporal properties of the rupture (length, direction, speed, and segmentation). Over recent decades, the development of large-scale dense seismic networks has enabled the Back-Projection imaging of the rupture process of major large earthquakes.</p> <p>This code package is developed based on MUSIC BP. Please see the "MUSICBP" branch for more details about MUSIC BP.</p> <p>Meng, L., A. Inbal, and J.-P. Ampuero. 2011. “A window into the complexity of the dynamic rupture of the 2011 Mw 9 Tohoku-Oki earthquake”, Geophys. Res. Lett., 38, L00G07, doi:10.1029/2011GL048118.</p> <p>Bao, H., Ampuero, J. P., Meng, L., Fielding, E. J., Liang, C., Milliner, C. W., ... & Huang, H. (2019). Early and persistent supershear rupture of the 2018 magnitude 7.5 Palu earthquake. Nature Geoscience, 12(3), 200-205.</p> <p>Kiser, E., & Ishii, M. (2017). Back-projection imaging of earthquakes. Annual Review of Earth and Planetary Sciences, 45, 271-299.</p> <p>Backprojection imaging is also performed routinely by IRIS for all new large earthquakes: <a href="https://ds.iris.edu/ds/products/backprojection/" rel="nofollow">https://ds.iris.edu/ds/products/backprojection/</a></p> <p>The MUSICBP code is contributed and maintained by Liuwei Xu (<a href="mailto:xuliuw1997@ucla.edu">xuliuw1997@ucla.edu</a>), Han Bao (<a href="mailto:hbrandon@ucla.edu">hbrandon@ucla.edu</a>), and Lingsen Meng (<a href="mailto:meng@epss.ucla.edu">meng@epss.ucla.edu</a>).</p> <ul> <li> <p> Instruction: SEBP.pdf</p> </li> <li> <p> Code and Data: AU*.m</p> </li> <li> <p> Related Paper: BaoNG_2019.pdf</p> </li> </ul>
ShareScore
24/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 4
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 0
- Engagement
- 0