by Shaobo Yang, University of Science and Technology of China, 2020
E-mail: [email protected]
This tool mainly targets at automatically extracting dispersion curves via deep learning for the image transformation technique (EGFAnalysisTimeFreq, https://github.com/ShaoboYang-USTC/EGFAnalysisTimeFreq) and the associated software developed by Huajian Yao, which is widely used in the community for dense array ambient noise analysis.
References: Yang, S., Zhang, H., Gu, N., Gao, J., Xu, J., Jin, J., Li, J., and Yao H. (2022). Automatically Extracting Surface Wave Group and Phase Velocity Dispersion Curves from Dispersion Spectrograms Using a Convolutional Neural Network. Seismological Research Letters, doi: https://doi.org/10.1785/0220210280.
- Platform: Linux
- Download repository
- Install Python 3.6
- Install dependencies:
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -r requirements.txt
-
Put the test data into
./data/TestData
. When saving the dispersion spectrograms, the velocity step (dv) must be 0.005 km/s and the recommended period step (dT) is 0.1 s. The dispersion spectrograms can be derived from EGFs or CFs using EGFAnalysisTimeFreq. -
Check the parameters in the configuration file:
./config/config.py
-
Write the station location information in
./config/station.txt
-
Run the detection program:
python pick_v.py
- The picking results are saved in
./result/pick_result
and some figures are saved in./result/plot1
and./result/plot2
- Run
./result/process/process_C.py
for QC of the phase velocity dispersion curves and./result/process/process_G.py
for group velocity. - The dispersion curves after QC is saved in
./result/process/new/
and all of the dispersion curves are shown inDPN_C.jpg
andDPN_G.jpg
. - Run
./result/process/plot_res.py
to plot each dispersion spectrogram and the corresponding DisperPicker picked dispersion curves. The figures are save in./result/process/new/plot/
.