Basic Grade Interpolation in Leapfrog

Basic grade interpolation in Leapfrog Ron Reid Jun 27, 2013

In my last post I post I mentioned that I composite for basic geostatistical reasons. Recently I observed a eapfrog grade interpolation run on ra! gold assays, using a linear variogram, and the result !as a!ful to say the least and in fact !as completely !rong by any an y measure. "rom a geostatistical point of vie! a number of rules !ere bro#en , it is not the purpose of this article to go into these in detail, but rather sho! ho! a simple application of some basic rules of o f thumb !ill result in a much more robust grade model. $ere I !ill cover the basics of the database, datab ase, compositing, applying a top cut, appro%imating a variogram and the basics of finding a &natural& cut for your first grade shell in order to define a grade domain to contain your model.  'ote that in the forthcoming discussion I refer largely largely to processes in eapfrog (ining, it being a more po!erful and useful tool than eapfrog )eo in its current form, ho!ever if you are a )eo user the follo!ing still applies * the !or#flo! may be slightly different.

The database +s in all things the )I) principle, &garbage in, garbage out&, applies in eapfrog. If your database has not  been properly cleaned cleaned and validated validated you !ill get erroneous erroneous results. I have noticed noticed that many many " users !ill load a drill hole database and not fi% the errors flagged by eapfrog. -he most common issue is retaining the belo! detection values as negative values values such as 0.01 for belo! detection gold for instance. If this is left in the database then the interpolation interpolation !ill use this value as an assay and it !ill lead to errors in the interpolation model. It is better to flag it as a belo! detection sample and instruct eapfrog in ho! to treat these. /here the database uses values such as  for lost sample, or  for insufficient sample you !ill get a spectacular fail !hen you attempt to model this yes it does happen4. If you only have a fe! errors it is simple enough to add a fe! special values through the fi% errors option to correct these issues "igure 14. If you have a large number of errors the fastest !ay to fi% the errors is to load the data once, e%port the errors in order to identify them and then build a special values table that records each error. -his is fairly simple to do and should be laid out as sho!n in "igure 2, you save this as a csv into the same folder as your drill hole data. -his file can then  be used for every " (ining (ining pro5ect you build build as long as your field names names do not change, or the particular particular assays do not vary, although it is not too big a 5ob to ad5ust the table if need be. 6ou then delete your database from your pro5ect and reload it, selecting the special values table at the same time "igure 34, the database !ill then load !ith the assay issue fi%ed. If you are a eapfrog )eo user you cannot do this as the special values option has been removed, you have to manually correct and validate as assessed every error flagged a process that can become uite tedious in a large pro5ect, and frustrating8 "igure 94. nce your assay table has been validated you can move on8 technically your !hole database should be validated validated but I !ill ta#e it as a given that the process has been completed, most people understand issues around drill holes !ith incorrect coordinates or drill holes that do a right hand bend due to poor uality survey data.

Figure 1. Fixing a simple series of errors in Leapfrog Mining is a simple process as the file can be adjusted to correct errors. In this case I have 2 errors a series of !"s that represent insufficient sample and #$.$1 %hich is belo% detection. I can fix these using the &dd 'pecial &ssa( value) option and selecting *ot 'ampled or Belo% +etection.

Figure 2. ,ith Leapfrog Mining (ou can create a 'pecial -alues Table that can be loaded at the +atabase import stage the 'pecial -alues Table should be structured as above.

Figure . Top image sho%s %here (ou can load the 'pecial -alues table /blue arro%0 this can onl( be loaded at the time of loading the database it cannot be added after the database has been loaded.

Figure . Leapfrog eo does not have a facilit( to import 'pecial &ssa( -alues (ou must manuall( correct the errors ever( time (ou create a ne% project once the rules have been decided (ou must tic3 the 4These rules have been revie%ed5 option to get rid of the red cross.

6omposite (our data7 I have not yet come across a drill hole database that consists of regular 1, 2 or 3 metre sampling, there is al!ays a spread of sample lengths, occasionally due to sampling on geological boundaries, through to bul#  bac#ground composites and unsampled lengths. -his leads to a large variation in !hat is termed support length "igure 4. It is also common for there to be a correlation bet!een sample length and grade, ie smaller sample lengths !here grade is higher "igure 4. -his can lead to problems !ith the estimation process that is !ell understood in )eostats, perhaps less !ell understood outside of the resource geology !orld. eapfrog:s estimation is basically a method of #riging, and so is sub5ect to all the foibles of any #riged estimate, these include issues of e%cessive smoothing and grade blo! outs in poorly controlled areas. eapfrog has a  basic  blog article about ho! leapfrog;s modeling method !or#s on their !ebsite. $aving multiple small high grade intervals and fe!er larger lo! grade intervals !ill cause the high grade to be spread around share and share ali#e4.

+ simple !ay of dealing !ith this is to composite your data. 0D of the sill, a range of say 2m and use an alpha value of .

"igure ?. "igure sho!ing the effect of varying the nugget value, -op is a straight isotrop ic linear interpolation /Linear is al%a(s a *o *o0 belo% that is a $> nugget then a  $> nugget and finall( a 9$> nugg

"igure . "igure sho!ing the effect of the +lpha Cariable, on the graph on the top is for LF Mining the graph on the botom is for LF eo? *ote that the variable changes bet%e en Mining and eo so that a higher &lpha variable in Mining /eg @0 is eAuivalent to a l o% &lpha in eo /eg

The natural cut -he ne%t step is to define the natural cut of the data. =ometimes !hen !e run an interpolation !e find that the lo!est cutoff !e use creates a solid bo% !ithin our domain "igure 104, this is because there are too many samples at that grade that are unconstrained, ie !e are defining a bac#ground value. -he first step in defining a set of shells from our interpolant is to start !ith one lo! grade shell, say 0.2gpt. +s !e are creating 5ust one shell, after the interpolant has been created the one shell is uite uic# to generate. /e may find that 0.2 fills our domain so generate a shell of 0.3 and rerun, continue doing this until you find the cutoff !here you suddenly s!itch from filling the domain to defining a grade shell, this is your natural cutoff for your data "igure 104. 6ou can use this as the first shell in your dataset, simply add several more at relevant cutoffs for assessment and vie!ing, or you can generate a )rade Fomain using this cutoff to constrain an additional interpolation that you can then use to select and evaluate a grid of points, effectively generating a eapfrog  bloc# model.

Figure 1$. Figure sho%ing the effect of shells above and belo% a natural cut#off. Bro%n C $.2g;t %hich is an unconstrained shell blueC$.g;t %hich is the constrained shell and defines the natural cut#off of the dataset.

 "ollo!ing this process outlined above !ill vastly improve your grade modelling and lead to  better interpolations !ith better outcomes. 'ote I have not spo#en about search ellipses, ma5or, minor or semi minor a%is, orientations of grade etc, this is because this is all dependent upon the

deposit. 6our deposit may reuire an isotopic search, or some long cigarshaped search, depending upon the structural, lithological and geochemical controls acting upon the d eposit at the time of formation and effects post formation. -he average nugget and the range of the variogram !ill generally conform to !hat is common to that deposit type around the !orld. + bit of study and research on the deposit is something that should already have been done as part of the e%ploration process, adding a uic# assessment of common variogram parameters is not an arduous addition to this process. It is not a reuirement to understand the intricacies of variogram modelling, nor the maths behind it, but #no!ing the average nugget percent and range for the deposit type should be an integral part of your investigations, and should inform your eapfrog )rade interpolations. $appy modelingG