Input files and jupyter notebooks to reproduce the processings and figures associated with the ambient seismic noise in Parkfield.
See the documentation for the instruction of running the examples, downloading the cross-correlation data and the recipe of generating the figures.
See the recipe of figures to perform the post-processing and plot figures.
Input files and the output log of the ambient seismic noise processing using SeisMonitoring.jl.
See the docs to run the processing from downloading the data, cross-correlation, stacking, and measurement of dv/v.
We also have the tutorial of the software in different Github repository: See SeisMonitoring_Example.
Post-processing of the cross-correlation and dv/v time history.
Compute the strain field and evaluate the sensitivity of dv/v to the cumulative strain.
Plot the availability of seismic data.
Fitting the model to the observed dv/v time history.
Plot the spectrogram of the continuous seismic waveform.
Plot the map and compute the fault normal distance of the seismic stations.
Notebooks to test the codes.
Some scripts used for manipulating the input and output of processings.
The intermediate files of the post-processing are available in the UW dasway (doi: 10.6069/PK9D-9411).
| Filename | Size | Description | Location in repo |
|---|---|---|---|
| SeisMonitoring_PPSDdata.tar.gz | 1.4GB | Probabilistic power spectral densities of the raw seismic data. | Post/Spectrogram/ |
| BP.CCRB-BP.CCRB-11.jld2 (← Link to download docs) | ~500MB/pair | Cross-correlation functions over 20 years for a give station-channel pair with different frequency bands. | e.g. Appx/plot_CCF/cc_channel_collection/ |
| corrdata_BP.LCCB-BP.SCYB-11_0.9-1.2.npz (← Link to download docs) | ~50MB/pair | Cross-correlation function of 0.9-1.2Hz stored in .npz format. |
Appx/plot_CCF/data_npz/ |
| monitoring_stats_uwbackup_2010-2022.tar.gz | 82MB | dv/v datasheet associated with the Stretching and MWCS methods | Post/ModelFit/data/ |
| MCMC_sampler_20000_v2_master.tar.gz | 3.3GB | Sampler of MCMC parameter search. | Post/ModelFit/processed_data/ |
| modelparam_data_master.tar.gz | 84MB | Maximum likelihood model parameters. | Post/ModelFit/ |
| MCMC_sampler_20000_v2_resheal.tar.gz | 138MB | Sampler of MCMC parameter search associated with the residual healing model. | Appx/casestudy_residual_healing/processed_data_resheal |
| MCMC_sampler_15000_v1_nobounds.tar.gz | 2.1GB | Sampler of MCMC parameter search for the case without the bounds of model parameters. | Others/get_MCMC_fixedparam/processed_data |
| modelparam_data_fixedparam.tar.gz | 38MB | Sampler of MCMC parameter search for the case without the bounds of model parameters. | Others/get_MCMC_fixedparam/ |
| monitoring_stats_TACCbackup.tar.gz | 452MB | archived dv/v datasheet of the case study in TACC | Other/dvvanalysis_onTACC/data/ |
We developed the notebooks using Mac OS (Monterey 12.6.7). The environment of python is exported in environment.yml. We used the Julia v1.8.1, SeisIO v1.2.1, and SeisNoise v0.5.3. The other dependencies associated with Julia can be found in the tutorial in the SeisMonitoring_Example.
You can create the python environment and launch the jupyter lab by
git clone https://github.com/kura-okubo/SeisMonitoring_Paper.git
cd SeisMonitoring_Paper
conda env create -f environment.yml
conda activate seismonitoring_paper
jupyter labThe default browser to open the jupyter lab can be changed following here.
To remove (uninstall) the environment, run the followings:
conda deactivate
conda env remove -n seismonitoring_paper