README.rst
July 26, 2024 ยท View on GitHub
.. image:: docs/figures/aurora_logo.png :width: 900 :alt: AURORA
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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 <https://github.com/kujaku11/mth5>, which in turn leverages the general mt-metadata <https://github.com/kujaku11/mth5> 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
- Convert raw time series data to MTH5 format, see
MTH5 Documentation and Examples <https://mth5.readthedocs.io/en/latest/index.html>_. - Understand the time series data and which runs to process for local station
RunSummary. - Choose remote reference station
KernelDataset. - Create a recipe for how the data will be processed
Config. - Estimate transfer function
process_mth5and out put as amt_metadata.transfer_function.core.TFobject which can output [ EMTFXML | EDI | ZMM | ZSS | ZRR ] files.