Multidimensional Passive Seismic Imaging of Legacy Tailings Storage Facility

Industrial mining activities have long driven the growth of civilization over the past hundred years. The large-scale mining activities, however, result in large amounts of residual waste in forms of e.g., rock waste and tailings. Engineered embankments or dams are typically used to contain these residual wastes, ensuring their long-term containment and stability. However, these dams can pose a risk of failure, and uncontrolled release of contained materials can bring harm to the environment and nearby communities.  The EU-funded project, MOSMIN (Multiscale observation services for mining related deposits), aims to integrate Earth observation techniques and in-situ geophysical survey for geotechnical and environmental monitoring, as well as the valorisation of mining-related waste. Within this framework, we explore the potential of passive seismic methods as a non-invasive approach for imaging and monitoring such mining waste deposits without the need for high impact seismic sources such as dynamite and vibroseis.

In this work, we present the first results of multidimensional imaging of a legacy Tailings Storage Facility (TSF) using a large-N passive seismic experiment to assess the geotechnical integrity of the legacy TSF, and its potential volume for valorisation. We deployed a total 200 autonomous seismic stations, consisting of 160 1-component and 40 3-components sensors on the surface of a TSF in a closed mining site in Laisvall, Sweden. The seismic stations were deployed as a grid array, with interstation distance of 20 m that covers the whole 500 x 700 m area of the TSF.

We applied the Horizontal-to-Vertical Spectral Ratio method to the 3-components seismic sensors to extract the Rayleigh wave ellipticity curves. The curves obtained were utilized to model the one-dimensional shear wave velocity (Vs) for every station. These values were subsequently interpolated to create a pseudo three-dimensional Vs model of the TSF. Additionally, we also applied Ambient Noise Tomography (ANT) to fully utilize the full coverage of the deployed large-N array. Preliminary results of this research show a robust Vs model that reveals the internal subsurface structure of the deposited tailings, highlighting areas with thicker deposits and lower Vs value. Ultimately, we discuss our results with respect to the implications for storage facilities safety and re-valorisation of the legacy deposits.