By Silvia Misk.
Conditional simulation helps geologists visualise and quantify uncertainty. It’s a proven method, but one that has historically required expert-level skills to use.
Now, with a recent Leapfrog release, conditional simulation is available through an intuitive new workflow in Leapfrog Edge.
In this article, you’ll learn about the benefits of conditional simulation, how this method fits into the Leapfrog ecosystem today, and what’s required to use it effectively.
What is conditional simulation?
Conditional simulation is a well-established geostatistical method that quantifies uncertainty by generating multiple equally probable scenarios (or “realisations”) of a continuous attribute, like grades, in the space. Instead of producing a single best estimate like Kriging, conditional simulation shows a range of equiprobable outcomes.
Think of it like forecasting your investment portfolio. Kriging tells you, “You’ll make $1M.” Conditional simulation shows you a spread, such as “You could make anywhere between $500K and $2M.” That additional insight helps guide smarter decisions, especially in the presence of uncertainty.
In early exploration or feasibility studies, it helps assess a range of potential outcomes for tonnage and grade. In production, it improves block classification by accounting for uncertainty in grade estimates, helping teams make better decisions about what to treat as ore or waste. Wherever it’s used, conditional simulation offers a more complete picture of geological risk and variability.
Despite its value, conditional simulation is not widely used. Historically, it has been complex to set up, slow to run, and required significant computing power. Interpreting the results often demanded specialist expertise, which limited its use to advanced studies rather than routine workflows. While some use cases remain periodic by nature, Seequent’s new implementation makes conditional simulation practical enough to support regular use cases like grade control in day-to-day or weekly production planning.
Bringing conditional simulation into the Leapfrog workflow
In 2025, Seequent added conditional simulation to Leapfrog Edge.
The new workflow brings conditional simulation into the Leapfrog interface using familiar tools and visuals, making what more accessible and practical.
Conditional simulation in Leapfrog Edge leverages Seequent Evo for the computationally intensive tasks of conditional simulation.
Rather than waiting on your local machine, the workflow uses Evo’s cloud infrastructure to run simulations, then streams the results back into Edge for post-processing, classification, and visualisation.
What is Seequent Evo?
Seequent Evo is a geoscience data and compute platform that enables integrated workflows and collaboration across Seequent and third-party products. It powers geoscience solutions for data processing, modelling, and insight generation. This drives innovation and enables users to continuously improve their workflows and business with open APIs and data formats.
Right now, there is a dedicated use case for grade control. Users can combine simulated grade variability with economic inputs such as mining cost, recovery rates, and cut-off grades or minimum metal price. This allows them to categorise blocks more confidently as ore or waste. Instead of relying on a deterministic value, the workflow incorporates uncertainty in grade estimates to support more robust classification based on profit and loss scenarios.
What you need to get started
You set up your workflow in Edge. Once configured, Edge sends the job to Evo. Results are returned and can be stored in BlockSync, Seequent Evo’s cloud-native data manager for block models. Behind the scenes, the cloud-based geostatistics API in Evo handles the heavy processing.
Continuous innovation through Evo
Today’s conditional simulation workflow is just the beginning. Seequent’s long-term vision is to provide a modern user experience across a comprehensive suite of geostatistical tasks. That means making them faster, more accessible, and more integrated across the Leapfrog and Evo ecosystem. And because the workflow runs on Evo, many of these improvements will be released continuously, without waiting for a major Leapfrog update.
A new era of accessible risk modelling
This release is just the first step toward broader, more scalable risk modelling and uncertainty analysis across the mining lifecycle.
If you already use Leapfrog Edge and have access to Evo, you can start exploring conditional simulation today. If not, this may be the perfect time to consider adding Evo to your toolkit.