https://github.com/jpvantassel/swprepost
A Python package for surface-wave inversion pre- and post-processing.
https://github.com/jpvantassel/swprepost
dinver dispersion geopsy inversion post-processing pre-processing surface-waves swinvert
Last synced: about 2 months ago
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A Python package for surface-wave inversion pre- and post-processing.
- Host: GitHub
- URL: https://github.com/jpvantassel/swprepost
- Owner: jpvantassel
- License: other
- Created: 2019-11-17T17:39:31.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2024-04-29T14:51:09.000Z (about 1 year ago)
- Last Synced: 2024-12-08T05:45:00.229Z (6 months ago)
- Topics: dinver, dispersion, geopsy, inversion, post-processing, pre-processing, surface-waves, swinvert
- Language: Python
- Homepage:
- Size: 3.55 MB
- Stars: 38
- Watchers: 2
- Forks: 16
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# _swprepost_ - A Python Package for Surface Wave Inversion Pre- and Post-Processing
> Joseph P. Vantassel, The University of Texas at Austin
[](https://zenodo.org/badge/latestdoi/222287042)
[](https://github.com/jpvantassel/swprepost/blob/main/LICENSE.txt)
[](https://circleci.com/gh/jpvantassel/swprepost)
[](https://swprepost.readthedocs.io/en/latest/?badge=latest)
[](https://lgtm.com/projects/g/jpvantassel/swprepost/context:python)
[](https://www.codacy.com/manual/jpvantassel/swprepost?utm_source=github.com&utm_medium=referral&utm_content=jpvantassel/swprepost&utm_campaign=Badge_Grade)
[](https://codecov.io/gh/jpvantassel/swprepost)
## Table of Contents
---
- [About _swprepost_](#About-swprepost)
- [A Few Examples](#A-Few-Examples)
- [Getting Started](#Getting-Started)
## About _swprepost_
---
_swprepost_ is an open-source Python package for performing surface wave
inversion pre- and post-processing. _swprepost_ was initially developed by
Joseph P. Vantassel under the supervision of Professor Brady R. Cox at
The University of Texas at Austin. The package continues to be developed by
Joseph P. Vantassel.
The package includes multiple class definitions for interacting with the various
components required for surface wave inversion. It is designed to integrate
seamlessly with the _dinver_ module of the popular open-source software _[geopsy](www.geopsy.org)_,
however has been written in a general manner to ensure its usefulness with other
inversion programs. Furthermore, some of the class definitions provided, such as
`GroundModel`, may even be of use to those working in the geotechnical or
geophysical fields, but who do not perform surface wave inversion.
If you use _swprepost_ in your research or consulting we ask you please cite the
following:
> Joseph Vantassel. (2020). jpvantassel/swprepost: latest (Concept). Zenodo.
> http://doi.org/10.5281/zenodo.3839998
> Vantassel, J.P. and Cox, B.R. (2021). SWinvert: a workflow for performing
> rigorous 1-D surface wave inversions. Geophysical Journal International
> 224, 1141-1156. https://doi.org/10.1093/gji/ggaa426
_Note: For software, version specific citations should be preferred to general_
_concept citations, such as that listed above. To generate a version specific_
_citation for _swprepost_, please use the citation tool for that specific_
_version on the _swprepost_ [archive](https://doi.org/10.5281/zenodo.3839998)._
## A Few Examples
All examples presented here can be replicated using the Jupyter notebook titled
`ReadmeExamples.ipynb` in the `examples/basic` directory.
### Import 100 ground models in less than 0.5 seconds
```Python
time_start = time.perf_counter()
gm_suite = swprepost.GroundModelSuite.from_geopsy(fname="inputs/from_geopsy_100gm.txt")
time_stop = time.perf_counter()
print(f"Elapsed Time: {np.round(time_stop - time_start)} seconds.")
print(gm_suite)
```
```Bash
Elapsed Time: 0.0 seconds.
GroundModelSuite with 100 GroundModels.
```
### Plot the ground models
```Python
fig, ax = plt.subplots(figsize=(2,4), dpi=150)
# Plot 100 best
label = "100 Best"
for gm in gm_suite:
ax.plot(gm.vs2, gm.depth, color="#ababab", label=label)
label=None
# Plot the single best in different color
ax.plot(gm_suite[0].vs2, gm_suite[0].depth, color="#00ffff", label="1 Best")
ax.set_ylim(50,0)
ax.set_xlabel("Vs (m/s)")
ax.set_ylabel("Depth (m)")
ax.legend()
plt.show()
```
### Compute and plot their uncertainty
```Python
fig, ax = plt.subplots(figsize=(2,4), dpi=150)
disc_depth, siglnvs = gm_suite.sigma_ln()
ax.plot(siglnvs, disc_depth, color="#00ff00")
ax.set_xlim(0, 0.2)
ax.set_ylim(50,0)
ax.set_xlabel("$\sigma_{ln,Vs}$")
ax.set_ylabel("Depth (m)")
plt.show()
```
## Getting Started
---
### Installing or Upgrading _swprepost_
1. If you do not have Python 3.6 or later installed, you will need to do
so. A detailed set of instructions can be found
[here](https://jpvantassel.github.io/python3-course/#/intro/installing_python).
2. If you have not installed _swprepost_ previously use
`pip install swprepost`. If you are not familiar with `pip`, a useful tutorial
can be found [here](https://jpvantassel.github.io/python3-course/#/intro/pip).
If you have an earlier version and would like to upgrade to the latest version
of _swprepost_ use `pip install swprepost --upgrade`.
3. Confirm that `swprepost` has installed/updated successfully by examining the
last few lines of text displayed in the console.
### Using _swprepost_
To start learning about _swprepost_, we recommend walking through the
provided examples.
1. To access the
[examples](https://github.com/jpvantassel/swprepost/tree/main/examples)
you can download the latest release of the project archived on
[zenodo](https://doi.org/10.5281/zenodo.3839998).
2. Unzip the project folder titled `swprepost-vX.X.X.zip`. And move the
`example` directory to any location of your choosing. You can now discard
the other files and directories in the .zip.
3. Explore the Jupyter notebooks in the
[basic](https://github.com/jpvantassel/swprepost/tree/main/examples/basic)
directory for a no-coding-required introduction to the _swprepost_ package.
If you have not installed `Jupyter`, detailed instructions can be found
[here](https://jpvantassel.github.io/python3-course/#/intro/installing_jupyter).
4. Move to the
[adv](https://github.com/jpvantassel/swprepost/tree/main/examples/adv)
directory and follow the Jupyter notebook title
`example_swinvert_workflow.ipynb` for
an example of _swprepost_ applied in the context of the SWinvert workflow
(Vantassel and Cox, 2021). This workflow demonstrates how to use _swprepost_
to perform surface wave processing and _swbatch_ for running batch-style
surface wave inversion. For more information on _swbatch_ see
[its GitHub page](https://github.com/jpvantassel/swbatch).
5. Enjoy!