New Conditional Expectation

The Conditional Expectation is the expected value of a random variable, given that a certain set of “conditions” is known to occur (i.e., its conditional probability distribution). This technique, also known as multi gaussian kriging, lets you calculate estimations on points or blocks along with other essential results such as quantiles, confidence intervals, probability of exceeding a threshold, accumulations, etc. While its greatest advantage is its speed, though it is not suitable for multivariate datasets or filtering components, unlike simulations.

A fully redesigned Estimation Validation tool

The upgraded Estimation Validation tool provides greater flexibility compared to its predecessor. It boasts a newly designed interface similar to the EDA interface, along with more statistical outputs, including histograms (raw, cumulative or inverse cumulative), swath plots (possibly using a rotation), box plots (possibly between data from various data tables). With this tool, you can display multiple data tables and variables and customize the graphics to meet your specific needs.

New management of variables with a lot of zero values

The anamorphosis parameters now include a Zero Effect option to appropriately perform the gaussian transformation of raw variables (and reverse it) for datasets with many zero values or values close to the detection limit.

• Turning Bands and Sequential Gaussian Simulations now account for faults or geographical discontinuities. They are considered in the conditioning step and serve as “screens” to identify neighboring samples.
• The post-processing of categorical simulations has been enhanced with new outputs. These include the most and least probable facies, the absolute difference between the input proportions and those calculated from the simulations, the entropy, which indicates the average level of uncertainty in the variable’s possible outcomes, and the corrected most probable facies as defined by Soares.
• Plurigaussian Simulations have been enhanced with a new Conditioning option to reproduce the simulation variability better.

Boolean operations in the Mesh Editor

With the Mesh Editor, you can now easily calculate the union, intersection, or difference of multiple closed meshes. These operations can be recorded in a batch file.

Defining new values in the Variable Editor

Using the Variable Editor, you can now modify the value of structural variables such as point coordinates, drill hole depths, sub-block IJK, and polyline type. The tool ensures that you enter values consistent with the variable type and previous or subsequent samples (e.g., from/to values) and prevents you from making arbitrary inputs.

Now, it is possible to display 2D fence sections by loading a file containing the coordinates of the points that outline the path in addition to setting it interactively. This file can be a Shapefile, a CSV file, or an Isatis.neo polyline.

To enhance the view, you can add grid axes and locate the path points. The horizontal axis represents the distance along the path, while the vertical axis indicates its height.

• You can now store a list of cutoffs, thresholds, quantiles, percentiles, and confidence intervals in files for easy use across multiple applications.
• Besides graphics, you can save tables within chart files and quickly export their contents into CSV or Excel (.xlsx) files.
• The Leapfrog import interface now allows you to load lfm files in addition to the msh ones.
• When working with a large dataset that takes a long time to display in 2D or 3D, you can use the Properties before display mode to specify the graphic properties of the item before displaying it. This will help you save time and display the data more efficiently.
• When editing properties of a categorical variable (right mouse click), you can access the catalog and promptly add, remove, or modify a category.