Every drillhole represents a significant investment. Between drilling, sampling, and laboratory analysis, collecting subsurface data can cost hundreds of dollars per meter, making every decision about where to drill a high-stakes one.

The challenge is that promising mineral deposits are rarely identified by a single variable. High assay grades may look encouraging until they’re viewed alongside lithology, alteration, depth, structural features, or the concentrations of associated elements. When these variables are evaluated independently in spreadsheets or separate graphs, important relationships can be overlooked, increasing the risk of missed exploration targets, unnecessary drilling, and costly misinterpretation.

That’s why multivariate data visualization is an essential part of modern mineral exploration. By bringing multiple dimensions of drillhole data together into a single visualization, geo-professionals can see how geology, geochemistry, and depth interact, making it easier to identify promising targets and improve drilling decisions.

Every borehole tells part of the geological story, but rarely the whole story. To determine where an orebody begins, ends, or changes character, exploration geologists must compare information across multiple drillholes, depth intervals, and datasets simultaneously. That often means jumping between assay tables, lithology logs, cross sections, and separate graphs to piece together what’s happening beneath the surface.

While each visualization provides valuable information, manually cross-referencing them becomes increasingly difficult as a project grows. Questions that seem straightforward—such as Where do the highest grades occur? or How does mineralization change with depth?—often require comparing several datasets before a meaningful pattern begins to emerge.

One way to simplify that process is to incorporate additional dimensions into a single visualization. Rather than limiting a graph to just two variables, a multivariate visualization assigns different visual properties to represent different aspects of the drillhole data.

Here’s an example of how it works in Grapher:

• X and Y (Position): Represents the primary axes of the graph, such as drillhole location, distance along a cross section, time, or another foundational measurement.
• Z (Depth or Bubble Size): Adds a third quantitative dimension. In a 3D visualization, Z commonly represents depth or elevation. In a 2D visualization, it can be represented through bubble size or another visual scale to communicate magnitude.
• C (Color): Illustrates an additional variable using a color gradient. In mineral exploration, this might display assay grade, mineral concentration, rock type, alteration intensity, or another important geological measurement.

By assigning multiple variables to different visual properties, a single graph can communicate far more information than a traditional scatter plot or line log alone. Instead of comparing separate charts to understand how multiple geological variables relate to one another, exploration geologists can evaluate those relationships within a single view so it’s easier to identify promising targets and make more informed drilling decisions.

Now that we’ve explored how multivariate visualization expands beyond traditional charts, let’s see what that looks like practically in mineral exploration.

The heatmap below compares gold and copper concentrations across five boreholes drilled to a depth of 100 meters. Rather than displaying each drillhole or mineral in its own graph, the visualization brings them together into a single view, making it much easier to compare mineralization patterns throughout the exploration area.

To understand how the graph works, it’s helpful to break down each visual element:

• X (Horizontal Axis): Represents the individual borehole (BH-01 through BH-05).
• Y (Vertical Axis): Represents depth below the surface, increasing downward from 0 to 100 meters to reflect how exploration geologists typically view borehole data.
• C (Color): Represents relative enrichment (% of each mineral’s peak concentration), with darker charcoal tones indicating lower assay values, while bright gold highlights peak mineralization, making it easy to see where and at what depth each mineral reaches its highest concentration.
• Grouped Bars: Display gold and copper side by side within each sampling interval, creating a heat-mapped profile for every borehole to ensure both elements are visually evaluated and compared at the exact same depth.

A multivariate visualization is only as useful as the design decisions behind it. Exploration datasets often combine measurements with different units, ranges, and geological significance, so thoughtful design is essential for making meaningful comparisons. That said, here’s how to make relationships between variables in mineral exploration easier to recognize and interpret in your visualizations.