278 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). The scope includes a structured workflow built in collaboration among TSF owner, EoR, and DFOS Solution provider to design and optimize a DFOS platform based on the site-specific conditions, based on TSF types (downstream, upstream, and centerline) and stages from operational to closure‑transition and legacy states. The approach is non‑prescriptive regarding specific equipment and focuses instead on sensing physics, data workflows, governance integration, and field‑deployable patterns that can be implemented, scaled, and audited, Figure 1.
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