Abstract

In the context of mining exploration, local earthquake tomography serves as a valuable complementary tool, applicable across varying scales from greenfield to brownfield projects. Nevertheless, interpreting body-wave velocity anomalies within tomographies poses a significant challenge, which largely depends on the expertise of the analyst and the availability of information. Addressing this challenge, this paper proposes a geostatistical analysis to effectively compare and enhance the information extracted from tomographies ranging from lower to higher resolutions. The data utilized in this study correspond to the tomographic inversion values of Mantos Rojos (MR) and Radomiro Tomic (RT) porphyry copper deposits situated within the Chuquicamata District in northern Chile. MR has a resolution of 2 x 2 km(2), comparatively lower than RT's resolution of 1 x 1 km(2), yet both share the same spatial zone. This study evaluated the discernment capabilities of lower-resolution tomography (MR) in comparison to its higher-resolution counterpart (RT) using turning bands simulation. The simulated Vp/Vs values of MR were compared against RT seismic tomography data. Visual validation revealed that simulated Vp/Vs values from P- and S-wave velocity values of MR can identify the low Vp/Vs anomalies (< 1.7). Moreover, spatial analysis compared the experimental variograms for MR realizations and for RT values in preferential directions for Vp/Vs ratios, finding a correspondence between both spatial tools. Finally, geological validation was carried out by comparing the simulation results with geological maps of the study area and copper grades obtained through drilling campaigns provided by CODELCO, where spatial patterns indicative of mineralization and larger-scale geological features like the West Fault were identified. Our research has practical implications because, through geostatistical simulations, the grid dimensions of seismic tomography of MR can be reduced and still identify low Vp/Vs anomalies within the area of study, being consistent with the lower-resolution validation grid of RT. Our findings demonstrate the efficacy of geostatistical methods in enhancing exploration decision-making by providing insights into subsurface geological features and their relationship to mineralization. This approach not only improves the efficiency and success rate of mineral exploration projects but also minimizes environmental impact by allowing for more targeted and informed exploration activities.