Seismic interferometry under dominant anthropogenic noise

Seismic monitoring in active mining environments is strongly conditioned by the presence of intense and highly variable anthropogenic noise sources, which are often considered a limitation for conventional ambient-noise–based approaches. However, these sources also represent a persistent and information-rich wavefield that can be exploited if properly characterized and handled.

In this contribution, we focus on mining settings where seismic noise is dominated by anthropogenic activity (e.g., machinery, blasting-related processes, and operational cycles) and present a methodology specifically designed to turn these coherent but non-stationary sources into a useful signal for subsurface monitoring. The approach is based on seismic noise interferometry, combined with tailored preprocessing and source-selection strategies that enhance the stability and interpretability of the retrieved waveforms under strongly time-varying noise conditions.

We show how this method allows us to extract robust information on subsurface dynamics in mining-related contexts, with particular emphasis on applications such as tailings dam monitoring and near-surface mechanical stability. The results demonstrate that, rather than being an obstacle, anthropogenic noise can be systematically leveraged to improve seismic monitoring in active resource-extraction environments, opening new perspectives for environmental risk assessment and sustainable mining practices.