Neste vídeo, a geóloga de projetos da Seequent, Paulina Cortez, fará um rápido tour sobre como usar a modelagem de indicadores em seu fluxo de trabalho para estimar e codificar blocos usando a funcionalidade da extensão Contaminantes para o Leapfrog Works.
Project Geologist, Seequent
<v Narrator>Welcome to Leapfrog Works</v>
and its Contaminants extension.
In this video,
we’ll give you a quick tour through our workload
that will help you use indicator modeling,
to estimate and code blocks using the functionalities
of the contaminant extension.
In this project,
we have already coded our contamination data
into zeros and ones,
using a threshold of four PPM.
For this video,
we’ll use the Contaminant Models folder that becomes
available, once the contaminant extension is activated.
To start with this workflow,
we must right click on the estimation folder
and create a new estimation domain.
Then we select the data set we want to estimate
and the estimation domain.
In this scene, the already coded water data set is projected
within the select geological domain
of unconsolidated sediments.
A spatial model allow us to analyze
the spatial variability of values within the domain.
We will be able to model our 3D variogram as an input
to the estimator with the assistance of Leapfrogs
highly connected 3D scene.
It allows for real time visualization
of your experimental variograms with our 3D scene,
making the process more intuitive.
Anytime we change a parameter within
our experimental variogram or the variogram model,
we’ll see how our variography change.
When we right-click on the estimation folder,
we can select which estimator we want to use.
For this workflow, we’ll use simple grading.
We set the parameters for our estimation,
including the created 3D variography.
Define the search ellipsoid ranges, the search definition,
and finally, the required outputs.
Once the domain, the spatial model,
and the estimators are set, we must create a block model,
to code our estimated indicator values.
Creating a block model is a straightforward workflow
and a great way of representing contaminants models
We can adjust our parameters and select which models
and estimation will be represented at (inaudible).
In this case,
we have evaluated several geological and numerical models,
as well as different estimators.
All this information can be reviewed in the scene in a very
When indicators are coded into block models,
we can analyze the probability of each block to be above
or the final threshold.
In this case four PPM chloride.
We can filter the block to this value
and observe the distribution of the plume.
Calculations are available in block models to visualize
new created variables and to report
mass and average concentration, for example.
In this case,
we have created a new category defined by
the estimated indicator.
If the indicator value is above 0.6 or 60% probability,
the blocks will be assigned as a category
of “High” grow ability.
If the indicator value ranges from 0.2 to 0.6,
then it will be classified as medium probability.
And if the indicator value is below 0.2,
it will be assigned a ‘Low’ probability.
As all variables created within the block models,
we can review this data on a scene.
Other tools available in block models are statistics,
interrogate estimator, swath plots, and also reports.
When creating a report, we must decide,
what do we want to show.
In this report, we are categorizing the data
for geology domain and by probability.
You can use any data to create indicator modeling.
Just be clear your objectives
and select the right tools available.
If you would like to learn more,
please contact us at [email protected]