Isatis.neo Fundamentals | Training course

Get up to speed with Isatis.neo: learn to navigate and apply core features with ease.

Objectives

Isatis.neo offers a streamlined, powerful environment for exploring spatial data, creating accurate models, and easily quantifying uncertainty. This course ensures that, in just one day, you’ll feel confident in boosting your analysis and incorporating best-practice geostatistics into your daily projects.

Course content

  • Isatis.neo overview
    Navigate the intuitive user interface, 3D viewer, Python-powered calculator, and batch automation tools.
  • Data import
    Import diverse data types, including points, block models, and wireframes, and prepare datasets for analysis.
  • Data analysis
    Perform robust exploratory data analysis (EDA): QC with histograms, scatter plots, anisotropy analysis, outlier detection, trend analysis, and variography (2D & 3D).
  • Estimation
    Conduct estimation workflows including neighborhood analysis, kriging (point and block), cross-validation, and model validation.
  • Conditional simulations
    Get an introduction to conditional simulations to better assess uncertainty.

Outlines

  • Hands-on software training: Practice with real-world datasets and receive a temporary Isatis.neo license.
  • Personalized feedback: Receive individualized guidance and feedback from experienced trainers during online sessions to support your learning journey.
  • Comprehensive resources: Access detailed course materials, including documentation, journal files, and datasets, to reinforce learning and facilitate application post-training.

Who should attend

This course is ideal for professionals with a foundation in geostatistics who want to take full control of their spatial data workflows in Isatis.neo, whether you work in mining, petroleum, or environmental science.

Prerequisites

This course is dedicated to practical exercises with Isatis.neo, and no theoretical reminders about geostatistics will be provided. Participants are, therefore, required to have a fundamental knowledge of geostatistics.