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Public reports and publications


Application of geostatistical analysis interacting with the earth observation data for recovery of raw materials from mining residuals (stockpiles and tailings): research projects at University of Bologna.

Authors: Sara Kasmaee, Emanuele Mandanici, Francesco Tinti, Stefano Bonduà, Roberto Bruno

Keywords: earth observation, geostatistics, raw materials, recovery

Abstract: The poster presents an overview of the ongoing research projects at University of Bologna – DICAM Department, applying geostatistical methods to mining stockpiles and tailings with the purpose of metal recovery. The educational program RawMatCop of EIT Raw Materials is the main supporter of the research. The work takes advantage of the use of Earth Observation (EO) data for sampling optimization in mining residuals from abandoned and active mines. Purposes are both recovery of raw materials and environmental rehabilitation of tailing dams and landfills. EO can play an important role in accounting the raw material resources of a territory, since current satellites, such as the Copernicus constellations (Sentinels), provide continuous spatial and temporal coverage of the global at no cost. Thanks to the spatial resolution, Copernicus data can improve the characterization (quantification and evaluation) of a resource, together with the assessment of the associated risks. Moreover, EO can be used for continuous monitoring of the target areas, conditioned by mining activites. On the other hand, geostatistical analysis, using in situ sampling and EO images, exploit innovative methods to improve accuracy of grade and pollution maps, thus reducing the number of samples, with evident cost reduction. Test sites are bauxite residuals, located in Mediterranean Region: Greece and Montenegro (under analysis, 2019), Sardinia and Apulia (programmed work, 2020). Finally, a new international Project, INCO-Piles, starting in early 2020 and led by the research group, has the scope to identify the most promising mining residuals of Southern Europe for recovery of critical raw materials.

Poster session, Conference on Mining the European Anthroposphere, 20-21 Feb 2020, Bologna


Evaluating the correlation between ground information and satellite spectral data by geostatistical tools

Authors: Roberto Bruno, Sara Kasmaee, Francesco Tinti, Emanuele Mandanici

Keywords: satellite data, geostatistics, kriging of components

Abstract: Satellite information opened new scenarios for planet surface mineral exploration. Hyperspectral information brought by sensors on board potentially help identifying and measuring concentrations of an element if an accurate calibration is done, based on available ground sampling. Most popular uses of satellite images refer to 2D problems and most calibrations refer to the spectral properties of the objects to be discovered and characterized (Follador M., 2005). Before calibration, images are affected by standard preprocessing, for instance for filtering unwanted effects, and for enhancing the information considered useful. The general problem for mineral exploration and reserves characterization is the spatial distribution of the target variable, with limited and sparse in situ information. Satellite images provide auxiliary information, which can be used, when correlation is found with the target variable. In this case the expected result should improve the estimation or the representation of spatial distribution of the sampled variable. Independently of the variable at hand (grades, discovery probability, …) and of the spatial distribution model (estimation, simulation), Geostatistics allows to tackle the central problem: finding meaningful correlations and modelling the unknown surface distribution of the interest variable by including satellite data as auxiliary information (Chiles et al., 2012; van der Meer, 1994). Three issues need attention when considering satellite images: a) the different support of direct and auxiliary information, being pixel data refereed to a surface, in contrast with the punctual ground data b) the need of 3D modelling c) the space-time nature of the satellite information. The correlation study is one of the most delicate phases when using satellite images for improving the models’ quality of surface distribution of a target variable. Geostatistics offers a wide variety of powerful tools for a deep study of these correlations. A short case study is reported as an example where it was identified the most correlated spatial component by a multivariate structural analysis.

Oral Presentation, Mineral Exploration Symposium, 17-18 Sep 2020 Online Event

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