Three-dimensional block models are the most frequently used tool for estimating mineral resources and reserves within a mineral deposit. In open pit mining, the basis of mine design and the long term mining schedule is calculation of the ultimate pit limit. The ultimate pit limit is the pit with the highest profit value. Over the years, different algorithms have been developed that enable us to calculate the final pit: floating or mobile cone, floating cone II and its corrected forms, floating cone III, the Korobov algorithm and its corrected form, the Lerchs–Grossmann 2D algorithm (dynamic programming), and the Lerchs–Grossmann 3D algorithm (graph theory). All these algorithms have advantages and disadvantages. The floating cone method stands out for its simplicity, speed, and easy implementation, even for calculating a pit with a variable slope angle. The main drawback of this method is that it is unable to examine all possible combinations. For this reason, the algorithm does not consistently give optimal results, which is why it has required improvements over time. However, the improved methods still have some problems. To overcome these problems, a new algorithm called the floating cone IV method will be demonstrated in this paper.

The El Robledal deposit is a Mg-Fe-B skarn hosted in a dismembered block from the footwall contact of the Ronda orogenic peridotites in the westernmost part of the Betic Cordillera. The skarn is subdivided into two different zones according to the dominant ore mineral assemblage: (1) the ludwigite–magnetite zone, hosted in a completely mineralized body along with metasomatic forsterite, and (2) the magnetite–szaibelyite zone hosted in dolomitic marbles. In the ludwigite–magnetite zone, the massive mineralization comprises ludwigite (Mg₂Fe³⁺(BO₃)O₂), Mg-rich magnetite, and magnetite, with minor amounts of kotoite (Mg₃(BO₃)₂), szaibelyite (MgBO₂(OH)), accessory schoenfliesite (MgSn⁴⁺(OH)₆), and pentlandite. The ratio of ludwigite–magnetite decreases downwards in the stratigraphy of this zone. In contrast, the mineralization in the magnetite–szaibelyite zone is mainly composed of irregular and folded magnetite pods and bands with pull-apart fractures, locally associated with a brucite-, szaibelyite-, and serpentine-rich groundmass. The set of inclusions identified within these ore minerals, using a combination of a focused ion beam (FIB) and high-resolution transmission electron microscope (HRTEM), supports the proposed evolution of the system and reactions of the mineral formation of the skarn. The analysis of the microstructures of the ores by means of electron backscatter diffraction (EBSD) allowed for the determination that the ores experienced ductile deformation followed by variable degrees of recrystallization and annealing. We propose a new classification of the deposit as well as a plausible genetic model in a deposit where the heat source and the ore-fluid source are decoupled.

Variogram calculation is a fundamental tool for studying ore grade data in mineral deposits. It allows the discovery of hidden structures within the data and preferential directions of mineralization where the geological continuity is longer. The variogram allows us to classify samples and to define both the search radii for interpolation and the use of kriging as an interpolation and resource classification method. It is not difficult to use it in one dimension; complexity increases in two dimensions as the necessity to search for possible grade anisotropies in all directions arises. Three-dimensionally it is even more difficult to try to define the parameters of lag, bandwidth, and tolerances that define the data of the variogram calculation algorithm. There are rules of thumb to help in the development, but a trial-and-error approach is used in order to find enough sample pairs to allow the generation of variograms truly representative of the data. In this paper, two strategies are shown to help in variogram construction, the use of variogram maps and the graphical representation of the pair search areas (cones or pencils). The freeware tool RecMin Variograms has been developed and is freely available for download at its website; it is easy to learn and use. R code based on RGeostats libraries is used to check the operation and results of RecMin Variograms. Applying geostatistics to mineral deposits is essential to know the areas with the highest grades, thus allowing exact planning of future mining exploitation and minimizing mine footprint and environmental impact.

A fundamental aspect in the evaluation of mining projects is the classification of mineral resources, as it directly influences the definition of mineral reserves and affects both the planning and operational phases of the mine. Traditional methods employed in the industry are based on geometric or geostatistical criteria which, while constituting the fundamental basis of the process, may prove insufficient when applied in isolation to reflect the uncertainty inherent in the databases used for the evaluation of mineral deposits. As discussed throughout the article, this limitation can lead to an incorrect or imprecise assignment of resource categories. This work presents a methodology to integrate variables related to sample quality as an additional criterion in resource classification. This allows for the identification of areas with greater uncertainty and the adjustment of their categories more consistently with data reliability. The effectiveness of the proposed method is demonstrated through its application to a real case study, complemented by a comprehensive analysis of its implications and results.

Three-dimensional block models are the most widely used tool for the study and evaluation of ore deposits, the calculation and design of economical pits, mine production planning, and physical and numerical simulations of ore deposits. The way these algorithms and computational techniques are programmed is usually through complex C++, C# or Python libraries. Database programming languages such as SQL (Structured Query Language) have traditionally been restricted to drillhole sample data operation. However, major advances in the management and processing of large databases have opened up the possibility of changing the way in which block model calculations are related to the database. Thanks to programming languages designed to manage databases, such as SQL, the traditional recursive traversal of database records is replaced by a system of database queries. In this way, with a simple SQL, numerous lines of code are eliminated from the different loops, thus achieving a greater calculation speed. In this paper, a floating cone optimization algorithm is adapted to SQL, describing how economical cones can be generated, related and calculated, all in a simple way and with few lines of code. Finally, to test this methodology, a case study is developed and shown.