| Name | pymarchenko JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | |
| Summary | A bag of Marchenko algorithms implemented on top of PyLops |
| upload_time | 2023-11-29 18:47:30 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | |
| license | |
| keywords |
geophysics
inverse problems
seismic
|
| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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| coveralls test coverage |
No coveralls.
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# PyMarchenko
This Python library provides a bag of Marchenko algorithms implemented on top of [PyLops](https://pylops.readthedocs.io).
Whilst a basic implementation of the [Marchenko](https://pylops.readthedocs.io/en/latest/api/generated/pylops.waveeqprocessing.Marchenko.html#pylops.waveeqprocessing.Marchenko)
algorithm is available directly in PyLops, a number of variants have been developed over the years. This library aims at collecting
all of them in the same place and give access to them with a unique consistent API to ease switching between them and prototyping new
algorithms.
## Objective
Currently we provide the following implementations:
- Marchenko redatuming via Neumann iterative substitution (Wapenaar et al., 2014)
- Marchenko redatuming via inversion (van der Neut et al., 2017)
- Rayleigh-Marchenko redatuming (Ravasi, 2017)
- Internal multiple elimination via Marchenko equations (Zhang et al., 2019)
- Marchenko redatuming with irregular sources (Haindl et al., 2021)
Alongside the core algorithms, these following auxiliary tools are also provided:
- Target-oriented receiver-side redatuming via MDD
- Marchenko imaging (combined source-side Marchenko redatuming and receiver-side MDD redatuming)
- Angle gather computation (de Bruin, Wapenaar, and Berkhout, 1990)
## Getting started
You need **Python 3.6 or greater**.
#### From PyPi
```
pip install pymarchenko
```
#### From Github
You can also directly install from the main repository (although this is not reccomended)
```
pip install git+https://git@github.com/DIG-Kaust/pymarchenko.git@main
```
## Documentation
The official documentation of PyMarchenko is available [here](https://dig-kaust.github.io/pymarchenko/).
Visit this page to get started learning about the different algorithms implemented in this library.
Moreover, if you have installed PyMarchenko using the *developer environment* you can also build the documentation locally by
typing the following command:
```
make doc
```
Once the documentation is created, you can make any change to the source code and rebuild the documentation by
simply typing
```
make docupdate
```
Our documentation is hosted on Github-Pages and created with a Github-Action triggered every time a commit is made
to the main branch.
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"description": "# PyMarchenko\n\nThis Python library provides a bag of Marchenko algorithms implemented on top of [PyLops](https://pylops.readthedocs.io).\n\nWhilst a basic implementation of the [Marchenko](https://pylops.readthedocs.io/en/latest/api/generated/pylops.waveeqprocessing.Marchenko.html#pylops.waveeqprocessing.Marchenko)\nalgorithm is available directly in PyLops, a number of variants have been developed over the years. This library aims at collecting\nall of them in the same place and give access to them with a unique consistent API to ease switching between them and prototyping new\nalgorithms.\n\n## Objective\nCurrently we provide the following implementations:\n\n- Marchenko redatuming via Neumann iterative substitution (Wapenaar et al., 2014)\n- Marchenko redatuming via inversion (van der Neut et al., 2017)\n- Rayleigh-Marchenko redatuming (Ravasi, 2017)\n- Internal multiple elimination via Marchenko equations (Zhang et al., 2019)\n- Marchenko redatuming with irregular sources (Haindl et al., 2021)\n\nAlongside the core algorithms, these following auxiliary tools are also provided:\n\n- Target-oriented receiver-side redatuming via MDD\n- Marchenko imaging (combined source-side Marchenko redatuming and receiver-side MDD redatuming)\n- Angle gather computation (de Bruin, Wapenaar, and Berkhout, 1990)\n\n\n## Getting started\n\nYou need **Python 3.6 or greater**.\n\n#### From PyPi\n\n```\npip install pymarchenko\n```\n\n#### From Github\n\nYou can also directly install from the main repository (although this is not reccomended)\n\n```\npip install git+https://git@github.com/DIG-Kaust/pymarchenko.git@main\n```\n\n## Documentation\nThe official documentation of PyMarchenko is available [here](https://dig-kaust.github.io/pymarchenko/).\n\nVisit this page to get started learning about the different algorithms implemented in this library.\n\nMoreover, if you have installed PyMarchenko using the *developer environment* you can also build the documentation locally by\ntyping the following command:\n```\nmake doc\n```\nOnce the documentation is created, you can make any change to the source code and rebuild the documentation by\nsimply typing\n```\nmake docupdate\n```\n\nOur documentation is hosted on Github-Pages and created with a Github-Action triggered every time a commit is made\nto the main branch.\n\n",
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