https://github.com/simpeg/aurora
software for processing natural source electromagnetic data
https://github.com/simpeg/aurora
Last synced: 5 months ago
JSON representation
software for processing natural source electromagnetic data
- Host: GitHub
- URL: https://github.com/simpeg/aurora
- Owner: simpeg
- License: mit
- Created: 2021-05-12T21:10:22.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2026-01-21T04:30:44.000Z (5 months ago)
- Last Synced: 2026-01-21T04:38:09.108Z (5 months ago)
- Language: Jupyter Notebook
- Size: 54.6 MB
- Stars: 24
- Watchers: 6
- Forks: 7
- Open Issues: 89
-
Metadata Files:
- Readme: README.rst
- Contributing: CONTRIBUTING.rst
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
.. image:: docs/figures/aurora_logo.png
:width: 900
:alt: AURORA
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.. image:: https://img.shields.io/pypi/v/aurora.svg
:target: https://pypi.python.org/pypi/aurora
.. image:: https://img.shields.io/conda/v/conda-forge/aurora.svg
:target: https://anaconda.org/conda-forge/aurora
.. image:: https://img.shields.io/pypi/l/aurora.svg
:target: https://pypi.python.org/pypi/aurora
Aurora is an open-source package that robustly estimates single station and remote reference electromagnetic transfer functions (TFs) from magnetotelluric (MT) time series. Aurora is part of an open-source processing workflow that leverages the self-describing data container `MTH5 `_, which in turn leverages the general `mt-metadata `_ framework to manage metadata. These pre-existing packages simplify the processing by providing managed data structures, transfer functions to be generated with only a few lines of code. The processing depends on two inputs -- a table defining the data to use for TF estimation, and a JSON file specifying the processing parameters, both of which are generated automatically, and can be modified if desired. Output TFs are returned as mt-metadata objects, and can be exported to a variety of common formats for plotting, modeling and inversion.
Key Features
-------------
- Tabular data indexing and management (Pandas dataframes),
- Dictionary-like processing parameters configuration
- Programmatic or manual editing of inputs
- Largely automated workflow
Documentation for the Aurora project can be found at http://simpeg.xyz/aurora/
Installation
---------------
Suggest using PyPi as the default repository to install from
``pip install aurora``
Can use Conda but that is not updated as often
``conda -c conda-forge install aurora``
General Work Flow
-------------------
1. Convert raw time series data to MTH5 format, see `MTH5 Documentation and Examples `_.
2. Understand the time series data and which runs to process for local station `RunSummary`.
3. Choose remote reference station ``KernelDataset``.
4. Create a recipe for how the data will be processed ``Config``.
5. Estimate transfer function `process_mth5` and out put as a ``mt_metadata.transfer_function.core.TF`` object which can output [ EMTFXML | EDI | ZMM | ZSS | ZRR ] files.