LAUSR

Abstract Estimation of the number of undiscovered mineral deposits is critical for quantitatively assessing mineral resources. Traditional statistic methods are limited to analyzing the spatial distribution of mineral deposits which can be considered spatial point processes because of complexity, self-organized criticality and singularity properties. In this study, spatial statistic methods, including average nearest neighbor distance, Fry analysis, K function and fractal analysis, are used to quantify the agglomeration and fractal characteristics of tungsten polymetallic deposits in the eastern Nanling metallogenic belt (ENMB). Local singularity analysis for W-Sn-Mo-Bi-Be association is used to delineate the permissive areas for tungsten deposits. The results show that tungsten polymetallic deposits are distributed with fractal clustering in region while at random in permissive areas. The summary function comparison shows that the K function analysis based on a heterogeneous process may be more robust than on a homogeneous K function when quantifying the distribution of mineral deposits, especially for endogenic deposits. Furthermore, the concept of mineral equivalent is introduced in this study and the combination of mineral equivalent and radial density model for mineral deposits mitigates the problem of ignoring the proportion of large-, medium-, and small-sized deposits in the uniform deposit density model. The estimation results indicate that there are still good future prospects in this region. This research not only extends the application of spatial statistics in quantifying the spatial distribution of mineral deposits, but also provides a new approach to estimate the number of undiscovered mineral deposits.