By Peter Joynt

As you may know, we have recently released Leapfrog Geo 3.1 – this new release adds 3D point selection, a relatively simple feature that unlocks a number of new workflows. I am going to show you a few of these workflows – and hopefully it helps identify others that will fit your unique problems.

The point selection tool works in a similar way to interval selection, which has been available in Geo for some time. Interval selection is extremely useful for reclassifying poorly logged intervals and picking vein and dyke segments out of large complex drillhole datasets in 3D. The workflow is based around a simple to use 3D paint brush tool which, when combined with in-scene slicing and query filters, lets you quickly select and re-classify segments. We have now adapted this tool to be used with points, and allowed the creation of new category selection columns on points. Below are three examples of ways point selection can be used in your projects.

 

Selection of collars

Have you ever worked with a large drillhole dataset that has multiple prospects but no column in the database that can be used to separate them? This is a situation where the point selection tool can be useful. You can easily select a subset of drillholes in 3D and assign them a category. On top of this, you can apply query filters to give you flexibility for modelling and visualisation. The selection itself can be carried out directly on the collar table in the Drillhole Data folder.

Collar selection

Working with blastholes

Guide points are another great feature introduced in 3.1 and are specifically aimed at working with blasthole data. This feature can be used to create intrusion values on points based on inside, outside and ignored categories. If you have not used intrusion values in Geo – these allow you to create implicit volumes around points.

So what can you do with guide points and the 3D point selection tool? Lets look at blasthole data that has been logged with a lithcode. Blastholes are notorious for poor-quality logging when compared to diamond or RC, so we need a way of fixing errors and reclassifying them in 3D. Using 3D point selection, we can easily reclassify poorly logged holes or pick out certain domains from a variety of data sources in the 3D scene with good context from surrounding data.

Once you have Imported the blasthole midpoints into the Points folder you can then make a new selection based on the logged column. Guide points can then be generated from the selection and added to the geological contact.

Blasthole selection

Cleaning up a point set

With a lot of projects I often find myself working with point data that has errors or artifacts in it, and I have met many others with the same issue. In the case of topography datasets, which have been collected by scanning or merging of multiple data types, it is relatively common to come across problematic data. The new 3D point selection tool can help with this problem.

In the example below, we have a section of a pitwall that has been scanned to generate a point cloud. There are a number of invalid points from the accidental scanning of drill rigs, dump trucks and an offsider eating his lunch that need to be removed to produce a realistic surface. The 3D selection tool allows us to quickly swipe the unwanted features and assign them a category. You can then easily use a query filter to remove the incorrect points in order to obtain the subset of points to generate a good surface.

Topo with point cloud