Our team of geologists brings a wealth of experience to the field of resource estimation, with extensive expertise in resource domaining and geological modelling. Our geoscientists and data scientists have experience in all commodities and deposit types, and collaborate with your team to create tailored solutions that will advance your project at any stage – from initial feasibility to production.
Across our resource estimation services, we add value for our clients by providing faster and lower cost results. Our team uses proprietary tools to automate the re-logging of core, the reinterpreting of existing geological models and data sets, and the integration of new and existing data to create new and more accurate inversions. These technologies allow us to produce resource domains that conventional methods would not be able to achieve.
We provide traditional deterministic and geo-statistical Interpolation as well as using complex multi-dimensional mathematics to generate models that have never before been possible. This process allows us to provide highly accurate Resource Domaining, ensuring a consistently high level of QA/QC compliance standards are met.
We combine data sets in unprecedented ways to create 2D and 3D models, using advanced machine learning algorithms to extract correlations and recognize patterns never seen before. Partitioning and isolating unique geostatistical populations allows us to work with grade estimations to rapidly and accurately calculate mineral volumes and values. As we further automate these processes, we are able to generate results quicker and more efficiently. We are also able to use these models as inputs for further machine learning, which increases the accuracy and relevancy of subsequent models.
Our ability to quickly generate accurate non-compliant resource estimates can guide further exploration in real time, ahead of compliant reports for the broader market.
The ability to accurately model ever deeper geological structures is becoming increasingly crucial, and the value of these models depends significantly on the amount and quality of the data integrated into their construction. As more and different datasets become available, knowing which aspects are the most valuable allows us to assemble more robust models, without reaching a point of limiting returns. Our ability to pre-condition or pre-process some of the data sets improves inputs and results in greater accuracy and clarity – Smart Targets that reduce your risk in making drilling, mining or infrastructure placement decisions.
This process allows us to partition geological envelopes into discrete domains that can each be accurately sampled for their mineral composition, to more accurately estimate the total ore volumes – identifying mineral resources and mineral reserves for planning purposes or ahead of listing.
We use proprietary software and machine learning technology to collect, clean and interpret a variety of data sets, efficiently modelling the mineralization of deposits and generating Smart Targets most likely to lead to discovery.
Geologists are always using points of data or sample points to estimate and predict values in other unknown areas – where lakes or geological formations or other complications result in a data set with gaps or underrepresented grid areas. We use mathematical modelling to computationally fill these gaps in a way that is mathematically robust, and can be further interrogated or revised. In addition to providing traditional deterministic and geo-statistical interpolation, our team uses post-graduate mathematics – like Gaussian process regression and other complex multidimensional math – to solve for these unknown regions. These models far exceed what is possible using typical software.
Ensuring the accuracy of anything that is done with the numerical data that comes from the assays is the foundation of our resource modelling technologies. Our team follows formalized and well-established workflows and verification measures to ensure the quality of the data supporting our interpretations, and to ensure compliance with international reporting standards.