The safety of tailings storage facilities and mining assets is facing a paradigm shift. In today's digital era, intelligent data management is the critical success factor — which is why adopting AI and advanced technologies has become imperative.
This article looks at how integrating geospatial intelligence, on-site instrumentation and deep-learning models makes it possible to anticipate structural failures weeks in advance. YNSAT uses this approach to define asset risks more precisely and reliably, removing the blind spots of conventional management and supporting compliance with the most demanding governance standards, such as the GISTM.
How would geospatial intelligence have changed the fate of Aznalcóllar and Brumadinho?
The history of modern mining is marked by events that shaped today's regulation. The sector has, sadly, witnessed catastrophes that not only devastated entire ecosystems and contaminated vital resources, but also cost hundreds of human lives — leaving an indelible scar on our collective memory.
In Spain, the Aznalcóllar disaster (1998) is the closest reminder. The failure originated from a deep-seated slide in the clayey ground beneath the dam. This foundation failure showed that deep precursor movements can go completely unnoticed by the monitoring systems of the time — systems limited exclusively to point on-site instrumentation and periodic visual inspection.
Years later, the Brumadinho disaster (2019) in Brazil shook the world again. The dam's collapse unleashed a wave of mud that buried facilities and communities, killing 270 people in an environmental catastrophe without precedent. This event proved that, without an integrated view combining surface-deformation measurement with thorough control of internal moisture, structural instability and liquefaction phenomena remain a latent threat to safety in the mining industry.
17 Jan 2019 · before
01 Feb 2019 · afterHistorically, the mining industry has carried the structural challenges typical of a traditional sector. One critical, often-overlooked problem is the fragmentation of data. Ground instrumentation — piezometers, inclinometers and the like — offers a point, isolated view, leaving large areas of the asset under constant informational uncertainty. In this context, if a failure begins outside the reach of a physical sensor, the system stays completely blind to the risk.
The future of critical-asset monitoring: the 3 technical pillars
Overcoming these technological limits requires a transition toward a multi-instrument monitoring model, where satellite observation and on-site measurement stop being independent layers and form a "single source of truth". This methodological approach — developed by the Spanish geospatial-intelligence company YNSAT — does not seek to replace conventional instrumentation, but to give it complete, continuous spatial context through three complementary technical pillars.
Active satellite monitoring (InSAR): millimetric surface displacement
Using InSAR (Interferometric Synthetic Aperture Radar), the system continuously measures surface deformation from space. Drawing on public and private satellite data, this non-invasive method dispenses with physical contact in risk zones, tracking every millimetric displacement of the asset to ensure its stability and optimisation.
Optical remote sensing: moisture and free-liquid control
Optical sensors capture the data, detecting free liquid, seepage or changes in ground moisture. It is a fundamental early-warning tool for identifying water-saturation processes that precede failures by liquefaction — or by collapse from loss of soil cohesion and rising internal water pressure.
On-site instrumentation: the ground truth
Remote monitoring does not aim to replace traditional in-mine sensors, but to extend their reach and put them in context. Data from piezometers, inclinometers and topographic surveys, among others, are integrated to act as the final ground validators.
"An isolated signal rarely tells the whole story. Risk is read far more reliably when several signals move at once: a deformation, a change in moisture, a pore pressure out of range." Samuel Álvarez — CTO & Co-Founder, YNSAT
This combined architecture ensures unprecedented 360° surveillance. The system acts as a mutual backup: in extreme weather conditions that limit remote monitoring, for example, on-site instrumentation stays on watch; if a physical sensor fails, satellite coverage prevents operational blindness. The result is the removal of technical uncertainty — turning reaction to an incident into proactive anticipation of risk.
So, is it possible to anticipate this kind of disaster?
The key lies not in monitoring the three pillars in isolation, but in the ability to centralise and correlate the data flows through intelligent information management. This approach also lets the system be parametrised to the geological and operational characteristics of each area, adapting safety criteria to the particular conditions of each piece of infrastructure. In this way, the precision and certainty of early warnings increase for any anomaly that escapes the asset's stability thresholds.
To reach this level of precision, Remote Tailings — YNSAT's platform — goes a step beyond conventional data flows and standardised methodologies. The core of the system acts as a centralised intelligence engine that synergistically unifies spatial data sources with the mine's existing on-site instrumentation ecosystem. By jointly processing and interpreting this universe of information with advanced Deep Learning algorithms, the platform not only dissolves the informational shadow zones but acts as the analytical brain of the operation, completely eliminating its infrastructural blind spots.
Surveillance thus stops being a rigid model and becomes a dynamic solution — fully parametrisable and adaptable to the geological, geophysical, climatic and operational particularities of each project. By intelligently configuring control thresholds to each asset's real behaviour and physical context, Remote Tailings marks a true paradigm shift in the sector: the definitive transition from simple reaction to an incident toward proactive, predictive anticipation of risk.
The role of AI at YNSAT: turning history into anticipation and alerts
In today's landscape, the strategic challenge is not collecting data, but interpreting it to act in time. Within YNSAT's ecosystem, Artificial Intelligence gives meaning to the complexity of the data flows, transforming static analysis into intelligent, predictive surveillance. Through advanced machine-learning algorithms, the platform trains models to identify anomalous patterns and risk trends before critical safety thresholds are reached.
This capability completely redefines risk management by activating a shield of automated alerts based on real-time cross-correlation of events: the system does not merely collect data — it autonomously understands that if an acceleration measured by InSAR coincides with a rise in pore pressure, the probability of a catastrophic failure spikes.
Beyond automating real-time alarms, AI acts as a simulation engine capable of anticipating future scenarios and modelling how the ground will evolve — allowing the company to get ahead of events before they happen and make strategic decisions based on predictive certainty.
"The state of the art in technology and AI enables a new generation of monitoring systems, with levels of precision and analytical capability that simply weren't possible before." Víctor Moreno — CEO & Co-Founder, YNSAT
An AI assistant that supports the operator
One of the most disruptive advances built into this architecture is the ability to assist the operator through intelligent virtual agents. This assistant lets users interact with the platform to land technical diagnostics in plain terms, easing their work and their forecasting of risk.
Transparency and governance: technological support for regulatory compliance
Implementing this integral monitoring model goes beyond operational improvement to become a technical response to the demands of international governance. The GISTM (Global Industry Standard on Tailings Management) has redefined the sector's safety framework, requiring levels of transparency and traceability that call for structured, consistent and auditable data management across the facility's entire lifecycle.
In this context, Remote Tailings is designed to support that process: it centralises monitoring information and makes it possible to generate audits and reports based on verifiable technical evidence, giving operators a solid basis on which to underpin their safety protocols and reduce the risks that come from scattered or inconsistent data.
Scaling the model: monitoring strategic assets
While the control of tailings storage facilities is a critical challenge in its own right, YNSAT's architecture is inherently scalable to the entire mining operation.
One of its most important applications is deployed in open-pit operations, where monitoring the structural stability of the walls is paramount to guaranteeing operational continuity and personnel safety against precursor movements. This monitoring capability extends with equal effectiveness to underground mining — detecting surface deformation from subsidence or collapse by cross-checking internal sensors with satellite vision — as well as to the protection of critical infrastructure such as pipelines. In short, it is a solution designed to unify global instrumentation, safeguarding both the integrity of the assets and the company's economic profitability.