Compositing numeric and assay data

Leapfrog Geo allows users to easily manipulate drilling data. This blog, written by Applications Specialist Kirk Spragg, explains the process of compositing numeric data. Numeric data compositing takes numeric data that is unevenly spaced down drillholes, and turns it into data that is regularly spaced down the same drillholes. In this article, we explain the separate stages of the compositing process in detail. We also show you how Leapfrog Geo’s compositing algorithm works.

The compositing process

The compositing process is performed in three stages:

Choosing an interpolant function

Leapfrog Geo uses two different base functions to form interpolants. They are the linear interpolant function and the spheroidal interpolant function. This blog covers when to use each base function, how to set the function parameters, and how to convert the parameters for a Leapfrog Mining interpolant across to Leapfrog Geo.

As explained in the Leapfrog Interpolation Basics blog article, the interpolant functions indicate how the function values are expected to vary as the distance between points increases. At small distances the values are expected to be similar and so the function values are small. At large distances the values are expected to vary considerably and so the function values are larger.   The nature of this relationship means that the interpolant function is equivalent to the variogram used in geostatistical modelling.

Video guide – How to share assay data from Leapfrog Geo with ioGAS

By Sam Bain

ioGAS software allows its users to identify geochemical relationships within assay data. With the newly released ioGAS Link, a live link between Leapfrog Geo and ioGAS, you can easily incorporate geochemical data into your geological modelling process. The geochemical relationships identified in assay data can be used to refine existing geological models and define new geological units.

This video demonstrates a workflow that allows you to share assay data from Leapfrog Geo with ioGAS. Once the live link is opened, the assay data is shared with ioGAS. The TAS plutonic diagram within ioGAS is then used to identify geochemically defined lithologies. The newly defined lithologies are transferred back to Leapfrog Geo and used to create new geological surfaces. The new geological surfaces are compared to the units identified by field logging. As you will see, there are significant differences!

While this is demonstration data, it illustrates the potential benefit in using assay data to cross-check the field logging of drillholes.

For more information on the ioGAS Link visit the Leapfrog website.

Modifying your drillhole data: Grouping

By Sam Bain

A standard workflow in Leapfrog is building a geological model from the interval columns of imported drillholes. This workflow requires the importation of a collar locations file, a survey file with drillhole geometry, and an interval table with the observed lithological contacts. In addition, other down-hole information such as from geophysical logging or drill core assays can be imported as interval tables.

Often there will be problems with the drillhole data. Perhaps the logging in the field was inaccurate and two unique units were incorrectly lumped into one. Of course, the opposite could happen if an eager geo sub-divides a single sandstone deposit into separate poorly sorted and well sorted units. In these situations, the drillhole data needs to be edited so that a new unit is defined or existing units are combined. The “Group Lithologies” and “Split Lithologies” tools in Leapfrog allow you to create a new interval column based on edits of an existing interval column. An additional tool called “Interval Selection” has been developed that uses elements of the grouping and splitting workflows to create a new interval column. In this post we will look at the “Group Lithologies” tool.

The dark art of drillhole desurveying

By Richard Lane

Desurveying computes the geometry of a drillhole in three-dimensional space based on its collar location and the raw dip (or inclination), azimuth (or direction) and depth data of one or more surveys. The resulting geometry is a polyline – a connected series of (X, Y, Z) coordinates used to find the composite locations.

Only under ideal conditions will the path of a drilled hole follow the original dip and azimuth established at the top of the hole. It is more usual that it will deflect away from the original direction as a result of layering in the rock, the variation in the hardness of the layers, and the angle of the drill bit relative to these layers. The drill bit will be able to penetrate the softer layers easier than the harder layers, resulting in a preferential direction of drill bit deviation.

Introduction

Everyone would like accurate and correct data. A lot of money is spent on trying to ensure the accuracy of data, whether it is from core logging, field mapping, or geophysical surveys. However, the reality is that mistakes and accidents will happen and some data will be wrong. Accepting that errors are always going to get in somehow, it makes sense to create systems and software that identify these errors, and then provide the tools to fix them. Good software packages help deal with data quality by identifying common errors on import of data and providing automated correction of these problems. Ideally there should be a person in the loop to ensure the identified data is an error and to decide how they should be dealt with.

Leapfrog software automatically highlights recognised data errors and in most cases provides automated or semi-automated correction tools for you. The principle source of errors is drillhole information and the drillhole interface prominently highlights recognised issues and offers you the ability to correct errors. Leapfrog software will also recognise errors and potential errors in imported locations and prompt user input to fix them.

What is happening with Mining and Geo

By Richard Lane

Leapfrog Geo was introduced in response to a number of requests from users to have a tool that could be integrated more easily in a standard work environment and manage the level of complexity they were encountering in their models. This led to a new interface that helped organise the models in Leapfrog Geo in a more structured way.

We now have received a considerable amount of feedback on how Leapfrog Geo compares to Leapfrog Mining. It has been very informative from our perspective because it has clearly illustrated the very wide variety of uses and tasks that Leapfrog Mining has been used for.

The principal difference between the two products is that Leapfrog Mining is a toolbox, which contains a significant number of powerful tools. Leapfrog Geo is designed to do certain workflows and it does these very efficiently. For users whose principal tasks are these workflows it is the obvious choice, and a significant number of users have indicated their preference by switching to Leapfrog Geo.

Introduction

The Leapfrog software suite uses a mathematical method called interpolation to produce dynamic implicit models.  An interpolation tool, FastRBF™ has been specifically developed by ARANZ Geo. FastRBF™ has revolutionised the way geologists produce geological models, as it dramatically speeds up the process and allows models to be updated dynamically. Although the mathematical details of how FastRBF™ works are somewhat complicated, the basic idea is relatively simple. This blog explains the process using simple examples.

Interpolation is a method that produces an estimate or “interpolated value” of a quantity which is not known at a point X say but is known at other points such as from drillhole data.  With the user’s expert guidance, Leapfrog uses FastRBF™ to “interpolate” or fill in the gaps where there is no data.  This is how Leapfrog creates deposits, intrusions and grade shells from the user’s data. Since FastRBF™ is fast, results can be quickly updated when new data is added, ensuring the implicit model is dynamic.