Ambient seismic noise analysis of LARGE-N data for mineral exploration in the Central Erzgebirge, Germany

Abstract. Ambient seismic noise tomography is a novel, low-impact method for investigating the earth's structure. Although most passive studies focus on structures crustal scales, there are only few examples of this technique being applied in mineral exploration context. In study, we performed an ambient experiment to ascertain relationship between shallow shear wave velocity and mineralized zones Erzgebirge Germany, one important metal provinces Europe. Late Variscan greisen veins occurring Geyer-Ehrenfriedersdorf mining district Central were mined from medieval times until end 19th century. These occurrences represent significant resource commodities high economic importance, such as tin, tungsten, zinc, indium, bismuth lithium. Based data dense “LARGE-N” network comprising 400 low-power, short-period stations, innovative tomographic inversion based Bayesian statistics (transdimensional, hierarchical Monte Carlo search with Markov Chains using Metropolis/Hastings sampler) derive three-dimensional model. An auxiliary 3D airborne time-domain electromagnetic dataset used provide additional insight into subsurface architecture area. The model shows distinct anomalies down approximately 500 m depth that correspond known geological features study area, (a) gneiss intercalations mica schist-dominated host rock, imaged by SW–NE striking low-velocity zone moderately steep northerly dip, (b) NW-trending strike-slip fault, subvertical linear cross-cutting offsetting domain. Similar data, exhibit north-dipping (high-conductivity) schists, corresponding strike dip predominant metamorphic fabric. unsupervised classification bivariate yielded nine spatially coherent classes, which correspondence drilled roof granite pluton. relatively mean resistivity values class could be explained changes density composition during formation, observed other areas Erzgebirge. Our demonstrates great potential cost-efficient technology exploration, especially when combined independent geophysical datasets.