## 1 Introduction
### Mine Tailings Storage Facility Dam Failures: Geotechnical Human Errors or Unavoidable Accidents
central computer or datalogger for storage, (iii) process the stored data, and (iv) display the information in a visual, graphic, or numerical form on screens called dashboards. The initial level of data processing entails filtering the noise from the signals to improve the visualization. The second level of data processing includes making predictions of how the captured signals may vary in the future [34]. The ability to compare present or future signals with predefined variation ranges and, based on this comparison, generate an alarm when these ranges are exceeded or expected to be exceeded based on the calculations can be added to these functions [35]. In all of these cases, the monitoring systems involve only signal processing, although variants of these systems incorporate image processing, or the transformation of signals and images into, for example, the frequency domain [36]. The applications of monitoring systems with the indicated characteristics are numerous; for example, in the process industry, they can be used to evaluate performance indicators (KPIs) in real-time or to detect equipment failures in advance. Many monitoring systems developed for the industry use standard industrial products, such as programmable logic controllers (PLCs), supervisory control and data acquisition (SCADA), and distributed control systems (DCS), which are marketed by companies that supply automation equipment, systems, and services [37]. Thus, mining monitoring systems that implement these technologies are transforming the productive activity of the real world into a virtual world that is also known as the metaverse, where the collection of large-volume data in real time and their processing permits better holistic and prospective decision-making [38; 39; 40; 41]. These technological improvements are driving changes in how mine tailings governance occurs; for example, sensors are gradually changing from mostly analog to mostly digital [13]. Similarly, the implementation of the Internet of Things (IoT) allows different sensors to be connected through the Internet and offers a powerful means of connectivity and data transmission via cloud storage and computers to implement statistical and probabilistic data analysis algorithms [42; 43; 44]. TSF monitoring, along with its design, construction, and operation, is critical for maintaining safe infrastructure. In this regard, a sensor system (instrumentation), as in the example shown in Figure 2, could support data gathering and processing, enhancing related activities such as (i) evaluating performance by comparing daily expectations with data, (ii) displaying early warnings about issues that could affect the integrity of the infrastructure, (iii) increasing knowledge of infrastructure behavior, (iv) assisting in the investigation and diagnosis of abnormal phenomena, (v) understanding the behavior of tailings in the short, medium and long term, and (vi) mitigating negative environmental impacts [45; 46; 47]. Figure 2: Example of a sensor monitoring system (piezometers) in a mine tailings storage facility dam [48]. Figure 2 shows a cycloned tailings dam that needs to be monitored with sensors to study the behavior of the water pore pressure and prevent dam failure due to tailings' dynamic liquefaction phenomena in response to earthquakes.