The Geosciences DeVL Experiment: new information generated from old magnetotelluric data of The University of Adelaide on the NCI High Performance Computing Platform
The AEGC is the largest petroleum and mineral geoscience conference in Australasia, and incorporates the West Australian Basin Symposium (WABS) and the ASEG-PESA International Geophysical Conference and Exhibition.
The theme for the 2019 conference is “Data to Discovery”. The AEGC technical program committee has a focus on Geology, Geophysics, and Geochemistry and how these are applied in exploration for both Petroleum and Mineral systems in Australasia and the wider Asia-Pacific region.
In recent years, magnetotelluric (MT) processing has become computationally intensive as the scale and size of MT surveys being run increases. Consequently, High Performance Computing (HPC) is now becoming a valuable tool for timely processing and modelling of these large MT datasets. As part of the MT component of the 2017-2019 Australian Research Data Commons (ARDC) funded Geoscience Data Enhanced Virtual Laboratory (DeVL) continuity project, The National Computational Infrastructure (NCI) at the Australian National University will enable MT datasets from The University of Adelaide to be added to the NCI HPC platform with the goal of creating a more Findable, Accessible, Interoperable, Reusable (FAIR) and open public resource.
A focus will be on making the time series datasets more suitable for use on HPC and more interoperable with other Earth science disciplines, where High Performance Data (HPD) formats will allow for better scalability and performance. Metadata attributes, as defined by the Australian MT research community, will be added directly to the time series data files. Additionally, time series processing and 3D inversion codes are being optimised for HPD/HPC, with the end goal of rapid time series processing and 3D inversion.
Making FAIR MT time series available on HPC can lead to a transformative change in the way MT data analysis is routinely conducted and such a change has the capacity to create new ways of doing collaborative and transparent MT analysis.