1181 1. CONTEXT AND PROBLEM STATEMENT Tailings Storage Facilities (TSFs) are among the most safety‑critical and ESG‑exposed facilities in mining. They must perform reliably over long operational lives and often well beyond closure, under evolving climatic, seismic, and operational conditions. High‑consequence failures have reinforced that TSFs should be managed as complex systems where geotechnical, hydrological, operational, governance, and human factors interact. The Global Industry Standard on Tailings Management (GISTM) formalises this expectation by requiring risk‑informed design, operation, monitoring, emergency preparedness, and public disclosure across the lifecycle. A persistent gap remains between governance intent and monitoring reality. TSF monitoring commonly relies on point instruments (e.g., piezometers, inclinometers, prisms) and inspections that provide high-value measurements at discrete locations yet leave large volumes unmeasured and can delay localization of weak, distributed changes such as evolving seepage pathways or progressive deformation. Satellite InSAR adds repeatable, facility-wide surveillance of surface displacement; however, it primarily measures surface response, is constrained by line-of-sight, and can be limited by low temporal resolution and uncertainty without validation through additional sub-surface data. These constraints can reduce confidence in early detection, increase residual safety risk and weaken timely operational decisions, often leading to conservative controls, additional investigations, and avoidable cost escalation. WMC 2026 calls for practical contributions that build Trust, lead Transformation, and advance Technology to deliver the minerals society urgently needs, faster, smarter, and more responsibly. In this context, TSF monitoring is not only a technical matter: it is a trust‑critical capability influencing permit timelines, social license, capital availability, and operational continuity. Improving monitoring coverage and interpretability can support responsible schedule compression by reducing uncertainty, preventing rework, and enabling earlier, targeted interventions. Beyond TSFs, DFOS is increasingly adopted for mining geotechnical monitoring (e.g., rock mass response) and other sub‑surface applications, providing a growing body of field learnings on installation, commissioning, data QA/QC, and ‘data‑to‑decision’ workflows. This transfer of practice is important for TSFs: it demonstrates that DFOS can be implemented as a repeatable monitoring platform, especially when paired with fit‑for‑purpose analytics and visualization that translate distributed measurements into clear operational states for decision makers. 2. OBJECTIVES AND SCOPE This paper proposes a practical monitoring architecture that treats the TSF as a living structure with an embedded “nervous system”. The nervous system is implemented using Distributed Fiber Optic Sensing (DFOS) modules including Distributed Temperature Sensing (DTS), Distributed Acoustic Sensing (DAS), and Distributed Strain Sensing (DSS) to provide dense, continuous measurements along tens of kilometers of fibre installed in and around the TSF. The approach is designed to complement existing instrumentation and strengthen the link between sensing, decision‑making, and governance. The objectives of this paper are to: ● Define a DFOS‑enabled “nervous system” architecture that complements existing instrumentation and aligns with GISTM Describe deployment methods for both greenfield and brownfield TSFs, with a focus on scalability during raises and expansion. ● Map DFOS signal classes (thermal, strain, dynamic/acoustic) to common TSF risk mechanisms and operational response workflows (e.g., Trigger Action Response Plans, TARPs). ● Identify practical outcomes for industry (e.g., risk reduction, decision acceleration, and transparency).
RkJQdWJsaXNoZXIy MTM0Mzk2