In geospatial data interpolation, as in mapping, mineral resource estimation, modeling and
numerical modeling in geosciences, kriging has been a central technique since the advent …

Identification and categorization of geological, geotechnical, or geometallurgical domains is
a common practice in the modeling of mineral deposits, in order to account for the controls …

This study combines geochemical anomaly separation with geostatistical approaches and
compositional data analysis. To have a reasonable model for abnormal areas, suggesting …

The incorporation of geological controls is essential for an accurate assessment of the in-situ
and recoverable resources in an ore deposit, directly impacting the downstream stages of …

When working with hydrological data, the ability to quantify the similarity of different datasets
is useful. The choice of how to make this quantification has a direct influence on the results …

Classical tools for exploratory analysis of large geochemical datasets (eg, cluster analysis,
principal component analysis, etc.) have been successfully utilized for decades to …

Abstract Machine-aided geological interpretation provides an opportunity for rapid and data-
driven decision-making. In disciplines such as geostatistics, the integration of machine …

Identifying anomalies from geochemical data by modeling of the background and statistical
evaluation of anomalies is a major concern in geochemical exploration. This study …

The particularities of geosystems and geoscience data must be understood before any
development or implementation of statistical learning algorithms. Without such knowledge …

Classification of mineralized areas into different geochemical classes in terms of
prospectivity is crucial in the optimal management of exploration risk and costs. Machine …