The advent of fiber optic technology in geophysics exploration has grown in its use in the exploration, production, and monitoring of subsurface environments, revolutionizing the way data are gathered and interpreted critically to speed up decision-making and reduce expense and time. Distributed Acoustic Sensing (DAS) has been increasingly utilized to build relationships in complex geophysics environments by utilizing continuous measurement along fiber optic cables with high spatial resolution and a frequency response of up to 10 KHz. DAS, as fiber optic technology examining backscattered light from a laser emitted inside the fiber and measuring strain changes, enables the performance of subsurface imaging in terms of real-time monitoring for Vertical Seismic Profiling (VSP), reservoir monitoring, and microseismic event detection. This review examines the most widely used fiber optic cables employed for DAS acquisition, namely Single-Mode Fiber (SMF) and Multi-Mode Fiber (MMF), with the different deployments and scopes of data used in geophysics exploration. Over the years, SMF has emerged as a preferred type of fiber optic cable utilized for DAS acquisition and, in most applications examined in this review, outperformed MMF. On the other side, MMF has proven to be preferable when used to measure distributed temperature. Finally, the fiber optic cable deployment technique and acquisition parameters constitute a pivotal preliminary step in DAS data preprocessing, offering a pathway to improve imaging resolution based on DAS measurement as a future scope of work.

Fiber optic sensing (FOS) technologies have emerged as transformative platforms for structural health monitoring (SHM) of critical infrastructure operating in harsh environments. Unlike conventional electrical sensors, fiber optic sensors offer intrinsic immunity to electromagnetic interference, multiplexing capability, distributed sensing over tens of kilometres, and small footprint suitable for structural embedding. However, the transition from laboratory validation to long-term field deployment in extreme conditions including thermal extremes, high radiation fields, aggressive chemical environments, and severe mechanical loading remains impeded by a durability-reliability gap that this review systematically addresses. This paper presents the first critical synthesis integrating three foundational pillars which are the physics of environmental degradation mechanisms affecting silica and specialty optical fibers; signal robustness engineering encompassing temperature-strain decoupling, drift compensation, machine learning-enhanced denoising, and fault-tolerant network architectures; and long-term reliability science including accelerated life testing protocols, physics-of-failure modelling, and field validation case studies spanning nuclear reactors, offshore wind turbines, aerospace platforms, and extreme-climate civil infrastructure. A comprehensive gap analysis of existing standards (Telcordia GR-326, IEC 60793, MIL-STD) reveals critical deficiencies in SHM-specific qualification frameworks, and a roadmap toward standardized test protocols and regulatory acceptance pathways is proposed. Emerging frontiers including sapphire fibers, carbon nanotube coatings, physics-informed neural networks, and autonomous self-healing sensor networks are critically evaluated. The review concludes with actionable recommendations for researchers, practitioners, standards bodies, and funding agencies to accelerate the maturation and widespread deployment of reliable FOS-SHM systems in the world’s most demanding operational environments.

Distributed Acoustic Sensing (DAS) has revolutionized ambient noise interferometry, yet the reliability of retrieved empirical Green’s functions (EGFs) remains highly sensitive to non-diffuse noise fields and anthropogenic transients. In complex or noisy environments, global metrics frequently misclassify signal convergence due to out-of-band environmental noise, scattered coda, and non-stationary directional transients. Using both 30 min and 4 h passive recordings, this study presents a physics-guided quality control framework for evaluating interferometric diagnostics. Specifically, this study employs the signal-to-trailing noise ratio (STRN), signal-to-precursory noise ratio (SPNR), and spectral signal-to-noise ratio (SSNR) within the surface-wave arrival window $t=x/v$. Global phase metrics remain heavily suppressed $(\overline{x}\approx 0.05)$ regardless of stacking duration, whereas the surface-windowed SPNR exhibits an extraordinary statistical shift (p < 0.001), reaching 0.870 ± 0.106 at 30 min and 0.967 ± 0.034 at 4 h. We implement one-to-one correspondence between surface-windowed indicator values and fundamental-mode Rayleigh wave dispersion sharpness. In severely noise-contaminated segments, unwindowed global metrics yield distorted dispersion ridges with severe energy leakage, but the surface-wave window results in an increase in the SPNR above 0.70, fully reconstructing continuous dispersion trajectories (250–500 m/s). Grounded in these results, we formalize a standardized four-step quality control workflow (from velocity windowing to metric calculation, automation, and data output) and outline tailored adaptation guidelines for urban, mountainous, and industrial DAS deployments. This framework provides an automated, physically sound protocol that eliminates manual selection, optimizes computational efficiency, and ensures reliable dispersion extraction for passive DAS imaging.