Data

Magnetotelluric surveys of the AusLAMP Musgraves Province within Western Australia, 2016 to 2018 (Level 1 concatenated, resampled, rotated NetCDF time series)

National Computational Infrastructure
Graham Heinson ; Data Collections Team
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25914/xx26-b740&rft.title=Magnetotelluric surveys of the AusLAMP Musgraves Province within Western Australia, 2016 to 2018 (Level 1 concatenated, resampled, rotated NetCDF time series)&rft.identifier=10.25914/xx26-b740&rft.publisher=NCI Australia&rft.description=As part of the Australian Lithospheric Architecture Magnetotelluric Project (AusLAMP), long-period magnetotelluric time series data were collected in the Musgraves Province between 2016 and 2018. Level 1 concatenated, resampled, rotated NetCDF time series data from Earth Data Logger sites located in Western Australia are presented.This record was harvested by RDA at 2023-03-19T13:00:26.5+11:00 from NCI's Data Catalogue.Python was used for the generation of the Level 1 concatenated, resampled, rotated NetCDF time series products. The generation of these files was done in two stages: Stage 1: The raw time series were checked for gaps in continuity and if gaps were found, the problematic stations were segregated for stage 2 processing. The stations that passed the initial checks were merged into concatenated (over all days of recording) ASCII files (EX, EY, BX, BY, BZ). These ASCII files were then converted into a temporary binary file to accelerate the subsequent I/O operations. The binary data were read in, downsampled to 1 Hz and rotated by the rotation angle in the header metadata. The outputs are converted into a single NetCDF file per station and all the metadata attributes (based on https://doi.org/10.1080/14432471.2019.1600210) are added to the header. Stage 2: For those stations that did not pass the initial continuous time series test, the user manually determined which days had continuous time series without any gaps. The stage 2 processing then performs the same operations as the stage 1 processing, but only on the selected days.&rft.creator=Graham Heinson &rft.creator=Data Collections Team &rft.date=2023&rft.edition=v1&rft_rights= https://creativecommons.org/licenses/by/4.0/&rft_rights=Creative Commons Attribution 4.0 International&rft_subject=geoscientificInformation&rft_subject=magnetotellurics, MT, electrical resistivity, time series, long period, impedance, AusLAMP, Western Australia, WA&rft.type=dataset&rft.language=English Access the data

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Creative Commons Attribution 4.0 International

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Brief description

This record was harvested by RDA at 2023-03-19T13:00:26.5+11:00 from NCI's Data Catalogue.

Full description

As part of the Australian Lithospheric Architecture Magnetotelluric Project (AusLAMP), long-period magnetotelluric time series data were collected in the Musgraves Province between 2016 and 2018. Level 1 concatenated, resampled, rotated NetCDF time series data from Earth Data Logger sites located in Western Australia are presented.

Lineage

Python was used for the generation of the Level 1 concatenated, resampled, rotated NetCDF time series products. The generation of these files was done in two stages:

Stage 1:
The raw time series were checked for gaps in continuity and if gaps were found, the problematic stations were segregated for stage 2 processing. The stations that passed the initial checks were merged into concatenated (over all days of recording) ASCII files (EX, EY, BX, BY, BZ). These ASCII files were then converted into a temporary binary file to accelerate the subsequent I/O operations. The binary data were read in, downsampled to 1 Hz and rotated by the rotation angle in the header metadata. The outputs are converted into a single NetCDF file per station and all the metadata attributes (based on https://doi.org/10.1080/14432471.2019.1600210) are added to the header.

Stage 2:
For those stations that did not pass the initial "continuous time series" test, the user manually determined which days had continuous time series without any gaps. The stage 2 processing then performs the same operations as the stage 1 processing, but only on the selected days.

Created: 15 12 2022

Issued: 20 02 2023

Modified: 20 02 2023

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