Spring 2022 Pawsey update

We’re glad to contribute again to MRO News while we embrace the new season of Kambarang, the Noongar season of birth and wildflowers.

In mid-September, we welcomed a group of students from the Pia Wadjarri Remote Community School. It was a special visit as the students saw first-hand the destination of the vast amount of data collected by the telescopes at the Murchison Radio-astronomy Observatory on their Country.

Students looking at the rear of a tall black rack containing supercomputing parts. The view is mostly of rainbow braided cables all bunched together. A woman in a white and red/orange shirt with Aboriginal dot painting on the sleeves is explaining the cabling and pointing to a large bunch.

Exploring the insides of a supercomputer. Look at all those cables! Credit: Beth Cloake.

Two young students looking through Perspex windows in the flooring watched over by a woman in a white and red/orange shirt with Aboriginal dot painting on the sleeves.

Looking beneath the floating floor at Pawsey. Credit: Beth Cloake.

Students had an afternoon at Pawsey where they saw the massive supercomputers that process the data the telescopes collect, a range of astronomy-based activities with our colleagues at CSIRO and each walked away with their very own stuffed toy Quokka!

It was an absolute pleasure to host the Pia students at Pawsey and we welcome schools near or far (from Perth) to reach out to our team to organise a personalised tour of the Centre.

Apart from astronomy, we support research in a wide range of areas including data from mines that can be used to save lives. Hundreds of open-pit mines exist in Australia. The stability of their pit walls can be a life-or-death issue for on-site workers.

A large pit in red dirt with stepped edges.

Drill and blast rigs (Open-pit iron-ore mine, Pilbara, Western Australia). Credit: Dr Daniel Goldstein, Curtin University.

A greyscale image of a large dirt pit with a red circle over a hazard are identified by machine learning. Rainbow coloured strips are inset in the bottom right showing the data the machine learning algorithm used to identify the hazard.

Geotechnical hazard predicted by machine learning analytical techniques from Measure-While-Drilling blast rig data (Open-pit coal mine, Bowen Basin, Queensland). Credit: Dr Daniel Goldstein, Curtin University.

A Curtin School of Mines doctoral candidate, Daniel Goldstein, is working with Pawsey to develop ways to reduce the risk of pit wall failure by providing drillers feedback in real-time.

Daniel started his work on his personal computer. But as his data sets reached tens of thousands of points, he reached out to Pawsey.

If the mining industry adopts Daniel’s model, it has the potential to reduce the risk of injuries and fatalities at open-pit mines. By being able to characterise earth composition more accurately while drilling, it could reduce operational expenses and save lives.

Read the full case study.

Mark Stickells, Executive Director, Pawsey Supercomputing Research Centre