How to do Indicator Modelling in Leapfrog Works with the Contaminants Extension

Overview

Video Transcript

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<v Narrator>Welcome to Leapfrog Works</v>

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and its Contaminants extension.

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In this video,

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we’ll give you a quick tour through our workload

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that will help you use indicator modeling,

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to estimate and code blocks using the functionalities

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of the contaminant extension.

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In this project,

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we have already coded our contamination data

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into zeros and ones,

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using a threshold of four PPM.

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For this video,

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we’ll use the Contaminant Models folder that becomes

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available, once the contaminant extension is activated.

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To start with this workflow,

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we must right click on the estimation folder

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and create a new estimation domain.

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Then we select the data set we want to estimate

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and the estimation domain.

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In this scene, the already coded water data set is projected

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within the select geological domain

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of unconsolidated sediments.

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A spatial model allow us to analyze

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the spatial variability of values within the domain.

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We will be able to model our 3D variogram as an input

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to the estimator with the assistance of Leapfrogs

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highly connected 3D scene.

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It allows for real time visualization

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of your experimental variograms with our 3D scene,

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making the process more intuitive.

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Anytime we change a parameter within

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our experimental variogram or the variogram model,

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we’ll see how our variography change.

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When we right-click on the estimation folder,

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we can select which estimator we want to use.

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For this workflow, we’ll use simple grading.

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We set the parameters for our estimation,

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including the created 3D variography.

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Define the search ellipsoid ranges, the search definition,

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and finally, the required outputs.

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Once the domain, the spatial model,

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and the estimators are set, we must create a block model,

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to code our estimated indicator values.

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Creating a block model is a straightforward workflow

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and a great way of representing contaminants models

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in 3D.

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We can adjust our parameters and select which models

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and estimation will be represented at (inaudible).

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In this case,

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we have evaluated several geological and numerical models,

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as well as different estimators.

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All this information can be reviewed in the scene in a very

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flexible way.

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When indicators are coded into block models,

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we can analyze the probability of each block to be above

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or the final threshold.

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In this case four PPM chloride.

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We can filter the block to this value

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and observe the distribution of the plume.

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Calculations are available in block models to visualize

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new created variables and to report

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mass and average concentration, for example.

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In this case,

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we have created a new category defined by

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the estimated indicator.

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If the indicator value is above 0.6 or 60% probability,

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the blocks will be assigned as a category

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of “High” grow ability.

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If the indicator value ranges from 0.2 to 0.6,

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then it will be classified as medium probability.

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And if the indicator value is below 0.2,

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it will be assigned a ‘Low’ probability.

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As all variables created within the block models,

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we can review this data on a scene.

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Other tools available in block models are statistics,

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interrogate estimator, swath plots, and also reports.

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When creating a report, we must decide,

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what do we want to show.

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In this report, we are categorizing the data

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for geology domain and by probability.

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You can use any data to create indicator modeling.

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Just be clear your objectives

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and select the right tools available.

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If you would like to learn more,

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please contact us at [email protected].

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Thank you.