As an enthusiast of mineral resource estimation, I’ve embarked on a journey to quantify a gold deposit using drillhole data. I’m not a geologist or mining engineer, so I will learn as I go along. According to various sources, the main steps consist of first understanding the geological data of the resource, deriving a model, and then estimating the mineral content. In this post I will discuss my first step in understanding the drillhole data.

Understanding the Drillhole Data

I obtained data on 51 drillholes from an Access database (*.mdb), which includes several pivotal tables:

• Collar Data: Contains drillhole ID, starting point, and depth.
• Survey Data: Details the length and dip of drillhole segments.
• Geology Data: Describes the lithology along each drillhole.
• Assays Data: Records gold grade in parts per million (ppm) along each drillhole.

Visualizing this data will help me grasp the characteristics of the mineral resource, such as its shape, size, and grade distribution.

Techniques for Visualizing Drillhole Data

Lacking the budget for licensed software, I explored various free or open-source applications to visualize this geological data, settling on QGIS, Paraview, Geoscience Analyst free viewer, and Python’s plotting libraries in Jupyter Notebook.

I will only present in this post the visualisation of the data using QGIS and Geoscience Analyst free viewer.

Utilizing QGIS

In QGIS, I followed a process outlined in a specific article as well as a video by Mining Geologist. It involved:

• Downloading two plugins: Geoscience (for processing drillhole data) and Qgis2threejs (for 3D visualization).
• Adding a visualization layer by de-surveying the collar and survey data.
• Enhancing this layer with information from the geology and assays data.

The following picture shows the QGIS projection of the drillhole data.

As per above picture, the initial QGIS 2D visualization was somewhat limited, so I used Qgis2threejs to project it onto a 3D illustration as per below.

The spheres represent the colars and the “pipe” are the drillholes with different colors representing the lithology. I was not able to illustrate the grade along the drill holes. The zooming and detailed manipulation of the elements of the picture was limited.

Exploring Geoscience ANALYST

Geoscience ANALYST, particularly the free viewer by Mira Geoscience, offered more features for in-depth analysis. The visualization process, as demonstrated in their YouTube video, involved:

• Loading collar and survey data to create a basic drillhole layer.
• Augmenting this with geological, assay, and other interval data.

The following pictures depicts a view from the top and from the side (east) of the drillholes.

Figure 3: Geoscience ANALYST Top and Side Views of Drillholes

The illustration is more refined and better. I elongated the representation of the drillholes to see with better granularity the changes along the drillholes.

To gain further insights, I colored and sized each drillhole segment based on its gold grade. I mapped the colors and sizes of the drillhole segments against the CDF values of the gold grade across all the boreholes. The application provided key statistics, like mean, standard deviation, and maximum grade values. I then applied a minimum and maximum filter to focus on specific value ranges. The panel with the selected option is depicted in the following figure.

The following figure shows a close-up view of drillhole data.

This detailed visualization helps me in understanding the quality of the resource, namely the spatial distribution of the grade and the shape of the resource. This illustration will be instrumental in developing a model to estimate the deposit’s overall mineral content.