• Expertise in geological and resource modeling
    Ore Body Modeling


Exploratory data analysis is the application of a variety of techniques to maximize the insights of the dataset, clean the original information, uncover underlying structures, relevance feature detection, and develop parsimonious models.

Univariate and multivariate statistical and spatial analysis
Compositional data analysis
Contact analysis as grade transition between domains
Definition of geological and estimation units
Modeling workflow definition


Geological modeling delineates the spatial boundaries of geological units, ensuring attributes within these units are consistent and homogeneous in terms of statistics, geology, and spatial orientation. This process heavily relies on expert knowledge and data analysis, and its outcomes can vary based on the assumptions and techniques used.

Conventional: inverse distance, nearest neighbor, indicator (co)kriging
Tessellations, object-based models, process-based and process-mimicking models
Truncated Gaussian and hierarchical pluri-Gaussian simulation
Multiple-point statistics: Snesim, Direct Sampling, Simulated Annealing, and RCNN
Machine learning: geochemical-based unsupervised classification
Multi-source data integration


Predicting grades, density, and geometallurgical attributes at unobserved locations, both at specific points and larger block areas. Our process is both traceable and reproducible, utilizing respected industry-standard techniques.

Variographic analysis
Inverse distance weighting, and Nearest neighbor estimation
Kriging estimation: ordinary, simple, indicator, (co)kriging, and others
Kriging Neighborhood Analysis
Geostatistical simulation: Sequential Gaussian simulation and Turning Bands
Validation, change of support, and post-processing
Quantification of uncertainty in ore-reserve estimation.


Reserve quantification is the process of categorizing mineral resources into reserves, taking into account both technical and economic factors. This assessment considers various modifying factors, including mining, processing, metallurgy, economics, market conditions, legalities, environmental concerns, infrastructure, societal implications, and governmental regulations. At APMT, our assessments adhere to top international standards and are spearheaded by certified Qualified People (QP)

Mineral resource reporting under international code standards


Drilling strategies designed to minimize spatial uncertainty and enhance geological understanding

Spatial uncertainty reduction via optimal sampling considering efficiency on capital expenditures and operational expenses
Entropy-based optimal sampling