What is RMSP and how does it work?
The Resource Modeling Solutions Platform (RMSP) is a library for solving resource modeling challenges. RMSP implements core geostatistical modeling algorithms for the modeling and management of uncertainty in the earth sciences. RMSP is used by leading mining, petroleum, and consulting companies to quantify uncertainty, rapidly generate resource estimates, and apply modern geostatistical algorithms to their projects. RMSP enables you to use the right tools for your geostatistical modeling challenge, whether it is determining how many drill holes you need for your model to be within 15% on an annual basis, or generating short term models on the fly as new production data lands in your database.
RMSP provides more than 100 geostatistical modeling functions, from generating block models around your data, to variogram calculation and modeling, estimation with kriging, uniform conditioning, indicators, to advanced simulation technology with hierarchical truncated plurigaussian simulation and projection pursuit multivariate transform simulations. A tiny example using a set of copper blast holes:
RMSP works within the Python ecosystem so you can integrate directly with other data analysis and machine learning libraries. Interaction happens with Python, but underneath it is a fully parallel high performance library enabling you to model hundreds of millions of blocks for dozens of variables simultaneously. RMSP can be used on your laptop or on powerful cloud virtual machines and distributed environments. Set up a simulation locally then push it up to your corporate cloud to crunch hundreds of realizations and quantify uncertainty in your deposit on a Windows or Linux environment. RMSP is backed by our team of consultants who can assist with implementing and auditing workflows.
RMSP is fully documented with hundreds of small examples and more than 60 fully worked examples covering resource modeling challenges ranging from calibrating an ordinary kriging estimate for resources, to applying localized conditional simulation, to generating fully locally varying variograms and generate short term models for mine planning. When RMSP is combined with scripting interfaces such as Jupyter Notebooks, RMSP provides transparent and auditable workflows that are automated to the correct extent for rapid model deployment. Please refer to Highlights for a few more examples of the RMSP python wrapper, or get in touch to try RMSP for yourself on a cloud environment.