The mining industry is rapidly evolving, with the integration of data and advanced modelling techniques ushering in a new era of collaboration and efficiency across the mining value chain.
But with more data, people, and resources at their disposal, geological teams encounter a major challenge — how to collaborate effectively.
Traditionally, geological workflows have often been marred by isolated functions and fragmented siloed groups. This lack of transparency has hampered productivity, costing mining companies time and money.
Here, we take a look at how modern technologies, processes, and workflows enhance mining productivity, rather than scupper it, as well as some of the tools you need to drive projects forward effectively and efficiently.
Challenge #1: Siloed teams and systems
Successful ore extraction relies on inter-disciplinary collaboration. Geologists in exploration and production must form a symbiotic relationship, merging their expertise to unravel the complexities of ore bodies. This requires seamless workflows that integrate diverse data sources and technologies for confident orebody knowledge.
As well, mining technology has ushered in an era of detailed data acquisition, from LiDAR scans to 3D imaging, promising benefits including enhanced grade control, reduced costs, and increased productivity. Imagine real-time updates and insights on ore body dynamics through IoT data seamlessly feeding into continuous modelling processes from face mapping. This integration not only streamlines operations — it also ensures a smooth handover to production engineers, optimising productivity from extraction to mill recovery and minimising any uncertainty.
But integrating these digital workflows company-wide is proving challenging for many mining organisations, and the smarter, faster and seamless geoscience automation that should be integrated within these workflows is still lagging behind.
Why? The current landscape sees many mining companies purchasing technologies from various vendors, leading to compatibility issues, the emergence of silos, and disjointed workflows. Industry-wide standardisation hurdles further complicate the adoption of unified digital workflow solutions.
How to remove data siloes and compatibility issues with integrated software
Mining companies can eliminate silos by viewing their technology at each stage of the project as part of a much larger picture. Connected workflows that allow data to move between software without compatibility issues or time-consuming manual imports are vital for aiding near-real-time modelling and providing key, up-to-the-minute insights for more informed decision-making.
Ultimately, data from a variety of sources must be incorporated into a geological model throughout exploration and a mine’s operation. Looking at the larger picture means ensuring these data sources can easily connect to your geological modelling software.
Leapfrog Geo, for example, enables dynamic multisystem workflows through its native connections. 3D photogrammetric imagery data from Imago can be seamlessly incorporated in geological models, as well as drilling and sample data from the SaaS data management platform, MX Deposit.
Leapfrog Geo’s connection to Seequent Central ensures models can be cloud-hosted, enabling team members to work individually and apply updates to the mast model, ensuring the entire project team is working from a single source of truth.
The result? Frictionless collaboration and accelerated subsurface understanding.
Having access to accurate, real time data on site sets your team up for success.
Challenge #2: Protecting data integrity
Geologists have historically spent the largest chunk of their time on manually preparing, checking, ingressing, and validating data. Today, geologists are changing the paradigm, keen to spend more time on improving their analysis, gaining deeper insights, and validating their models to be more effective.
But robust data governance has become even more important as mining companies increasingly adopt digital workflows to enhance operational efficiency. Robust data governance practices are essential not just for compliance with audit requirements, but also for ensuring the reliability, integrity, and usability of geological data throughout its lifecycle. It also ensures an improved connection between exploration and production; data governance for exploration data can help ensure insights are passed down the lifecycle all the way through to the mine geology and production stages.
How to protect data with data management technology and digital workflows
Data governance is vital in all mining projects, but especially those involving grade control, where one’s understanding of ore quality hinges on the integrity of the sample data being analysed.
Effective data governance requires clear procedures and policies – but these are only effective with proper guardrails in place and staff compliance enabled. This is where your data management software plays a critical role.
Data management platforms should serve as a single source of truth, accessible by all collaborators to ensure everyone is working from the most current information. While this accessibility alone reduces opportunity for error, platforms that include a clear audit trail, like MX Deposit, provide further peace of mind that data is being handled correctly across all projects.
But what about the tools used outside your data management platform in various stages of a project? When interdisciplinary teams work in separate, incompatible software tools, the manual transfer of data from one system to another introduces opportunities for human error or interference. Connected digital workflows can close this gap. For example, MX Deposit offers a public API that enables the software to integrate directly with labs so users can submit and receive QAQC results directly, minimising manual data handling.
Because platforms like MX Deposit are designed specifically for mining projects, they offer intuitive interfaces that collaborators can learn quickly, further reducing risk of error.
By placing a strong emphasis on data governance and embracing digital workflows, mining companies can navigate the complexities of their operations more effectively.
Challenge #3: Finding fit-for-purpose ML and AI technology
To keep up with competition and the pace of technological advancement, mining companies are continually pushing the envelope in the pursuit of sustainable and efficient mining operations. Machine learning (ML) and artificial intelligence (AI) are becoming increasingly ubiquitous in our personal lives, but how are they being successfully applied in mining projects?
There are many potential benefits for mining companies implementing ML or AI — from streamlining tasks and expediting decision-making to optimising resource allocation — which foster sustainable growth in today’s fiercely competitive landscape. Enhancing data collection and analysis with ML, for example, lets mining companies better leverage the wealth of data at their disposal more efficiently. This enables them to gain invaluable insights into optimising grade control, reducing uncertainty, and enhancing operational accuracy at an accelerated pace.
Your entire project team can work from a single source of truth.
But a crucial factor is to ensure that AI and ML algorithms are deeply rooted in geologically accurate principles, which requires geologists, software engineers, and data scientists to work closely together to ensure the algorithms’ calculations are geologically sound. This protects the integrity of geological interpretations and decisions, preventing potential inaccuracies or misinterpretations that could compromise operational efficiency and safety.
Without applying these contextual geological principles, or measuring freshly available data, there is a very real risk of misclassification and delayed decision-making, which could potentially undermine both efficiency and profitability.
Boosting productivity with ML and AI specifically designed for mining
Drilling is a data-heavy process that can be long and expensive, presenting an ideal opportunity for productivity-enhancing ML.
Imago AutoCrop, for example, leverages ML to automatically crop and linearise core tray imagery, speeding up the image capture and cataloguing process. This results in clean, consistent, and organised image data, which enhances the visualisation and analysis of diamond core imagery.
An example of Image Autocrop
Integration with Leapfrog Geo allows users to validate model data and support deeper orebody analysis by visualising downhole imagery alongside drillholes. AutoCrop also processes historic data, centralising it in a cloud-based portal for easy access and sharing. This standardisation and centralisation improves decision-making, saves time, and maximises investment outcomes by providing high-quality image data more rapidly.
Imago AutoCrop is just one example of how ML – when properly integrated into proven mining software – reduces the time from data collection to modelling, enabling quicker, more informed decisions that save money and improve drill campaign results.
Looking towards the future of mining productivity
The convergence of data integration, advanced modelling, and machine learning is reshaping the future of mining productivity.
Prioritising collaboration, connected workflows, robust data governance frameworks, and embracing emerging technologies such as AI and ML are the key steps mining companies must take to unlock unprecedented levels of efficiency, productivity, and profitability across the entire value chain.
This integrated approach not only streamlines operations – it also ensures accuracy, agility, and sustainability. As mining enterprises continue to adapt and innovate, they are poised to lead the way towards a future where productivity in mining reaches new heights, driving sustainable growth and success in an ever-evolving industry landscape.
Change management and adoption
But there is a common thread that connects all three challenges and stands in the way of progress — people.
Adopting change in the workplace is often challenging because it disrupts established routines and introduces uncertainty, which can in turn trigger stress and anxiety. We know that the brain tends to favor familiarity to conserve cognitive resources, making employees naturally resistant to altering their habitual practices.
Geological teams may have concerns about data quality, which may cause teams to maintain the status quo.
In a Seequent panel discussion with industry experts (access insights paper here), Matt Blattman, Director of Technical Services, Hecla Mining, spoke to the challenges around the adoption of new technology. Matt told the panel that “One of the things that kicks people out of a new workflow and back into the old one is a data quality or data outage” making them feel it’s not working or it’s inaccurate and then reverting to their old way of .
Overcoming this resistance requires robust personal interaction and comprehensive support from mining executives. But the rewards mean it’s worth the effort.