Mining Data: Data in the Mineral Mining Industry

Published 24 Jun 2021

By Gordon Huang

The age-old mining industry supplies invaluable natural resources on which our modern technology and infrastructure depend. To meet the ever-increasing global demand for natural resources such as metal, oil, battery lithium and nuclear uranium fuel, mining firms undergo monumental projects in highly unpredictable economic and natural environments. In this article we will explore how the mining industry leverages data to maximise the efficiency and productivity of their operations.

Mining is a complex and fluctuating industry with many critical variables that need to be forecasted or strategically optimised to sustain the business. High-frequency, accurate predictive analysis using big data is being used to help keep mining businesses running efficiently and profitably. GlobalData predicts that mining firms’ spending on AI platforms will reach $218m by 2024. This is up from $76m in 2019, and represents an annual growth of 23.5%. Let’s explore some of the main ways in which data is being leveraged for greater efficiency and productivity in the mining industry.

1. Identifying optimal mineral deposits

Satellite image of the Morenci copper mine

ASTER satellite image of the Morenci copper mine in Arizona. Multispectral imaging of this kind allows deep learning algorithms to deduce geological properties.

Recent developments in geophysical imaging technologies are being used to identify the richest and least obstructed areas for mining. Autonomous aircraft are able to collect high resolution radiometric and magnetic data at inexpensive rates. Many startups are applying standard deep learning algorithms on public multispectral satellite imaging data to classify potential exploration targets for mineral mining.

Data collected using other techniques such as from the drill samples of an area are being used to determine suitability of the ore body, with state-of-the-art computer vision techniques being readily applicable to this data. Researchers have used convolutional neural networks to estimate the unconfined compressive strength, cohesion and internal friction angle of different rock types.

Goldspot's deep learning analysis of drill cores

Goldspot’s deep learning analysis of drill cores to distinguish mineral veins from rock

Having high signal data that provides a sound understanding of the terrain is critical for making effective data-driven decisions to maximise the productivity of any mining project.

2. Predictive equipment maintenance

Port Hedland iron ore port

Port Hedland iron ore port in Western Australia

Data on the lifetime of machines and equipment are used to predict malfunctions and obsolescence ahead of time. This enables companies to invest in the correct repairs and replacements when it is needed, rather than responding to problems as they arise. Malfunction history of equipment using many data points can be used to identify systematic problems in machinery and inform preventative measures. This also dramatically improves onsite safety.

3. Robotics and autonomous vehicles

Komatsu's autonomous mining haul trucks

Komatsu’s autonomous mining haul trucks

Many transport systems between the mineral deposit and the processing plant have been made driverless and remote to improve both efficiency and safety of mining operations. In particular, data collected about these transport systems are used to organise vehicle dispatch and reduce queueing of autonomous haul trucks and long-distance rail systems. Small improvements in these logistics produce significant gains in productivity over time.


From scouting potential high-yield mineral deposits to optimising equipment maintenance and logistics, data plays a key role in making all stages of mining projects more efficient and more productive. Future developments in data collection technologies and deep learning will bring unequivocally prosperous benefits to a timeworn yet imperative industry of the modern world.

Tags: Data Science Machine Learning, Deep Learning and Neural Networks Applications of Data Science