While a kriged estimate is the standard for reporting and long-term planning. It inherently moderates local highs and lows. Conditional simulation is the geostatistical answer to this, generating multiple equiprobably realisations that reproduce the true variance and high-grade tail of your input data to quantify uncertainty.
Historically, running a simulation has been operationally challenging. Generating those realisations has meant exporting data to specialist software, writing custom scripts, and locking up your local hardware during intensive processing.
This white paper explores how Leapfrog Edge and Seequent Evo make conditional simulation practical. By offloading the heavy processing to the cloud, this integrated workflow brings uncertainty modelling directly into your everyday 3D environment, without exporting, specialist scripting, and hardware limitations.
