Most geological models are still user-driven. Driver closes the gap between available data and extracted insights from it. Using machine learning to detect and quantify structural trends directly from your data, it gives geoscientists a faster, more objective foundation to build on. Driver shifts the paradigm from user-driven to data-driven. The result is a structural foundation that is faster to build, easier to defend, and continuously informed by everything your data has to say. The post A data-driven pathway to reliable geological models appeared first on Tech Research Online.
A data-driven pathway to
reliable geological models
Your next discovery is already in your data.
Most geological models are still user-driven. Driver closes the gap between available data and extracted insights from it. Using machine learning to detect and quantify structural trends directly from your data, it gives geoscientists a faster, more objective foundation to build on. Driver shifts the paradigm from user-driven to data-driven. The result is a structural foundation that is faster to build, easier to defend, and continuously informed by everything your data has to say.
In this white paper, you’ll learn:
- How Spatial Continuity Mapping reads geological continuity in 3D from your drilling data
- How OceanaGold found over 2,000 additional gold ounces from existing data in less than an hour
- How Driver clusters its structural trends to segregate domains and isolate individual veins
- Driver closes the loop from discovery to estimation, supporting LVA for accurate estimates
Download the white paper
Download the white paper
About Seequent
Seequent, part of Bentley Systems, is a company specializing in software for understanding and visualizing subsurface data. They provide tools for geoscience analysis, modeling, and data management, particularly for the mining, civil, energy, and environmental sectors. Seequent’s technology helps organizations make better decisions about subsurface resources and infrastructure.
The post A data-driven pathway to reliable geological models appeared first on Tech Research Online.
