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.
Geostatistics provides a probabilistic view to spatial prediction. The true spatial distribution of geologic properties is the result of a complex succession of physical, chemical and biological processes. A limited number of data are collected with error. Uncertainty arises because of this limited sampling and geological variability at all scales. We fall back on probabilistic tools to quantify uncertainty and generate numerical models of what the spatial distribution of geological properties might be like.
—Clayton Deutsch, 2015 on GeostatisticsLessons.com
RMSP has been developed from the ground-up, adhering to the highest standards of software development, testing and documentation. Core algorithms are implemented in C++ libraries for speed and portability. These numerical engines are designed for massive parallelization, allowing for powerful application on cloud instances of any vendor.
The geostatistical algorithms are accessible via a Python 3.6+ wrapper, whose API is designed to increase usability and reduce the time required for modeling setup and maintenance. The wrapper operates with core numerical Python packages including numpy, pandas, scikit-learn, and matplotlib. When 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 examples of the RMSP python wrapper.
RMSP is compliant with best practice estimation using legacy techniques such as variograms and kriging techniques, though it is aimed at modern probabilistic resource modeling. It seamlessly handles:
- Multiple data types, vintage, data with error and missing data
- Realistic decisions of stationarity, including local anisotropy
- Hierarchical structural, large scale and stochastic categorical modeling
- Multivariate and multiscale modeling with metallurgical and mechanical rock properties
- Uncertainty in all modeling parameters
- Uncertainty in all modeling outputs
- Optimization with appropriate utility
- Version and change control for data, parameters and models
- Disclosure of probabilistic resources
Interested in using RMSP?
Interested in applying Resource Modeling Solutions Platform to your resource modeling project?