Webinar: Resolving Geo-Modeling Challenges. Why Scale Matters?

Introduction to Scale in Geological Modeling

Thank you for joining us today. My name is Anya, and I’m part of the Business Development team at K-MINE. Welcome to our final webinar of 2025. We’re excited to close out the year with you and look forward to reconnecting in 2026, as we have several updates and new developments planned. Please follow us on LinkedIn and YouTube to stay informed about upcoming releases and events.

Today, we’re exploring geo-modeling challenges and examining why scale plays such a critical role in subsurface investigations. Most of us first encountered the concept of scale in school, where it was defined as the relationship between measurements on a map and those in the real world. In cartography, that definition works well. However, when we move into geological 3D modeling, uncertainty analysis, and mineral resource estimation, scale becomes a much broader and more nuanced concept. It directly influences how we interpret data, build models, and assess confidence in our results.

About K-MINE: Mining Software and Consulting Solutions

K-MINE was founded in 1994 in Ukraine, and over the years we’ve expanded our presence across Europe, the United States, and Canada. Our team brings together software developers, geologists, and mining engineers, including specialists certified under NI 43-101 and JORC standards.

We work with a broad range of clients, from early-stage exploration teams to large mining companies. Our focus is always on delivering reliable, cost-effective solutions tailored to the specific needs of each project.

K-MINE develops software solutions built specifically for the mining industry, supporting the entire workflow from early exploration through to production. Our platform offers flexible modules for both open-pit and underground operations. One of our key strengths is adaptability: you can start with a single module or scale up to the full suite, depending on your project’s needs.

Our tools are designed to help you manage geological data more efficiently and develop optimized mine plans for both short-term and long-term planning. If you decide to integrate IoT or dispatch systems, you can also enable real-time monitoring to keep operations running smoothly and well coordinated.

Our software is grounded in real industry experience and shaped by the challenges we’ve seen firsthand through our consulting work. Our team includes Qualified Persons who prepare technical reports in line with NI 43-101, SK-1300, and JORC standards, across project stages such as PEAs, MREs, PFSs, FSs, and DFSs. We work across a broad range of hard-rock commodities, with a particular focus on critical minerals.

Webinar Agenda: Key Topics on Scale in Geo-Modeling

Today’s discussion covers the following key areas:

How scale shapes the way we interpret and work with geological data, and why it’s such a critical factor in geo-modeling. The balance between detail and regional coverage. Best practices for handling scale in numerical models. How scale-related issues can affect uncertainty and risk. Real-world examples of scaling errors. Practical tools and workflows that help make the process more efficient.

Understanding the Core Definitions of Scale in Cartography

Scale is defined as the relationship between a distance measured on a map and the corresponding distance on the surface the map represents. In cartography, scale is typically shown in three ways.

The first is a verbal or written scale, for example, “1 centimeter equals 250 kilometers.” This means that one centimeter on the map represents 250 kilometers on the ground. It’s usually the easiest type of scale to understand because it uses familiar units.

The second type is a graphic or bar scale. This is the visual scale bar you often see on maps. Bar scales are especially important because they adjust automatically when a map is resized.

The third type is the representative fraction or RF, sometimes called the natural scale, for example 1:1,000,000. It means one unit on the map equals one million of the same units on the ground. On a well-prepared map, you’ll typically see both types included to ensure accuracy and clarity.

How Scale Classifies Maps and Defines Geological Investigation Stages

Scale is also what we use to classify maps, from overview and regional maps to more detailed ones. It directly determines the level of accuracy in the information being represented. This can range from mega-regions shown at small scales to local sites and individual drill collars at large scales.

Beyond mapping, scale also plays a supporting role in defining the stage of geological knowledge and investigation. If we look at industry guidelines and regulatory frameworks across different countries, we see that scale requirements vary by jurisdiction and stage of work.

In general, most frameworks recognize three to four stages of geological investigation. These typically progress from reconnaissance and prospecting through general exploration to detailed exploration. Each stage produces resource data with a clearly defined level of geological confidence.

Exploration Stages and Corresponding Mapping Scales

The reconnaissance stage, often referred to as G4, involves broad, regional-level studies based mainly on public data. It includes regional geological mapping, remote sensing, and aeromagnetic surveys. These activities aim to identify favorable settings and highlight greenfield areas.

The prospecting stage, or G3, shifts the focus to fieldwork and ground-truthing. This includes more detailed geological mapping and geochemical sampling. Geophysical surveys and sampling are used to identify mineral occurrences and secure claims.

General exploration, or G2, becomes more targeted and detailed. Surveys are used to define the size, geometry, and potential of mineralized zones. This is often supported by shallow drilling and initial core sampling.

Finally, detailed exploration, or G1, is where intensive drilling is carried out. The goal is to build robust 3D models, estimate resources, and assess economic viability.

Post-Soviet Geological Investigation Framework

In post-Soviet countries, the stages of geological investigation are typically organized into three phases: regional studies, prospecting and evaluation, and detailed exploration. These stages follow a strict sequence, moving from broad, generalized work toward increasingly detailed investigations.

The first phase is regional investigation and forecasting. At this stage, the focus is on building a regional geological framework and identifying prospective zones using small-scale mapping, airborne geophysics, seismic surveys, and parametric drilling. Maps at this stage are small-scale, typically ranging from 1:200,000 to 1:1,000,000 or smaller.

The second phase covers prospecting and evaluation. During prospecting, work becomes more focused and detailed, including site preparation, structural mapping, detailed seismic surveys, drilling, and sampling. This is followed by the evaluation stage, where resources and reserves are estimated and preliminary geological and economic assessments are completed. Mapping scales here are medium to large, generally between 1:50,000 and 1:200,000.

The final phase is exploration and development. This includes detailed deposit exploration, where mineralization is delineated and reserves calculated. It also includes mine-site exploration during production, focused on extending known mineralization. At this stage, large-scale maps are used, typically from 1:10,000 to 1:50,000 or larger.

Overall, as projects move from regional studies to detailed exploration, mapping scale decreases while detail and confidence in geological information increase.

Scale Requirements in CRIRSCO Reporting Codes

The word “scale” itself is rarely mentioned explicitly in national reporting codes within the CRIRSCO family. However, these codes implicitly assume that the Competent Person has reviewed all previous work completed on the project, including geological information produced at different scales of investigation.

In Section 2, “Management and Execution” of the CIM Mineral Exploration Best Practice Guidelines, the geoscientist is expected to base an exploration program on a solid understanding of regional-scale geology, property-scale geology, the target commodity, and the style of mineralization. That understanding should be supported by field data and a thorough review of published, corporate, and private information.

NI 43-101 states that technical reports must be illustrated by legible maps, plans, and sections, all prepared at an appropriate scale to distinguish important features. Maps must be dated and include a legend, author or source, a scale bar or grid, and a north arrow. All reports must also include a location map and a compilation map outlining the general geology of the property.

Common Reporting Issues Related to Scale

In practice, we still occasionally see technical reports where these basic requirements for presenting exploration results are not fully met. This can lead to misunderstandings on the investor side and incorrect interpretations of mineral resource potential.

We sometimes receive requests from junior companies planning further exploration or moving toward production, but with very limited baseline data. In some cases, there is no topography and no drillhole data at all. The only available information might be geophysics or a few surface samples, yet the client is already considering resource estimation and the start of mining activities. This situation is particularly common in weak regulatory environments.

When it comes to reporting exploration results, we often see two extremes. The first is when the scale and location of mineralized zones are unclear. Maps may be missing a coordinate grid, drill collar locations, sample points, or even a north arrow, making it difficult to understand the spatial context of the data.

The second extreme occurs when, at a very early stage, reports already present tonnage and grades by resource category. However, the JORC Code clearly defines that any information relating to an exploration target must be expressed so that it cannot be misrepresented or misconstrued as an estimate of a Mineral Resource or Ore Reserve.

There can also be the opposite situation. Sometimes, very detailed large-scale information exists for a permit area, supported by a dense exploration grid with multiple veins already identified. At the same time, there may be little understanding of the regional setting, ore-controlling structures, or secondary mineralization processes. In cases like this, effective follow-up exploration requires integration of all available regional information, including nearby prospects, analogue deposits, and a broader analysis of tectonic and structural controls.

Dynamic Scaling and GIS Capabilities in Modern Geo-Modeling

When we talk about geological modeling and resource evaluation in GIS environments or through web-based technologies, a wide range of new capabilities has become available. These include dynamic scaling, online geological maps for many countries, and access to registries and cadastral systems used to manage land tenure, protected areas, and other constraints.

In GIS, dynamic scaling refers to the automatic adjustment of map elements as the user zooms in or out. This ensures information remains readable and relevant, something not possible with static, paper-based maps.

Key aspects include scale bars or map sheets that automatically update units and lengths, dynamic labeling, and scale-dependent visibility of features. As you zoom in, details appear. As you zoom out, less critical information is hidden. This prevents visual clutter while maintaining clarity and context at every scale.

K-MINE GIS Tools: Coordinate Conversion and Google Maps Integration

K-MINE software allows users to automatically convert different coordinate systems and import imagery from Google Maps directly into the modeling environment. All you need to know is the coordinate system you’re working in. You can select the boundary of your area of interest and, using the built-in geocalculator, recalculate the coordinates of the boundary points and then load the corresponding Google Maps tiles.

The system includes a long list of coordinate systems. By entering the system ID into the filter, you can quickly find the one you need. The world map below shows the boundary of the selected zone in white, and you can drag frequently used coordinate systems into your favorites for easy access.

If needed, you can also adjust the map display and switch between Satellite and Roadmap views. Coordinates can be imported and exported in CSV and KML formats, which makes it easy to integrate external data into your workflow.

Together, the coordinate conversion tools and Google Maps integration provide a powerful way to analyze project geodata. In fact, this functionality has helped us avoid serious mistakes in resource estimation and has even supported geologists and legal teams working at operating mines.

Real-World Case Studies: Scale-Related Errors and Spatial Constraints

In one case involving a titanium deposit, the mining license listed a large area and significant tonnage. However, when we analyzed the spatial data, we discovered that part of the licensed area was covered by pine forest and lay within a protected reserve. Mining permits for that zone were denied, which restricted development and required part of the resources to be excluded. Because this was identified early, the team was able to amend the license in time and save substantial costs in future mineral use taxes.

A similar situation occurred at an iron ore quarry. The long-term mine plan showed a pit outline extending into protected zones, including a railway line, a station, and a regional gas pipeline. These facilities were not reflected in the original layout, as they lay beyond the short-term planning horizon. We recommended contacting the railway authority to explore relocation options. The request was denied, making it clear the mine development strategy needed a fundamental revision.

Another case involved Google Maps imagery at a graphite operation. The satellite images revealed overlap with a cemetery, a sports stadium, and residential housing. Addressing these constraints would have required major investments, including water management, housing compensation, and cemetery relocation. When these factors were included, the project was found to be unviable.

We’ve encountered many situations like this in practice. That’s why the value of integrated Google Maps tools with dynamic scaling and coordinate conversion is very clear. They help identify spatial constraints early, reduce risk, and support more realistic and defensible project decisions.

Scale vs. Resolution: Understanding the Relationship in GIS

Although scale and resolution are different concepts, in GIS they are closely related and often discussed together. Scale primarily describes how real-world distances are represented on a map, while resolution defines the smallest object that can be distinguished.

Spatial resolution refers to the level of detail in an image, expressed through pixel size. Higher spatial resolution means smaller pixels and more detail, while lower spatial resolution results in larger pixels and less detail. Overall, spatial resolution determines visual quality and how clearly individual features can be identified. As grid cells become smaller, the image captures finer details, which can be critical for accurate interpretation and analysis.

When it comes to raster data, resolution directly limits the level of detail you can see at a given scale. Even very high-resolution imagery can lose much of its value at small scales because fine details are no longer visible. For vector data, such as topographic maps, scale plays a much bigger role in how much detail is shown, while resolution is less limiting.

In GIS, raster data is represented as grids of pixels and is commonly used for continuous variables like satellite imagery, elevation models, temperature, or terrain. Vector data, on the other hand, is built from coordinates defining points, lines, and polygons, such as roads, boundaries, and drill locations. Vector data offers high precision, good scalability, and rich attribute information, although it can result in larger file sizes.

Raster data is ideal for surface analysis but can become pixelated. Vector data is better suited for precise mapping, network analysis, and editing. In practice, both types are often used together to support comprehensive spatial analysis.

Ultimately, spatial scale has a fundamental impact on geological modeling. It influences data resolution, the geological patterns we can identify, and model accuracy. Finer scales allow us to capture local heterogeneity, such as karst features. Broader scales highlight regional trends and structural controls.

Scale and Resolution in Numerical Geology

In numerical geology, scale and resolution play different but closely connected roles. Scale defines the physical extent of the system we’re modeling, whether we’re looking at the crust, a reservoir, or an outcrop. Resolution, on the other hand, controls how finely geological features such as faults, facies, or fractures are represented, typically through grid size or model detail.

Higher resolution allows us to capture geological heterogeneity, like thin layers or subtle structural features, which can be critical for understanding fluid flow or reservoir behavior. At the same time, higher resolution comes with a significant computational cost. That’s why upscaling is often necessary – moving from very detailed models to coarser, more manageable ones that are still practical for large-scale simulations, resource management, or hazard assessment.

Scale varies dramatically across geoscientific and engineering applications. The datasets span an enormous range, from measurements on the order of tens of thousands of feet down to micrometers. At the seismic stack level, certain types of variation, such as fine vertical detail, are necessarily averaged out. At smaller scales, well logs capture vertical variation because they operate at a much finer resolution. Going even further, thin sections reveal details within individual strata that are only visible at this very fine scale. Each dataset adds a different layer of understanding, but only within its own scale window.

The same principle applies to geophysical data interpretation. Scale and context must always be considered. This means understanding the spatial and temporal limits of each dataset and relating them to the geological processes and models we’re trying to interpret.

Seismic data can vary widely in resolution and frequency, depending on wave velocity and wavelength. Gravity and magnetic data have different sensitivities and levels of ambiguity depending on the distance and orientation of their sources. Electrical and electromagnetic methods differ in penetration depth and complexity based on subsurface resistivity and conductivity. Because of this, it’s essential to match scale with the right interpretation tools and techniques. When scale, resolution, and method are properly aligned, the results are far more reliable and meaningful.

Data Types and Scale-Dependent Geological Observations

Data types reveal different aspects of the geology, depending on the scale at which they’re collected.

At the core scale, measured in millimeters to centimeters, features such as veins, sulphide textures, alteration boundaries, and microstructures can be identified.

At the sample scale, typically ranging from centimeters to meters, observations focus on assay intervals, grade variability, and lithological contacts.

When we move to the geophysical scale, on the order of tens to hundreds of meters, data reveals much larger features, including major structures and broader trends in conductivity or density.

Because each dataset has its own inherent resolution, what we’re able to observe is always controlled by scale. Fine-scale features may disappear at larger scales, even though they can be critical for understanding ore continuity and mineralization behavior.

Resolution and Grid Cell Size in 3D Geological Modeling

Resolution and grid cell size are critically important when it comes to 3D geological modeling. In vein modeling, scale refers to how vein dimensions, such as length and thickness, change across different observation levels, from outcrop and mine scale to regional scale.

It also reflects how modeling software handles scale, different data types like drillholes and samples, and modeling parameters such as triangle size or snapping tolerance. All of this influences model realism, from small mineralized shoots to large structural systems.

In practice, this means managing multi-scale detail, from broad fault systems down to individual ore shoots, while maintaining consistency in geometry and grade distribution.

In K-MINE, vein models are built using drillhole data, surface information, and structural controls such as fault surfaces. This allows us to bridge scales, linking discrete point data and continuous geological surfaces.

In implicit vein modeling, scale becomes especially important. Narrow veins represent a small-scale challenge, while large faults operate at a large scale. Mathematical interpolation methods, such as radial basis functions or kriging, define veins as isosurfaces, while structural data constrains geometry. Often, the most realistic results come from hybrid methods, combining implicit and explicit modeling.

Scale-Dependent Continuity in Mineral Resource Estimation

It’s important to note that continuity is highly scale-dependent. At a large scale, structural continuity may extend for kilometers. Within that structure, vein continuity may persist only for tens of meters. Because gold is often concentrated in narrow veins, grade continuity can appear low globally but strong locally. For this reason, continuity should be discussed at multiple scales.

When we move into mineral resource estimation, scale again plays a defining role. A scoping study is generally based on Inferred Resources, with wide drill spacing and global continuity assessment. A pre-feasibility study requires higher confidence, supported by closer drill spacing and Indicated to Measured Resources. At this stage, continuity can be assessed at a local scale, while Inferred Resources are excluded from economics. By the feasibility stage, modeling reaches maximum detail.

Managing Scale-Related Challenges and Trade-Offs

Scale-related challenges often stem from choosing the wrong level of detail. Using mismatched data scales leads to misinterpretation. There is always a detail trade-off between resolution and coverage.

Scale-related errors directly affect model reliability and risk. Integrating data across multiple scales remains a key challenge. High-resolution data increases costs, often with diminishing returns.

Project teams must balance technical ideals with practical constraints. Modern software like K-MINE supports scale decisions, balancing resolution, coverage, and reliability.