Data‐Driven Velocity Model Evaluation Using K‐Means Clustering
Name
Geophysical Research Letters - 2021 - Xiong - Data‐Driven Velocity Model Evaluation Using K‐Means Clustering.pdf
Description
Published version
Size
2.89 MB
Format
Adobe PDF
Checksum (MD5)
0ecddeb23ce0bf353d2f47e77d670941
Author(s)
Xiong, Neng
Qiu, Hongrui
Niu, Fenglin
Date Issued
November 29, 2021
Journal
Geophysical Research Letters
Publisher
American Geophysical Union
Citation
Xiong, N., Qiu, H., & Niu, F. (2021). Data-driven velocity model evaluation using K-means clustering. Geophysical Research Letters, 48, e2021GL096040.
Version
Final published version
Abstract
We develop a data-driven clustering method to evaluate a velocity model using surface wave velocity dispersion. This is done by first computing theoretical dispersion curves for 1-D velocity profiles of all the grid locations and then splitting the resulting dispersion curves into a certain number of groups via the K-means clustering. The observed dispersion curves are also clustered following the same procedure and the velocity model is assessed by comparing the spatial patterns obtained for the observed and synthetic data sets. The method is applied to evaluate two community velocity models in southern California, CVM-S4.26 and CVM-H15.1, using phase velocity maps derived for 3–16 s Rayleigh waves. We found a good correlation in the spatial distribution of clusters between the result of CVM-S4.26 and that of the observed data, suggesting that the CVM-S4.26 fits the observed dispersion maps better than the CVM-H15.1 in terms of features extracted from the clustering analysis.
MIT Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1029/2021gl096040
