.. 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

1. Convert raw time series data to MTH5 format, see MTH5 Documentation and Examples <https://mth5.readthedocs.io/en/latest/index.html>_.
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.