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.

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.

No abstract