Tailings Storage Facility stability monitoring using CPT data analytics on the Zelazny most facility APCOM 2021

The Zelazny Most Tailings Storage Facility (TSF) is the largest reservoir of post–flotation tailings in Europe. The total amount of tailings from the mines in Lubin, Rudna, and Polkowice are stored each year at the disposal facility, which reaches approximately 30 million tons. It is crucial to accurately monitor and maintain such a huge storage facility, for the safety and economics of the entire region. In general, the monitoring network installed at the Zelazny Most TSF includes four groups of components: (a) geotechnical network (VW pore pressure transducers and inclinometers), (b) hydrological network (open piezometers, water level gauges, drainage discharge measurements, (c) geodetic survey network (e.g. surface and in-depth benchmarks, laser measurements, GPS measurements within GEOMOS network and (d) seismic network (e.g. accelerometer stations, cone penetration tests.) Apart from the above, chemical water analyses, tests of soil samples, monitoring of spigotting processes, and monitoring of relief wells’ discharge are also conducted. In total, approximately 2900 monitoring instruments are installed within the area of the Zelazny Most TSF. Such large amounts of data from the various information sources makes manual data analysis by the engineers almost impossible. In this paper we discuss a machine learning driven approach to improve the quality of the monitoring and maintenance of such facilities. We focus on the seismic cone penetration test (SCPT) data analysis. First, we carefully describe the collected data, its quality and availability. Then we discuss possible events that can occur in the TSF that need to be predicted using SCPT. Based on the information obtained in these two steps, the applicability of several machine learning methods is estimated. Some of them are then tested on available historical data. The algorithms described in this article will be further developed in the IlluMINEation project (H2020).