Using deep learning, we worked with miner IGO to revolutionise the minerals exploration process via AI-generated prospectivity heatmaps


Our challenge was to develop prospectivity heatmaps from IGO’s minerals exploration data for the Albany Fraser range in Western Australia


The collaboration between Aurizn's data science team and IGO's minerals exploration experts aimed to improve the mineral exploration process by applying deep learning techniques to a large survey area of over 30,000km². The geospatial datasets used included ground gravity, airborne magnetic, and geochemistry data from over 9,000 drill holes with elemental assays.


The data analytics process involved interpolating geophysics to a 10m grid and converting geochemistry to prospectivity binary labels using provided elemental thresholds and ratios.


The machine learning components included unsupervised clustering of similar geophysics and supervised deep learning models using expert-derived binary prospectivity labels.


The model was trained using geophysics data as inputs and prospectivity as outputs on 80% of drill holes. The model's performance was tested and verified on 20% of drill holes, with qualitative assessment from known prospects and geochemist feedback. Multiple algorithms and visualisations were employed to ensure the model's robustness.


Regular feedback from the exploration team was crucial for geological interpretation, culminating in the delivery of GeoTIFF heatmaps indicating high prospectivity regions for IGO's drilling efforts. The heatmap provided a colour scale covering full confidence range (0-1) and a narrowed confidence range (e.g., 0.5-1) to highlight only the more likely prospective areas.


This machine learning pipeline has been established for the next survey area, streamlining the process for other greenfields and brownfields exploration.


The development of this simple tool provides numerous benefits for mineral exploration, including:


Drilling targets - By identifying high prospectivity regions, the tool avoids pattern drilling effort and cost.


Increased speed and likelihood of finding an ore body - By leveraging machine learning, the tool accelerates the exploration process and enhances the probability of discovering ore bodies.


Rapid ROI potential - The small investment in AI is much less than 1% of the cost to collect and analyse data, while one discovery can pay back thousands of times over.


The multi-dimensional spatial data can be summarised in one heatmap, allowing both technical and non-technical audiences to easily interpret the results.


This innovative approach resulted in Aurizn (as Consilium Technology) being awarded the 2020 Energy & Mining Premier's Award in the Engineering Technology Services (ETS) sector (Innovation & Collaboration category) for our work in Minerals Exploration with Artificial Intelligence.

Case Studies

We have a strong reputation for taking on complex challenges. Find out more about the ground breaking work and projects we’ve undertaken.

  • All
  • Enterprise
  • Defence
In collaboration with Veracio (formerly Boart Longyear) we developed the TruStructure application — a cutting-edge, web-based platform for structural and geotechnical logging.
man holding a tablet in a vineyard
Wine Australia
We used Machine Learning and high resolution satellite imagery to scan the Australian continent for the location of vineyards.
digital flare helicopter model
Digital Flare Model
Aurizn developed a physics-based and validated Digital Flare Model for DSTG to enable simulated testing of scenarios to improve platform survivability.
active moment apparatus
Active Movement Extent Discrimination Apparatus: Technology Refresh
We ran a technology refresh to the Active Movement Extent Discrimination Apparatus, which is used by NASA to support research into astronaut rehabilitation strategies to counter long term effects of micro-gravity exposure.
defence helicopter with military inside
Electro Optic Survivability Integration Laboratory (EOSIL)
We supported DSTG with a technical refresh of its turn-key high-precision test and evaluation capability for Threat Warning Systems to support more efficient survivability assessments.
Essential Energy
Essential Energy – Vegetation Monitoring
By improving risk profiling of vegetation using remote geospatial sensing and AI, we helped power distributors improve electricity supply to millions across their network.
aerial view of a cityscape
Cross Domain Desktop Compositor
Working closely with CSIRO, we developed a new cross domain desktop compositor to advance data security for Defence.
TCS - soldier
Tactile Cueing System
We accelerated the technology maturity of a Tactile Cueing System from TRL 3 to TRL 7, resulting in improved outcomes for pilots when hovering.
defence helicopter flying over water
Electromagnetic Spectrum Synthetic Environment
We supported continuous evaluation of Electronic Warfare Self Protection (EWSP) systems and Tactics, Techniques and Procedures (TTPs) to improve effectiveness.
military tactics development with firearm
Tactics Development Experimentation
Through the AWAKENING Series of Experiences, Aurizn provided the ADF with insights into improving aircraft capabilities and platform survivability against land-borne ballistic weapons.
igo prospectivity chart
Using deep learning, we worked with miner, IGO, to revolutionise the minerals exploration process via AI-generated prospectivity heatmaps.