Using Data Science To Revolutionize Geological Logging

 

Using Data Science To Revolutionize  Geological Logging

To start:

The University of Western Australia (UWA) and Rio Tinto Iron Ore (RTIO) have entered into a four-year, $6.1 million research partnership to develop innovative data science solutions (artificial intelligence) for automated geological logging to improve mining practice.

Collaboration between UWA's Artificial intelligence team and RTIO

The partnership, which follows more than 10 years of collaboration between UWA's data science team and RTIO, will employ five full-time researchers and provide training opportunities for a number of industry-driven PhD programmes.

Dr Daniel Wedge, from (CDG) in UWA's School of Geosciences, said UWA's expertise will be resorted to help RTIO's mine geology team tackle the challenge of objective well geological materials.

 

Material composition, geomechanical metrics and their spatial distribution with artificial intelligence

"Until recently, geologist’s specialists had to manually interpret and document material found in core samples, a process that was time-consuming and challenging," Dr Wedge said.

"Our project can use artificial intelligence: machine learning, pc vision, spacial modelling and improvement techniques to integrate disparate borehole information, together with analysis, imagery, geochemical and natural science informationalong side chemical analysis, imagery, geochemical and earth science info, to."RTIO head Dr. Angus McFarlane said the past partnership between UWA and RTIO has led to the commercialisation of UWA's automated downhole image analysis software and three joint patent applications for RTIO-driven machine learning-based geological modelling.

"The UWA team has with success developed the bogus intelligence machine learning-based ways that within which and tools for material composition for stratigraphic analysis and resource assessment," said Dr. McFarlane." the foremost recent collaboration will align and extend variety of the advances in mining that ar serial section of resource assessment."

Professor Eun-Jung Holden, head of the CDG and UWA Data Institute, said that, like previous work, this new project involved advanced artificial intelligence data science, a geological understanding of the data and RTIO's extensive knowledge of the mining area.

"Applying  based solutions  ml (artificial intelligence) tailored for industry is a huge defiance," said Professor Holden. " "As a research team, we've benefited greatly by integration with the sponsor's team, gaining access to their current a day observe.

Dr Tom Horrocks from the varsity of Geosciences aforesaid it's additionally improved our understanding of however end-user geoscientists method information at totally different stages, what they require and the way best to integrate these new solutions into one in larger systems. "

Tom Green, manager of natural science and water, research, development and technology at RTIO, aforesaid the project was vital for the long run.

"The UWA and RTIO groups have developed a culture of mutual respect, cooperation, and mutual profit," aforesaid Zhihao Wang. "Through this partnership, we have a tendency to ar currently at the forefront of exploitation information science to remodel earth science interpretation."

Instructions at the highest of the page: The Data-Driven Geosciences Centre team (left to right): Dr Tom Horrocks; mister David Nathan; Dr Ming Tran; professor Eun-Jung Holden; Dr Chris Gonzalez; mister Luke Smith; Dr Daniel Wedge; mister Abel Janszoon Tasman Gillfeather-Clark.