Quantitative Classification of Near-Fault Ground Motions Using Wavelet Analysis

by Jack Baker and Shrey Shahi


Archive: downloads relating to the Baker (2007) publication are available at this link


This web page provides documentation and supporting software for the following manuscripts:

Shahi, S. (2013). “A probabilistic framework to include the effects of near-fault directivity in seismic hazard assessment.” Ph.D. Thesis, Stanford University, Stanford, CA.

Shahi, S., and Baker, J. W. (2011). “An empirically calibrated framework for including the effects of near-fault directivity in Probabilistic Seismic Hazard Analysis.” Bulletin of the Seismological Society of America, 101(2), 742-755.

Shahi, S. K., and Baker, J. W. (2011). “Regression models for predicting the probability of near-fault earthquake ground motion pulses, and their period.” 11th International Conference on Applications of Statistics and Probability in Civil Engineering, Zurich, Switzerland, 8.

Baker J.W. (2007). Quantitative classification of near-fault ground motions using wavelet analysis. Bulletin of the Seismological Society of America. 97 (5), 1486-1501.

These manuscripts describe an approach for analyzing earthquake ground motions using wavelet analysis, to quantitatively identify strong velocity pulses that might be caused by directivity effects. While the papers describe the method, complete documentation of the project is best achieved by providing the software used to perform the analysis, documentation of that software, and summary results from the analyses. This website serves to provide that documentation, allowing others to reproduce the results published in the manuscript.


Available downloads relating the the Shahi (2013) model

Matlab Scripts. This .zip file contains the basic functions used in the ground motion classification procedure. Further documentation is coming soon.

Classification results. This website contains detailed information about all ground motions in the considered database that were identified as pulse-like using the proposed algorithm.




This material is based in part upon work supported by the National Science Foundation under NSF grant number CMMI 0726684. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.


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You are welcome to download and use any of these materials, as long as you acknowledge this website and associated publications as the source of the data. The Matlab scripts are free software; you can redistribute them and/or modify them under the terms of the GNU General Public License as published by the Free Software Foundation, version 2. This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.