2D body-wave seismic interferometry as a tool for reconnaissance studies and optimization of passive reflection seismic surveys in hardrock environments

Abstract

Despite the unrivalled spatial resolution and depth penetration of active-source seismic methods used for mineral exploration in hardrock environment, economic and environmental restrictions (e.g., source permitting) may preclude its full-scale application. In such a case, 2D passive reflection seismics can be considered a cost-effective way to perform reconnaissance-type survey and provide body-wave structural imaging using ambient-noise seismic interferometry (ANSI). This is, however, conditional to the presence of noise sources in the subsurface, for example produced by underground mining activity. Here, we propose a 2D ANSI workflow as an intermediate step prior to a full-scale 3D ANSI survey and an affordable tool in brownfield exploration, e.g., when trying to update current geological models beyond the drilled area. We test the applicability of this approach by analysing selected receiver lines from a 3D passive dataset acquired over the Kylylahti mine in Finland. Our methodology aims at choosing the optimal processing strategy at possibly lowest acquisition (2D geometry) and computational (small amount of data) cost. We address the fundamental questions in ANSI, i.e., (i) how much AN should one record and (ii) which SI processing approach should one choose. Therefore, we test different processing steps necessary to produce virtual shot gathers (VSG): preprocessing, selection of the ambient-noise portion, and selection of the method for retrieving the impulse responses between the receivers (crosscorrelation - CC, crosscoherence - CCh, multidimensional deconvolution - MDD). We conclude that trace energy normalization and high-pass filtering are the preferred preprocessing steps, while the best imaging is obtained when VSGs are retrieved using MDD applied in the noise-volume approach or CC in the event-driven approach. An event-driven approach may significantly reduce the acquisition time: for the Kylylahti dataset, using 10 events with energetic body-wave arrivals, extracted from one hour of data, was enough to provide results comparable to the results from the noise-volume approach using the complete one hour of noise.