Last week I wrote about how mining companies are sitting on transformational value trapped in their data systems, and the comments that followed were fascinating. Most people agreed with the opportunity. But a recurring theme emerged: "That's all well and good, but what about the quality of the data in the first place?" Fair point. Because if you're going to build an AI-powered intelligence layer on top of decades of mining data, you'd better hope that data isn't complete rubbish. Recent industry surveys paint a confronting picture. The vast majority of mining professionals say data management is critically important to their organisation. Yet less than a third have an established framework for managing it. Most keep data "organised in various systems", which is corporate speak for scattered across a dozen folders, three legacy databases, and someone's USB stick. The historical data problem is particularly challenging. More than half the industry identifies unmanaged historical data as a significant challenge, yet only half feel confident their company can actually handle it properly. When you've got an average of 22 people touching datasets within an organisation and most companies can't reliably tell you who changed what, when, or why, you've got a recipe for expensive mistakes. In mining, decisions based on flawed geological data can lead to drilling in the wrong locations, overestimating reserves, or underestimating processing costs. Boards should be asking hard questions about this. So is fixing it actually possible? Yes. But it requires something the industry has historically resisted: discipline. The solution is in establishing clear data governance from the point of collection. Every piece of data needs provenance: who collected it, when, using what methodology, and what QA/QC processes were applied. The technology to automate most of this now exists. The barrier is cultural, not technical. Consider the alternative. You spend millions on an AI platform, hire a data science team, and build predictive maintenance models, only to discover your insights are based on assay results that were transcribed incorrectly in 2009. The billions sitting in server rooms that I mentioned last week? They're only accessible if the data is trustworthy. That’s why we aim to built our approach around data integrity from the start. AI technology must validate, cross-references, and flag anomalies before decisions are made. Because the most sophisticated algorithm in the world is worthless if it's trained on low quality data. Cleaning historical data and establishing proper frameworks isn't glamorous. It won't make headlines at mining conferences. But it's the foundation upon which every other digital transformation initiative depends.

[PT] Unindo dados de fragmentação, o trabalho compara cópulas e VAEs para gerar dados sintéticos. O melhor desempenho vem de um VAE com mistura de gaussianas, que guarda relações mais complexas entre variáveis. Em resumo, quando o dado é pouco e não linear, geradores mais expressivos funcionam melhor. Isso ajuda a treinar modelos que preveem granulometria com mais confiança. [EN] Merging small fragmentation datasets, this work compares copulas and VAEs for synthetic data. A VAE with Gaussian mixtures works best, keeping more complex relationships. In short, with little, non-linear data, more expressive generators perform better. That helps train models that predict particle sizes more reliably.

[PT] Quando faltam dados reais, o relatório mostra como criar dados sintéticos mais realistas com VAEs em vez de suposições simples. Ele ensina como avaliar se o sintético parece o real e se melhora o treino dos modelos. É útil para temas como vibração e fragmentação, em que medir tudo é caro. Assim, dá para treinar ML com mais qualidade. [EN] When real data are scarce, this report shows how to generate more realistic synthetic data using VAEs instead of simple assumptions. It explains how to check realism and whether the synthetic data improve training. It’s useful for vibration and fragmentation, where measurement is costly. A practical way to train ML with better inputs.

In the contemporary mining industry, data management has emerged as a strategic pillar that determines the success and sustainability of operations. Mining companies today generate vast amounts of data from exploration, drilling, blasting, hauling, processing, and environmental monitoring. When properly harnessed, this information becomes a valuable resource that drives decision-making, risk management, and long-term planning. 1. The Evolution of Data Management in Mining Traditionally, mining relied heavily on manual record-keeping and periodic field reports. However, the rise of digital transformation has revolutionized how data is captured, stored, and analyzed. Advanced technologies such as geospatial mapping systems, IoT sensors, drones, and real-time monitoring tools now allow for continuous data collection throughout the mine’s life cycle. These digital innovations not only enhance operational transparency but also improve predictive capabilities for safety and productivity. 2. Integration of Data Across the Mining Value Chain Effective data management ensures integration between departments that historically operated in isolation. Geological data can now be linked with geotechnical, hydrological, and production data to build a comprehensive view of the mine’s performance. Through centralized databases and cloud-based platforms, managers can make data-driven decisions faster, optimize resources, and respond proactively to challenges such as equipment failures or unexpected geological variations. 3. Data Analytics and Decision-Making The use of Big Data analytics and machine learning has brought a new dimension to mining intelligence. Predictive models can forecast ore grades, equipment maintenance needs, and even environmental impacts. Artificial intelligence (AI) tools analyze trends from large datasets, offering insights that human observation might overlook. This transition from reactive to predictive management reduces downtime, lowers costs, and enhances overall efficiency. 4. Data Management for Environmental and Regulatory Compliance Sustainability and compliance are central to modern mining operations. Data management systems track environmental parameters such as water quality, tailings stability, air emissions, and land rehabilitation. Accurate reporting ensures compliance with local and international mining regulations, helping companies maintain their social license to operate. Furthermore, by making environmental data transparent and traceable, mining firms build trust with regulators, investors, and host communities. 5. Challenges and the Path Forward Despite its benefits, data management in mining faces challenges such as data fragmentation, lack of standardization, cybersecurity risks, and skill gaps among personnel. To overcome these, companies must invest in training, secure digital infrastructure, and standardized reporting systems. Collaboration between IT experts, geologists, and engineers is also essential to ensure that data serves its true purpose improving operational outcomes. Conclusion Data management is more than just a technical process it is the lifeblood of modern mining operations. By integrating accurate, accessible, and actionable data, mining companies can enhance efficiency, reduce environmental risks, and achieve sustainable growth. As the global demand for minerals continues to rise, those who leverage data intelligently will lead the next generation of responsible and resilient mining. https://en.wikipedia.org/wiki/Data_management%20Additional%20References:%20•Mining%20Journal%20(2023).%20Digital%20Transformation%20in%20Mining.%20•World%20Bank%20Report%20(2022).%20Data-Driven%20Sustainability%20in%20the%20Extractive%20Sector.

Ce document classifie et catégorise les différents Indicateurs Clés de la Performance des Opérations and une gestion éfficace des données. Les indicateurs clés de performance: Outils de mesure de la performance.

Artificial intelligence (AI), the capability of computational systems to perform tasks typically associated with human intelligence, is without question one of the most far-reaching and impactful technological developments of the 21st century. From chatbots, online shopping recommendations and financial risk assessment to analysis of customer data, artificial intelligence is a game-changer for industries large and small. One industry rarely associated with artificial intelligence is mining; yet the nexus between AI and this industry—especially for Latin America—deserves increased attention, given its profound impact in both developed and emerging markets. For example, AI is capable of analyzing large amounts of data and providing digital solutions for businesses, with the technology helping to increase speed and safety in mining operations Mining is the backbone of the natural resources sector in Latin America with Chile, Peru, Brazil and Mexico the dominant players. The mining sector produces revenue exceeding $110 billion, accounting for 21% of the global base metal mining market, with copper, iron ore, and gold leading the way. The positive impacts of AI on the mining sector are threefold. First there is increased efficiency and cost reduction. AI-driven systems can predict when mining equipment will fail, reducing downtime and maintenance costs. For example, Rio Tinto, the British-Australian multinational, uses AI to monitor its fleet of autonomous haul trucks, reducing maintenance costs by up to 15%. Second is the factor of improved safety. AI can analyze environmental conditions to detect risks such as gas leaks, rockfalls, and structural failures. Take Newmont Goldcorp. The firm uses AI in underground mines to monitor for hazardous conditions. Finally, there is the impact of enhanced sustainability and compliance. AI improves ore detection and sorting, reducing waste and environmental impact. A good example is TOMRA’s AI-based ore sorting technology reduces water and energy usage in mines by 30%. Recognizably, AI also produces negative impacts in mining. High costs and barriers to adoption immediately come to mind. AI systems require significant upfront investment in hardware, software, and training. For example, the estimated cost of AI-powered autonomous truck fleets is $5 million–$10 million per vehicle. Another downside is workforce displacement. AI-driven automation reduces the need for human labor, particularly in routine mining jobs. In the case of Fortescue Metals Group’s transition to autonomous trucks, the company eliminated over 1,000 jobs in Australia. Many of the remaining workers needed to be reskilled. In general, workers must be retrained to manage AI-based systems, but many companies lack training programs. Also, of increasing concern surrounding AI are data privacy and cybersecurity risks. AI systems in mining operations are potential targets for cyberattacks, which could disrupt production. Three years ago a cyberattack on a mining company in Canada temporarily shut down operations. Not to be overlooked are ethical concerns enveloping AI. AI-driven decision-making in resource allocation and environmental compliance can raise ethical issues if not properly regulated. At a country level, Colombia provides an excellent case of AI applications. For example, wearable devices integrated with AI can monitor the health and safety of miners; and AI can be employed for the early prediction of potential methane explosions in underground coal mines. The 2021 explosion of the Tópaga mine, where 12 miners lost their lives, was reconstructed using AI with evidence revealing that the use of individual methane detectors could have displayed data alerting miners to potential risks. An increasing number of mining companies in Colombia are embracing AI. Drummond, one of Colombia’s largest coal producers, is using AI to enhance operational efficiency and safety. The company has been incorporating AI in predictive maintenance for mining equipment and optimizing the coal extraction process. Cerrejón, a major coal mining company in Colombia, has been experimenting with AI technologies to improve resource extraction efficiency and sustainability. And EPM (Empresas Públicas de Medellín), a Colombian energy and mining company, has implemented AI solutions in their mining operations, particularly in their hydroelectric and mineral extraction projects. The widest use of AI is for predictive maintenance, equipment monitoring, and operational optimization. What does the future hold for AI in the mining industry—for Latin America and beyond? As infrastructure improves, more mines in Africa, Latin America, and Asia will adopt AI to enhance efficiency and safety. Companies will use AI to discover new mineral deposits, reducing exploration costs by up to 50%. For example, GoldSpot Discoveries uses AI to identify gold deposits with high accuracy. We can also expect government to push AI-driven sustainability solutions to reduce mining’s environmental footprint; and more sophisticated AI-driven robots will handle complex mining tasks in hazardous environments. In essence, AI will continue to revolutionize the mining industry, enhancing efficiency, safety, and sustainability by automating tasks, optimizing operations, and improving decision-making through data analysis and predictive analytics. By 2035, AI is expected to generate over $100 billion in efficiency gains for the mining sector, with developed markets leading in automation and emerging markets catching up through AI-driven exploration and ore processing innovations. In resource-rich regions like Latin America, mining firms like Glencore, Zijin Mining Group, BHP, and AngloGold Ashanti are well-positioned to harness AI to the benefit of their shareholders and the public at large. ___________________________________________________________________________________________________ Jerry Haar is a professor of international business at Florida International University. He is also a fellow of the Woodrow Wilson International Center for Scholars in Washington, D.C., and the Council on Competitiveness. Eva Cristina Manotas R. is dean of the School of Mines and Full Professor at Universidad Nacional de Colombia. (This article first appeared in Latin Trade on March 31, 2025).

In the mining industry, the terms "Digitalization" and "Automation" are often used interchangeably, but at their core they represent fundamentally different concepts. Digitalization is about transforming mining processes into data: by collecting, quantifying and intepreting every aspect of operations in numercial form. This approach enables a mathematical and visual understanding of how processes function as it provides critical insights into operational dynamics. The focus is on gaining a deeper, data-driven comprehension of the system. In contrast, Automation to a large extent is about substituting human operators with machines or software, reproducing the actions previously performed by them. This can progress towards fully autonomous operations, where systems function without any human intervention or oversight. Thus, in a nutshell, automation - and in its advanced form, autonomy - usually relies on rule-based configurations. These systems are programmed with predefined instructions and parameters, ensuring that processes are executed consistently and predictably. The key value here is the ability to plan and rely on the process outcomes. Automation is ideal for repetitive, well-understood tasks where the primary goal is to remove variability and enhance safety, gain productivity within clearly defined boundarys and achieve cost efficiency. For example, haul trucks following predefined routes. As mining operations become more autonomous, the reliance on human intervention diminishes, but the underlying logic remains rule-based. Digitalization, on the other hand, is about unlocking new levels of understanding. By collecting vast amounts of process data, mining companies can identify inefficiencies, bottlenecks or previously hidden weaknesses. Advanced analytics, artificial intelligence (AI) and machine learning (ML) can then be applied to simulate a large amount of scenarios, predict outcomes and optimize operations - not just replicate existing ones. This continuous feedback loop enables dynamic improvement, rather than static execution. As a result, Digitalization is targeting for a predictive view into the future and improvement. However, usually a human interpretation on the insights provided and active actions on those findings accordingly is a precondition to generate value. While there are significant interdependencies, in particular, how Digitalization and Automation complement each other in modern mining, each one serves a fundamentally different purpose: Digitalization delivers data-driven insights, while automation focuses on replacing human tasks. For this reason, it is important to consider each approach individually, carefully assessing operational needs and addressing their unique challenges. Both are essential to the future of mining. By clearly understanding their distinct roles and benefits to defined mining problems, unlocking the full potential of digital mining transformation can be highly accelerated.

We continue with the series: 💎 "Foundations of a Reliable Geological Database" 📍 Post 3: The Critical Connection Between Files – Structure, Errors, and Control. In mineral exploration, we talk about grades, models, and estimations… but all of that begins with a well-structured geological database. A reliable database is not just a tidy spreadsheet — it's a system of interconnected files, where each part contributes essential geological context. 📁 The 4 Essential Files °Collar: 🔍 Possible errors: coordinates outside the grid, incorrect elevations, out-of-range values. ✅ Control: topographic validation, standard formatting. °Survey: 🔍 Common errors: reversed paths, dips >90° incorrectly recorded, paths not starting from the collar. ✅ Control: logical and geometric consistency, dip/azimuth range, review with 3D viewers. 🧪Assays: Chemical analysis data by interval (from/to and grades). 🔍 Frequent errors: overlapping intervals, gaps, duplicate grades, incorrect units. ✅ Control: continuity checks, positive limits, duplicates, unit conversions. °Lithology: 🔍 Typical errors: inconsistent names, intervals not closing properly, overlapping data. ✅ Control: use of a code dictionary, geologist review, QA/QC validation. ➡️ What connects them? 🔏 Hole_ID is the primary relational key—the unique identifier that links each file. 🗝️ Without this clear relationship, data integrity is lost and the model becomes unreliable. 💣 Common error: records with Hole_IDs not found in the collar, duplicates, or inconsistencies across tables. 🚦 How to maintain integrity? ✔️ Implement automatic cross-table validations ✔️ Standardize nomenclature (e.g., “LIT01” ≠ “LITO01”) ✔️ Use scripts to detect overlaps and structural issues ✔️ Ensure traceability with formal QA/QC (change logs, approvals, versioning) ✔️ Educate the team on each file’s role and associated risks 📌 In Summary 📂 Each file is like a layer of information that, when properly aligned, builds the full geological story. ❕ But if just one layer is misplaced or misclassified, the entire model can fail. 🛠️ Inter-file integrity isn’t just a technical issue—it’s a critical practice to avoid multi-million-dollar mistakes.

Mining Needs Cultures where Truth Survives In mining, data is everywhere - from fleet telemetry and drill logs to processing plant outputs and tailings dam monitoring. Yet as digital or analytics projects progress, the tipping point often is reached when insights from data do not match with expectations or even prove former decisions wrong. The blame then often falls on the data: "It's incomplete", "It's not ready", "It's biased!". While those critiques may carry some weight - especially at the beginning, when iteration is a mandatory part of the process - the real problem often runs deeper: A culture that rewards comfort over confrontation and hierarchy over honesty. Mines accumulate data at industrial scale, but need to master the challenge to achieve a coherent framework to interpret or act on it - across all organisational levels. This framework or governance should be purely mining driven. It is about mining correlations, process dependencies and business rules. To a large extent findings will confirm assumptions and established practices, but there will be also uncomfortable truths. However, dealing with those truths with comfort (and without filtering them out) is neither a data nor a mining problem. It is about an inclusive and hands-on environment - in short, it is about culture and what creates culture: the people. Beyond the need for mining experts to interpret and connect data, it is essential to involve those directly impacted by the digitalization initiative. Their input is valuable not only for interpreting data related to their specific process steps, but also for engaging them in the journey and fostering transparency about how digitalization might affect their individual roles. Without clear communication and context, many people understandably worry about worst-case scenarios - chief among them, the fear that digitalization will eliminate their jobs. While job displacement may occur in isolated cases, the broader reality is that digitalization often enhances roles: it supports better decision-making, reduces the burden of documentation and enables employees to focus more on meaningful aspects of their work, sometimes even allowing roles to evolve in positive new directions. The significant part of successful digitalization is people-centric (culture!): Actively involving employees in the process and clarifying the range of possible outcomes! Core of this journey is basic groundwork through one-on-one conversations, regular discussions and ongoing engagement beyond. This is not a trivial task, nor is it realistic to expect already fully loaded lead engineers or managers to take it on in addition to their existing responsibilities. These discussions require substance to be meaningful (and not to turn into being counter-productive), with a clear and thoughtful preparation. Typically, this calls for dedicated resources - individuals who can bridge the worlds of mining, IT and human factors - to ensure digitalization in mining is both effective and embraced by all stakeholders. Mining is complex and unforgiving. To thrive, mining companies need more than numbers - they need cultures that can confront reality. That means engagement and empowering technical teams to ask hard questions. It means rewarding those who challenge assumptions with evidence from data. And it means building processes where inconvenient truths aren’t buried but investigated. Only then digitalization and analytics can serve what they’re meant to: clarity, not consensus. Stefan Ebert, Digital Mining Technologist

Blank samples comprise reference material with a grade less than the detection limit or should be close to zero, blanks are usually composed of material such as quartz, limestone, granites or basalt or any materials which ensured have grades lower than detection limit. Blanks are very useful in testing samples laboratory preparation and controlling contamination effect, usually it's inserted into sample batch after mineralised samples or main mineralised zones. There are most common two scenarios: * if it was inserted after the meniralised zones and its results recording assays, it should be reviewed the laboratory preparation and the cleanliness. * When it was inserted into expected low grade samples and its results recording high assays, it has been swapped during samples preparation and we can ensure that from our geological logging. It's common that blanks have some assays but it must be very close to the detection limits or until (10) times of it will be acceptable.

En seis páginas de información relevante, se mencionan aspectos que se deberían considerar al crear un buen programa de mantenimiento de equipos mineros, como la selección de maquinarias, capacitación de personal o la toma de datos y documentación para crear reportes de indicadores con Power BI. Si desean más información sólo deben contactarme, WhatsApp +584125375083

The mining sector is undergoing a rapid digital transformation. Artificial intelligence (AI) is reshaping every stage of mining—from exploration and planning to operations and environmental compliance. Below is a curated list of the most effective and up-to-date AI tools that mining professionals can use to improve productivity, safety, and long-term sustainability. 1. Predictive Analysis & Data Intelligence Microsoft Azure GeoAI Combines geospatial data with machine learning to accurately identify mineral deposits, significantly reducing exploration costs and timelines. Databricks Lakehouse for Mining A unified data platform that supports real-time analytics, predictive maintenance, and energy optimization, tailored for complex mining operations. Power BI with Copilot Provides real-time dashboards enhanced with natural-language insights to support fast, data-driven decision-making at every operational level. 2. Monitoring & Operational Safety Hexagon Mining Safety Suite AI-driven monitoring system for geotechnical hazards, real-time personnel tracking, and automated incident alerts—vital for both underground and surface mining. Darktrace Industrial Cyber AI Specializes in protecting mining infrastructure from cyber threats, especially in operations involving heavy automation and remote control systems. 3. Automation & Workflow Optimization UiPath AI Automation Automates back-office tasks like supply chain workflows, inventory management, and compliance reporting—freeing up personnel for higher-value work. Notion AI Streamlines technical documentation, checklists, and shift reports, allowing engineers and operators to create and organize content more efficiently. ClickUp with AI Assistant Ideal for managing large-scale mining projects, the AI assistant summarizes updates, tracks goals, and helps prioritize tasks in complex environments. 4. Training & Knowledge Capture Gamma AI Designs visually engaging training content and safety presentations in minutes—ideal for onboarding field teams or reinforcing SOPs (Standard Operating Procedures). Fireflies.ai Automatically records and transcribes technical meetings, shift briefings, and planning discussions—ensuring that critical knowledge is always documented. 5. Environmental Intelligence & Compliance Alteryx + AutoML Processes large sets of environmental data (air, water, soil) to anticipate impacts, reduce ecological footprint, and support informed mitigation strategies. Diginex ESG AI Monitor A tool purpose-built for mining and extractives, helping track environmental, social, and governance (ESG) metrics to ensure regulatory compliance and investor transparency. 🚀 Final Thoughts The smart implementation of AI in mining is no longer optional—it’s essential. These tools not only increase efficiency and profitability, but also enhance safety and environmental stewardship. Companies and professionals that embrace this “Mining 4.0” revolution will be more resilient, competitive, and future-ready

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Mining comes with its fair share of challenges, from finding valuable resources to protecting the environment and keeping communities safe. That’s where Geographic Information Systems (GIS) and Remote Sensing step in, offering smart, practical solutions. Whether it’s pinpointing mineral deposits, tracking underground fires, or managing waste, GIS helps mining teams work smarter, faster, and with greater accuracy. Through the use of cutting-edge geospatial tools, mining operations can boost efficiency, reduce risks, and take better care of the planet. From deep underground to the land and people above, GIS is making mining safer, more productive, and more sustainable.

Excited to Share: The Complete Data Terms Dictionary! After dedicated effort and passion for making complex concepts accessible, I've created a comprehensive guide that makes complex data terms simple to understand. Whether you're a beginner or an expert, this dictionary is your go-to resource. What's Inside: • 19 Categories • 114 Essential Terms • Clear, Two-Line Definitions • Zero Jargon, Pure Clarity Covers Everything From: 📊 Basic Data Concepts 🔄 Modern Data Architecture 🛠️ Engineering & Processing 🤖 Machine Learning 🔒 Security & Privacy ...and much more! Example: "Data Fabric" - A unified architecture that connects all data across an organization. Provides consistent data management regardless of location. Hot Topics Included: • DataOps & MLOps • Data Mesh • Modern Data Stack • Data Governance • Privacy Standards