| Name | noisepy-seis JSON |
| Version |
0.9.90
JSON |
| download |
| home_page | None |
| Summary | A High-performance Computing Python Package for Ambient Noise Analysis |
| upload_time | 2024-11-06 23:47:29 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <3.11,>=3.9 |
| license | MIT License Copyright (c) 2019 Marine Denolle & Chengxin Jiang Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
ambient
change
cross-correlation
dispersion
monitoring
noise
seismic
surface
velocity
wave
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
|
# About NoisePy
NoisePy is a Python package designed for fast and easy computation of ambient noise cross-correlation functions. It provides additional functionality for noise monitoring and surface wave dispersion analysis.
[](https://noisepy.github.io/NoisePy/)
[](https://github.com/noisepy/NoisePy/actions/workflows/test.yaml)
[](https://codecov.io/gh/noisepy/NoisePy)
[](https://zenodo.org/badge/latestdoi/157871462)
<img src="https://raw.githubusercontent.com/noisepy/NoisePy/main/docs_old/figures/logo.png" width="800" height="400">
## Major updates coming
NoisePy is going through a major refactoring to make this package easier to develop and deploy. Submit an issue, fork the repository and create pull requests to [contribute](CONTRIBUTING.md).
## Installation
The nature of NoisePy being composed of python scripts allows flexible package installation, which is essentially to build dependent libraries the scripts and related functions live upon. We recommend using [conda](https://docs.conda.io/en/latest/) or [pip](https://pypi.org/project/pip/) to install.
**Note the order of the command lines below matters**
### With Conda and pip
```bash
conda create -n noisepy -y python=3.10 pip
conda activate noisepy
pip install noisepy-seis
```
To add jupyter dependencies, install them
```
pip install ipykernel notebook
python -m ipykernel install --user --name noisepy
```
### With Conda and pip and MPI support
```bash
conda create -n noisepy -y python=3.10 pip mpi4py
conda activate noisepy
pip install noisepy-seis[mpi]
```
### With virtual environment
```bash
python -m venv noisepy
source noisepy/bin/activate
pip install noisepy-seis
```
### With virtual environment and MPI support
An MPI installation is required. E.g. for macOS using [brew](https://brew.sh/) :
```bash
brew install open-mpi
```
```bash
python -m venv noisepy
source noisepy/bin/activate
pip install noisepy-seis[mpi]
```
## Functionality
Here is a list of features of the package:
* download continous noise data based:
+ on webservices using obspy's core functions of [get_station](https://docs.obspy.org/packages/autogen/obspy.clients.fdsn.client.Client.get_stations.html) and [get_waveforms](https://docs.obspy.org/packages/autogen/obspy.clients.fdsn.client.Client.get_waveforms.html)
+ on AWS S3 bucket calls, with a test on the SCEDC AWS Open Dataset.
* save seismic data in [ASDF](https://asdf-definition.readthedocs.io/en/latest/) format, which convinently assembles meta, wavefrom and auxililary data into one single file ([Tutorials](https://github.com/SeismicData/pyasdf/blob/master/doc/tutorial.rst) on reading/writing ASDF files)
* offers scripts to precondition data sets before cross correlations. This involves working with gappy data from various formats (SAC/miniSEED) and storing it on local in ASDF.
* performs fast and easy cross-correlation with functionality to run in parallel through [MPI](https://en.wikipedia.org/wiki/Message_Passing_Interface)
* **Applications module**:
+ *Ambient noise monitoring*: measure dv/v using a wide variety of techniques in time, fourier, and wavelet domain (Yuan et al., 2021)
+ *Surface wave dispersion*: construct dispersion images using conventional techniques.
## Usage
To run the code on a single core, open the terminal and activate the noisepy environment before run following commands. To run on institutional clusters, see installation notes for individual packages on the module list of the cluster.
### Deploy using Docker
We use I/O on disk, so users need root access to the file system. To install rootless docker, see instructions [here](https://docs.docker.com/engine/security/rootless/#install).
```bash
docker pull ghcr.io/noisepy/noisepy:latest
docker run -v ~/tmp:/tmp ghcr.io/noisepy/noisepy:latest cross_correlate --path /tmp
```
## Tutorials
Short tutorials on how to use NoisePy can be is available [here](https://noisepy.github.io/NoisePy/) and can be run directly in Colab. These tutorials present simple examples of how NoisePy might work. We strongly encourage you to download the NoisePy package and play it on your own! If you have any comments and/or suggestions during running the codes, please do not hesitate to contact us through email or open an issue in this github page!
Chengxin Jiang (chengxinjiang@gmail.com)
Marine Denolle (mdenolle@uw.edu)
Yiyu Ni (niyiyu@uw.edu)
### Taxonomy
Taxonomy of the NoisePy variables.
* ``station`` refers to the site that has the seismic instruments that records ground shaking.
* ``channel`` refers to the direction of ground motion investigated for 3 component seismometers. For DAS project, it may refers to the single channel sensors.
* ``ista`` is the index name for looping over stations
* ``cc_len`` correlation length, basic window length in seconds
* ``step`` is the window that get skipped when sliding windows in seconds
* ``smooth_N`` number of points for smoothing the time or frequency domain discrete arrays.
* ``maxlag`` maximum length in seconds saved in files in each side of the correlation (save on storage)
* ``substack, substack_windows`` boolean, number of window over which to substack the correlation (to save storage or do monitoring).
* ``time_chunk, nchunk`` refers to the time unit that defined a single job. for instace, ``cc_len`` is the correlation length (e.g., 1 hour, 30 min), the overall duration of the experiment is the total length (1 month, 1 year, ...). The time chunk could be 1 day: the code would loop through each cc_len window in a for loop. But each day will be sent as a thread.
## Acknowledgements
Thanks to our contributors so far!
[](https://github.com/noisepy/NoisePy/graphs/contributors)
### Use this reference when publishing on your work with noisepy
Main code:
* Zenodo DOI: [noisepy/NoisePy](https://zenodo.org/badge/latestdoi/157871462)
* Jiang, C. and Denolle, M. [NoisePy: a new high-performance python tool for seismic ambient noise seismology.](https://doi.org/10.1785/0220190364) _Seismological Research Letter_ 91, no. 3 (2020): 1853–1866. https://doi.org/10.1785/0220190364
Algorithms used:
* (data pre-processing) Seats, K. J., Jesse F. L., and German A. P. [Improved ambient noise correlation functions using Welch′ s method.](https://doi.org/10.1111/j.1365-246X.2011.05263.x) _Geophysical Journal International_ 188, no. 2 (2012): 513-523. https://doi.org/10.1111/j.1365-246X.2011.05263.x
* (dv/v in wavelet domain) Yuan, C., Bryan, J. T., and Denolle, M. [Numerical comparison of time-, frequency- and wavelet-domain methods for coda wave interferometry.](https://doi.org/10.1093/gji/ggab140) _Geophysical Journal International_ 226, no. 2 (2021): 828-846. https://doi.org/10.1093/gji/ggab140
* (optimal stacking) Yang X, Bryan J, Okubo K, Jiang C, Clements T, Denolle MA. [Optimal stacking of noise cross-correlation functions/](https://doi.org/10.1093/gji/ggac410) _Geophysical Journal International_. 2023 Mar;232(3):1600-18. https://doi.org/10.1093/gji/ggac410
This research received software engineering support from the University of Washington’s Scientific Software Engineering Center ([SSEC](https://escience.washington.edu/software-engineering/ssec/)) supported by Schmidt Futures, as part of the Virtual Institute for Scientific Software (VISS). We would like to acknowledge [Carlos Garcia Jurado Suarez](https://github.com/carlosgjs) and [Nicholas Rich](https://github.com/nrich20) for their collaboration and contributions to the software.
Raw data
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"description": "# About NoisePy\nNoisePy is a Python package designed for fast and easy computation of ambient noise cross-correlation functions. It provides additional functionality for noise monitoring and surface wave dispersion analysis.\n\n[](https://noisepy.github.io/NoisePy/)\n[](https://github.com/noisepy/NoisePy/actions/workflows/test.yaml)\n[](https://codecov.io/gh/noisepy/NoisePy)\n[](https://zenodo.org/badge/latestdoi/157871462)\n\n<img src=\"https://raw.githubusercontent.com/noisepy/NoisePy/main/docs_old/figures/logo.png\" width=\"800\" height=\"400\">\n\n## Major updates coming\nNoisePy is going through a major refactoring to make this package easier to develop and deploy. Submit an issue, fork the repository and create pull requests to [contribute](CONTRIBUTING.md).\n\n## Installation\nThe nature of NoisePy being composed of python scripts allows flexible package installation, which is essentially to build dependent libraries the scripts and related functions live upon. We recommend using [conda](https://docs.conda.io/en/latest/) or [pip](https://pypi.org/project/pip/) to install.\n\n**Note the order of the command lines below matters**\n\n### With Conda and pip\n```bash\nconda create -n noisepy -y python=3.10 pip\nconda activate noisepy\npip install noisepy-seis\n```\n\nTo add jupyter dependencies, install them\n```\npip install ipykernel notebook\npython -m ipykernel install --user --name noisepy\n```\n\n### With Conda and pip and MPI support\n```bash\nconda create -n noisepy -y python=3.10 pip mpi4py\nconda activate noisepy\npip install noisepy-seis[mpi]\n```\n\n### With virtual environment\n```bash\npython -m venv noisepy\nsource noisepy/bin/activate\npip install noisepy-seis\n```\n\n### With virtual environment and MPI support\nAn MPI installation is required. E.g. for macOS using [brew](https://brew.sh/) :\n```bash\nbrew install open-mpi\n```\n\n```bash\npython -m venv noisepy\nsource noisepy/bin/activate\npip install noisepy-seis[mpi]\n```\n\n## Functionality\nHere is a list of features of the package:\n* download continous noise data based:\n + on webservices using obspy's core functions of [get_station](https://docs.obspy.org/packages/autogen/obspy.clients.fdsn.client.Client.get_stations.html) and [get_waveforms](https://docs.obspy.org/packages/autogen/obspy.clients.fdsn.client.Client.get_waveforms.html)\n + on AWS S3 bucket calls, with a test on the SCEDC AWS Open Dataset.\n* save seismic data in [ASDF](https://asdf-definition.readthedocs.io/en/latest/) format, which convinently assembles meta, wavefrom and auxililary data into one single file ([Tutorials](https://github.com/SeismicData/pyasdf/blob/master/doc/tutorial.rst) on reading/writing ASDF files)\n* offers scripts to precondition data sets before cross correlations. This involves working with gappy data from various formats (SAC/miniSEED) and storing it on local in ASDF.\n* performs fast and easy cross-correlation with functionality to run in parallel through [MPI](https://en.wikipedia.org/wiki/Message_Passing_Interface)\n* **Applications module**:\n + *Ambient noise monitoring*: measure dv/v using a wide variety of techniques in time, fourier, and wavelet domain (Yuan et al., 2021)\n + *Surface wave dispersion*: construct dispersion images using conventional techniques.\n\n## Usage\n\nTo run the code on a single core, open the terminal and activate the noisepy environment before run following commands. To run on institutional clusters, see installation notes for individual packages on the module list of the cluster.\n\n### Deploy using Docker\nWe use I/O on disk, so users need root access to the file system. To install rootless docker, see instructions [here](https://docs.docker.com/engine/security/rootless/#install).\n```bash\ndocker pull ghcr.io/noisepy/noisepy:latest\ndocker run -v ~/tmp:/tmp ghcr.io/noisepy/noisepy:latest cross_correlate --path /tmp\n```\n\n## Tutorials\nShort tutorials on how to use NoisePy can be is available [here](https://noisepy.github.io/NoisePy/) and can be run directly in Colab. These tutorials present simple examples of how NoisePy might work. We strongly encourage you to download the NoisePy package and play it on your own! If you have any comments and/or suggestions during running the codes, please do not hesitate to contact us through email or open an issue in this github page!\n\nChengxin Jiang (chengxinjiang@gmail.com)\nMarine Denolle (mdenolle@uw.edu)\nYiyu Ni (niyiyu@uw.edu)\n\n### Taxonomy\nTaxonomy of the NoisePy variables.\n\n* ``station`` refers to the site that has the seismic instruments that records ground shaking.\n* ``channel`` refers to the direction of ground motion investigated for 3 component seismometers. For DAS project, it may refers to the single channel sensors.\n* ``ista`` is the index name for looping over stations\n* ``cc_len`` correlation length, basic window length in seconds\n* ``step`` is the window that get skipped when sliding windows in seconds\n* ``smooth_N`` number of points for smoothing the time or frequency domain discrete arrays.\n* ``maxlag`` maximum length in seconds saved in files in each side of the correlation (save on storage)\n* ``substack, substack_windows`` boolean, number of window over which to substack the correlation (to save storage or do monitoring).\n* ``time_chunk, nchunk`` refers to the time unit that defined a single job. for instace, ``cc_len`` is the correlation length (e.g., 1 hour, 30 min), the overall duration of the experiment is the total length (1 month, 1 year, ...). The time chunk could be 1 day: the code would loop through each cc_len window in a for loop. But each day will be sent as a thread.\n\n## Acknowledgements\nThanks to our contributors so far!\n\n[](https://github.com/noisepy/NoisePy/graphs/contributors)\n\n### Use this reference when publishing on your work with noisepy\n\nMain code:\n\n* Zenodo DOI: [noisepy/NoisePy](https://zenodo.org/badge/latestdoi/157871462)\n* Jiang, C. and Denolle, M. [NoisePy: a new high-performance python tool for seismic ambient noise seismology.](https://doi.org/10.1785/0220190364) _Seismological Research Letter_ 91, no. 3 (2020): 1853\u20131866. https://doi.org/10.1785/0220190364\n\nAlgorithms used:\n* (data pre-processing) Seats, K. J., Jesse F. L., and German A. P. [Improved ambient noise correlation functions using Welch\u2032 s method.](https://doi.org/10.1111/j.1365-246X.2011.05263.x) _Geophysical Journal International_ 188, no. 2 (2012): 513-523. https://doi.org/10.1111/j.1365-246X.2011.05263.x\n\n* (dv/v in wavelet domain) Yuan, C., Bryan, J. T., and Denolle, M. [Numerical comparison of time-, frequency- and wavelet-domain methods for coda wave interferometry.](https://doi.org/10.1093/gji/ggab140) _Geophysical Journal International_ 226, no. 2 (2021): 828-846. https://doi.org/10.1093/gji/ggab140\n\n* (optimal stacking) Yang X, Bryan J, Okubo K, Jiang C, Clements T, Denolle MA. [Optimal stacking of noise cross-correlation functions/](https://doi.org/10.1093/gji/ggac410) _Geophysical Journal International_. 2023 Mar;232(3):1600-18. https://doi.org/10.1093/gji/ggac410\n\nThis research received software engineering support from the University of Washington\u2019s Scientific Software Engineering Center ([SSEC](https://escience.washington.edu/software-engineering/ssec/)) supported by Schmidt Futures, as part of the Virtual Institute for Scientific Software (VISS). We would like to acknowledge [Carlos Garcia Jurado Suarez](https://github.com/carlosgjs) and [Nicholas Rich](https://github.com/nrich20) for their collaboration and contributions to the software.\n",
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