Category: How-to

Perform a bootstrap-style analysis using Leapfrog EDGE in just 5 steps

One simple yet effective method for testing estimation sensitivity is to run a bootstrap analysis. This is when a sample or set of samples is intentionally removed from a dataset, an estimation is performed in the location of the missing samples, and the estimated result is compared back to the samples.

The tools and workflow built into Leapfrog EDGE make this a simple operation to perform at a block level by comparing the results of two different estimations.

1. Create a standard estimator

The first step to running a bootstrap is to create an estimator using all data required. For this example I will use the LMS1 domain from the Leda training dataset, and will be estimating the Zn grades within that volume.

Image 1

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Tips for vein modelling with channel samples

By Antonio Garibaldi,  Zak Hynd and Ignacio Torresi.

This is written for those who are having trouble modelling veins with fragmented data, especially channel samples.

Leapfrog Geo’s vein tool was designed to work with drillhole data, ideally drillholes which intersect both walls of the tabular vein structure. If you are using the vein modelling tool with fragmented channel sample data you will find these tips and tricks helpful.

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Part 2: More confident resource understanding

Iain Levy, Senior Technical Lead, Seequent.

In the first blog, we talked about a new more immersive approach to resource estimation. In this, the second part, we use real world data to highlight how this works and how this deeper understanding benefits the user. We will start off by looking at how you set up a domain for the immersive approach, before looking at how this could then be applied to investigating the search parameters.

Before we get started though, here’s a quick outline of the mineralisation:

There are two major pods of massive sulfide mineralisation running in a NE-SW trend, plunging to the NE (Figure 1). There is also a small pod of mineralisation to the north with limited drilling. We will be looking at the northern (red) pod to start. While it is a base metal deposit, we are going to be estimating gold as it has more interesting statistics and trends than zinc or lead. In terms of drilling, we are looking at a grade control situation with high density drilling.

 

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Part 1: Where have the macros gone? An immersive approach to estimation

Iain Levy, Senior Technical Lead, Seequent.

When discussing Leapfrog EDGE with clients I’m often asked, “what about macros?” or, “will Leapfrog EDGE be getting macros in the future?” Along this vein, a common request is for a swish multidomain tool that enables you to quickly carbon copy parameters from one domain and apply them to multiple other ones.

This has led me to reflect on why macros are so engrained into our estimation workflows, and if this has resulted in a focus on the processes of resource estimation to the detriment of the geology and reasoning behind it. As Dr Jacqui Coombes, General Manager – Innovation at METS Ignited recently commented; ‘The future of resource estimation will not focus so much on the process of resource estimation but on the reasoning of it’.

This blog is part one of a two-part series which considers the standard approach undertaken for resource estimation, and the value of a new approach where reasoning and validation play a more integrated role throughout the estimate.

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Distance functions as a resource / Confidence classification and drillhole planning tool

By Findlay Craig

The distance function tool in Leapfrog Geo allows you to calculate the distance from a given object or objects within a project, and derive distance buffers – solids whose walls are a set distance from the objects we are interested in.  Although conceptually simple, this tool can be applied to drillhole planning and resource classification to quickly and easily inform and justify our planning, and quantify current and potential reserves.

In the example below, we have an ore body which is partially drilled out; to the south it is poorly constrained and we need to plan an additional drillhole program to gather more information in this area.

Distance functions as a resource blog

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3D geological mapping: From 2D GIS maps to 3D modelling

By Antonio Celis

phot 1

 

Traditionally, geoscientists have translated their field data collection efforts into a digital version of a geological map, which is typically built using GIS software (e.g. ArcGIS). Consequently most companies will have large archives loaded with GIS information from their projects. With more companies now choosing to collect their field data using electronic devices it ultimately results in a large repository of GIS data.

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Geotechnical data collection and presentation – It starts and ends with a vision

We’d like to share this article by Louie Human and Ben Jupp, Principal and Senior Consultants at SRK. The article gives some great advice on best practice approaches to Data Collection as the first step towards minimising uncertainty and input into the advanced technical assessment and design of a caving operation. The article goes on to stress the importance of using 3D visualisation tools such as Leapfrog in engineering, to make the understanding of the data in a spatial context possible.

The authors outline why combining robust data collection with software toolsets that visualise, interrogate and develop geotechnical models is so important and how these ultimately feed into the mine design and decision making process and impact on the safety and economics of recovery.

The article first featured in AusIMM Bulletin in August this year: http://bit.ly/2dPsZa9

 

 

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Leapfrog Aspect: Tips and tricks

By PJ Hollenbeck

The Leapfrog Aspect Android app has been available for more than a month on Google Play. Over 300 devices have installed the app, and use cases have ranged from general geology and mining visualization to civil tunneling projects and environmental studies.

The first update went out two weeks after release which added support for contour lines and planned drillholes, as well as a number of minor bugs.

 

File size limitations

Questions regarding File Size limitations have come up a lot, but unfortunately there’s not a great answer for a standard size limit.  Aspect has better success loading wireframes than drillholes and point data, so a file that’s 50MB but mostly wireframes is going to be more suitable for viewing in Aspect than a 20MB file consisting of 3000 drillholes.  High-resolution texture maps on surfaces (such as draped aerial photos on topography) will also slow down Aspect and may lead to crashes or other undesirable behavior. Likewise, if a scene is choppy or erratic on the desktop Viewer, it will probably have issues in Aspect. If there are errors in wireframes they may also cause issues in Aspect, so be sure to use clean data when exporting scenes!

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Point selection in Leapfrog Geo 3.1

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

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Dimensions and decisions: Mineral resource risk

By Mike O’Brien

 

10% and 90% Probability Shells for a Model of a Kimberlite Pipe

10% and 90% probability shells for a model of a Kimberlite Pipe

Mineral Resources and Reserves are not easy things to define. Qualified Persons (QP – NI43-101) and Competent Persons (CP – JORC, SAMREC etc) exist to do difficult things independently and provide a level of comfort to investors, clients and regulatory authorities.

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