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Webinar - Geological Knowledge Discovery using Machine Augmented Intelligence.

Event Type

Event Date

Thursday, January 28, 2021

Event Location

Event Address


Event Start

1300 AEDT

Event End

1400 AEDT

Event Details

We have another exciting year of ASEG webinars kicking off next week with a new webinar on Thursday 28 January, 1pm AEDT by Professor Eun-Jung Holden from the University of Western Australia on Geological Knowledge Discovery using Machine Augmented Intelligence.

Geological interpretation is a complex task where an interpreter’s bias plays an important role. As a result, interpretation outcomes are variable and uncertain, but nevertheless, these outcomes form the basis of decisions with significant environmental, social and financial implications. With the increasing use of artificial intelligence and machine learning in our daily lives such as for information search, online shopping, and virtual assistant AI, the geoscience domain has also been active in the uptake of machine learning and AI to assist in interpreting geology from data.

This talk presents innovative machine-assisted technologies that improve the efficiency and the robustness of geological interpretation of different types of geodata used in the resource industry. A number of applications of machine learning were developed in collaboration with the mining industry for the analysis and integration of multi-modal drill hole data. These applications integrate the algorithms and workflows to assist human decisions. The approach is to provide end users the control of the algorithmic process as much as possible; and to enable a seamless integration of algorithms in the interpreter’s workflow using interactive visualisation. This talk also presents an on-going AI research that extracts geological insights from documents using machine reading of text. It applies advanced text mining methods and constructs a graph based knowledge base called a knowledge graph to store and access geological information. Case studies on different mineral deposits demonstrate the effectiveness of the methods for rapidly and robustly transforming text data into structured information that faithfully represents the contents of the source reports.


Professor Holden received her BSc, MSc and PhD in computer science from the University of Western Australia.  Her postgraduate and postdoctoral research focused on developing visualisation, automated image analysis and machine learning techniques for hand gesture recognition.  Then in 2006, she made a transition to geoscience and currently leads the Geodata Algorithms Team at UWA.  The team effectively spans the boundaries of computational science and geoscience and links academia and industry.  The team’s research resulted in the commercialisation of three software products, namely CET Grid Analysis and CET Porphyry Detection extensions for Oasis Monaj; and televiewer image analysis methods in the Image & Structure Interpretation workspace for ALT’s WellCAD.  These products had significant uptake by the resource industry globally.   Recently, their research also resulted in two industry driven patents on machine assisted drillhole data interpretation methods.  Professor Holden currently leads a major industry funded research engagement named the UWA-Rio Tinto Iron Ore Data Fusion Projects.  Her team won the UWA Vice Chancellor Award in Impact and Innovation in 2015 and she was a winner of the Women in Technology in WA (WiTWA) Tech [+] 20 Awards in 2019.


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