Processing Codes for Magnetotelluric Data
Project description
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
Convert raw time series data to MTH5 format, see MTH5 Documentation and Examples.
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_mth5 and out put as a mt_metadata.transfer_function.core.TF object which can output [ EMTFXML | EDI | ZMM | ZSS | ZRR ] files.
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