Passive Seismic Reflection Imaging in Active Mining Environments: A Directional Energy Balancing Strategy Applied to the Huoshaoyun Deposit, Tibet Plateau

Deep mineral exploration in high-altitude permafrost regions, such as the Qinghai-Tibet Plateau, faces severe challenges due to complex topography, fragile ecosystems, and intense industrial noise. While passive seismic reflection imaging offers an eco-friendly alternative to active sources, its reliability is often compromised in active mining areas where the ambient noise field is strongly directional and non-stationary, violating the stationary phase assumption required for interferometry.

In this study, we present a successful application of passive seismic reflection imaging at the Huoshaoyun super-large lead-zinc deposit (>5,000 m elevation) in Xinjiang, China. To overcome the artifacts induced by strong directional noise (e.g., heavy mining trucks and machinery), we propose a novel wavefield reconstruction method based on Directional Energy Balancing in the frequency-wavenumber (f−k) domain. Unlike traditional linear stacking, our approach introduces a Directionality Index (DI) to quantify the energy asymmetry of noise slices. We implement a "bucket balancing" weighting strategy that actively screens and balances the noise energy flux, constructing a virtual isotropic illumination environment. This process effectively suppresses spurious artifacts and significantly enhances the signal-to-noise ratio of body-wave reflections.

Utilizing 31 days of continuous waveform data from a dense linear array of 500 short-period seismometers, we retrieved high-resolution reflection profiles reaching 2 km depth. The imaging results clearly reveal the spatial geometry of ore-controlling syncline structures and interlayer fracture zones. These geophysical interpretations were validated by subsequent drilling, demonstrating a high consistency with geological facts. Our findings indicate that the proposed directional balancing strategy can turn "noise into signal" even in strongly heterogeneous noise environments, providing a robust, low-cost, and non-invasive solution for deep resource exploration in extreme environments.