Designing a Geostatistical-Based U-Spatial Statistics Algorithm for the Separation of the Anomaly Area: Application at Baghqloom Porphyry Copper System, Southeastern Iran - Mining, Metallurgy & Exploration (2021)

Separation of geochemical anomalies from the background using various methods for identifying areas of higher mineral potential is a critical first step in Greenfield and Brownfield programs. There are several methods for differentiating anomalous regions from the background. The U-spatial statistics is an effective technique that provides the opportunity of reaching this goal by considering the geochemical sample locations in the separation of geochemical anomalies. In this study, a developed form of the U-spatial statistics method is introduced, which not only considers the existence of anisotropic spatial variations in input data but also reduces the computational (CPU) time. While the previous method is based on the isotropic assumption in the study area and a set of search windows at different times, here, the geochemical anomaly is determined by pasting only one ellipsoid window, effectively combining the flexibility of the U-spatial statistics method with the advantages of geostatistical approaches. This paper compares various approaches (the previous and developed version of the U-spatial statistics method, concentrationarea (C–A) fractal model, and probability diagram modeling) for the separation of anomalous areas in the presence of heterogeneous spatial variations. This is done with an application to a Baghqloom porphyry copper deposit located in southeastern Iran. The high potential areas identified via different approaches show an anomalous region in central parts of the Baghqloom area, which partially coincides with an intense potassic alteration area. Compared to other methods, our algorithm separated the high potential areas at a greater spatial resolution.