1887
2nd Australasian Exploration Geoscience Conference: Data to Discovery
  • ISSN: 2202-0586
  • E-ISSN:

Abstract

Summary

Large geophysical data has traditionally been difficult to manage in a consistent, open, and efficient manner. The demands of modern, large-scale computing techniques, coupled with the need for sound data and metadata management, mean that established data formats and access methods are no longer adequate.

Geoscience Australia (GA) has been working with its partners to leverage and extend existing data standards to represent various geophysical data in modern scientific container formats including netCDF & HDF. The new data encodings support rapid and efficient data subsetting, either directly from a file or remotely via web services. These will underpin GA’s future data delivery pipelines for Australian government-funded geophysical data.

NetCDF efficiently handles multi-variate raster, line, and point data, as well as n-dimensional data structures supporting more demanding applications such as AEM and airborne gravity data. Structural and metadata standards deliver interoperability, and existing and emerging data types are supported without loss of precision or other information.

This extended abstract will cover:

  1. The rationale for Modernising GA’s geophysical data holdings into modern open standard container formats
  2. An outline of the netCDF4 file format and associated tools, and some of the benefits they provide
  3. The open-source tools and methodology used to translate grid, line, point and other data into netCDF4, and to perform metadata synchronisation
  4. A brief description of a live use case exploiting web services

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/content/journals/10.1080/22020586.2019.12073191
2019-12-01
2026-01-19
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References

  1. ASEG 2003, The ASEG-GDF2 Standard for Point-Located Data, Australian Society of Exploration Geophysicists Standards Committee, viewed 10 April 2019, <https://www.aseg.org.au/sites/default/files/pdf/ASEG-GDF2-REV4.pdf>
  2. CF Metadata 2018, NetCDF Climate and Forecast (CF) Metadata Convention, CF Conventions, viewed 10 April 2019, <http://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/cf-conventions.html>
  3. ESIP 2017, Attribute Convention for Data Discovery 1-3, ESIPFed Wiki, viewed 10 April 2019, <http://wiki.esipfed.org/index.php/Attribute_Convention_for_ Data_Discovery>
  4. Force11 2017, The FAIR Data Principles, Force11, viewed on 11 April 2019, <https://www.force11.org/group/fairgroup/fairprinciples>
  5. Geoscience Australia 2019, Geophys Utils GitHub Library, Geoscience Australian GitHub Library, viewed 10 April 2019, <https://github.com/GeoscienceAustralia/geophys_utils>
  6. Geoscience Australia 2018, A NetCDF Ground Gravity Point Surveys Collection, Geoscience Australia, viewed 10 April 2019, <http://dx.doi.org/10.26186/5c1987fa17078>
  7. ISO 2016, ISO/TS 19115-3:2016 Geographic information -- Metadata -- Part 3: XML schema implementation for fundamental concepts, International Standards Organisation, viewed on 10 April 2019, <https://www.iso.org/standard/32579.html>
  8. MT Geophysics Australia 2019, MTpy GitHub Library, MT Geophysics, viewed on 10 April 2019, <https://github.com/MTgeophysics/mtpy>
  9. OGC 2019, OGC Standards and Supporting Documents, The Open Geospatial Consortium, viewed 10 April 2019, <https://www.opengeospatial.org/standards>
  10. OPeNDAP 2019, Open-source Project for a Network Data Access Protocol, OPeNDAP Advanced Software for Remote Data Retrieval, viewed 10 April 2019, <https://www.opendap.org/>
  11. Seismic Data 2015, ASDF - Adaptable Seismic Data Format, ASDF Definition, viewed 10 April 2019, <https://asdf-definition.readthedocs.io/en/latest/>
  12. The HDF Group 2019, The HDF5 Library and File Format, The HDF Group, viewed 10 April 2019, <https://www.hdfgroup.org/>
  13. UCAR 2019, Network Common Data Format (NetCDF), Unidata Data Services and Tools for Geoscience, viewed 10 April 2019, <https://www.unidata.ucar.edu/software/netcdf/>
  14. UCAR 2013, Chunking Data: Why it Matters, Developers at Unidata - Data Services and Tools for Geoscience, viewed 10 April 2019, <https://www.unidata.ucar.edu/blogs/developer/en/entry/chun king_data_why_it_matters>
  15. Wikipedia 2019, Catalog Service for the Web, Wikipedia, viewed on 10 April 2019, <https://en.wikipedia.org/wiki/Catalog_Service_for_the_Web>
  16. Webopedia 2019, Web Services, By Vangie Beal, viewed on 10 April 2019, <https://www.webopedia.com/TERM/W/Web_Services.html>
  17. Wyborn L, and Evans B, 2015, Integrating ‘Big’ geoscience data into the petascale national environmental research interoperability platform (NERDIP): Successes and unforeseen challenges, 2015 IEEE International Conference on Big Data (Big Data), DOI:10.1109/BigData.2015.7363981
/content/journals/10.1080/22020586.2019.12073191
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  • Article Type: Research Article
Keyword(s): data; geophysics; HDF; metadata; netCDF; web services
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