H. Mohammadigheymasi et al., "Sparsity-promoting approach to polarization analysis of seismic signals in the time-frequency domain," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2022.3141580.
SP-TFF has been tested using several operating systems (Macintosh, Unix, and PC), and it should run on all platforms that support MATLAB.
I will constantly update this repo to make them easier to use and understand.
SP-TFF code package aims to present a methodology for high-resolution polarization analysis and filtering of seismic signals in the TF-domain. The main developments in this research work are: (a) reformulation of the eigenvalue decomposition polarization analysis (EDPA) in TF-domain, (b) combining the SP-TFR to the formulation to obtain high-resolution TF-domains polarization parameters for discriminating nearby seismic phases, and (c) incorporating TF-domain directivity, rectilinearity, and amplitude attributes to extract (or eliminate) different seismic phases. The main focus is to discriminate between Love and Rayleigh from the body and coda waves.
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The synthetic and real example of the "Sparsity-promoting approach to polarization analysis of seismic signals in the time-frequency domain" are reproducible.
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A fast and efficient code for sparsity-promoting time-frequency representation (SP-TFR) is presented.
Run POL_Synthetic_main.m and POL_Real_main.m codes
If you use the SP-TFF codes in your research, please cite:
Hamzeh Mohammadigheymasi, Paul Crocker, Maryam Fathi, Eduardo Almeida, Graca Silveira, Ali Gholami, and Martin Schimmel. (2022). Sparsity-promoting approach to polarization analysis of seismic signals in the time-frequency domain. IEEE transaction in Geoscience and Remote sensing journal, DOI:10.1109/TGRS.2022.3141580.
SP-TFF is a method for separating not only the Rayleigh waves from the Love waves, but also in discriminating them from the body and coda waves.
If you occur any bugs or questions, you can either open a new issue in this repo or send me an e-mail (hamzeh@ubi.pt).
This project is licensed under the MIT License - check the LICENSE file for details.
The code computes the outputs of the synthetic simulation of "Sparsity-promoting approach to polarization analysisof seismic signals in the time-frequency domain", IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
https://github.com/SigProSeismology/SP-TFF
Hamzeh Mohammadigheymasi, Sparsity-promoting approach to polarization analysis of seismic signals in the time-frequency domain, July 2020
NOTE: This SOFTWARE may be used by any individual or corporation for any purpose with the exception of re-selling or re-distributing the SOFTWARE. By using this software, you are agreeing to the terms detailed in this software's Matlab source file.
BEGIN TERMS OF USE LICENSE
This SOFTWARE is maintained by the SP-TFF Project. The copyright and ownership is jointly held by the first author of the "Sparsity-promoting approach to polarization analysis of seismic signals in the time-frequency domain" published in the IEEE transaction on Geoscience and remote sensing journal. project may be contacted via email at: hamzeh@ubi.pt
The term 'SOFTWARE' refers to the Matlab source code, translations to any other computer language, or object code
Terms of use of this SOFTWARE
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This SOFTWARE may be used by any individual or corporation for any purpose with the exception of re-selling or re-distributing the SOFTWARE.
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The AUTHOR and SP-TFF must be acknowledged in any resulting publications or presentations
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This SOFTWARE is provided "as is" with no warranty of any kind either expressed or implied. SP-TFF makes no warranties or representation as to its accuracy, completeness, or fitness for any purpose. SP-TFF is under no obligation to provide support of any kind for this SOFTWARE.
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SP-TFF project periodically adds, changes, improves or updates this SOFTWARE without notice. New versions will be made available at ble at https://github.com/SigProSeismology/SP-TFF.
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Use this SOFTWARE at your own risk.
END TERMS OF USE LICENSE
Written by Hamzeh Mohammadigheymasi, 2020 Last Modified: 11/Nov/2021 Version 1.0 Since 0.0.1