Location-dependent noise modeling: bridging the gap between simulated and experimental guided wave data in fiber optic pipeline monitoring

Pipelines are critical components of modern infrastructure, transporting resources such as oil, gas, and water securely and efficiently. Yet they face persistent threats—such as corrosion and cracking—that can lead to costly failures. To address these risks, guided wave techniques combined with fiber optic sensing have emerged as powerful tools. By enabling continuous, distributed measurements across long distances, these methods facilitate early detection of subtle defects, reducing maintenance costs and enhancing overall pipeline safety and reliability. However, interpreting the large and complex datasets generated by these systems can be challenging. Manual analysis is time-consuming and may miss subtle trends, prompting increased use of machine learning (ML) and artificial intelligence. While previous work has explored some of these aspects, our study is among the first to integrate fiber optic sensing, guided wave detection, and ML-driven domain adaptation into a unified framework for pipeline health monitoring. A key difficulty lies in bridging the gap between simulated and real-world data. Although simulations are easier to generate, they often lack the complexity and noise characteristics of actual measurements. Domain adaptation methods show promise, yet pipelines introduce unique obstacles, including varying wave propagation conditions, sensor placement differences, and limited experimental datasets. To tackle these challenges, we focus on modeling noise patterns that occur along the pipeline. Three primary noise factors—time-axial misalignment, coherent signal interference, and background fluctuations—shape the measured signals. We develop a position-specific noise model that learns from a single healthy pipeline, capturing how flexural waves evolve from sensor to sensor. By applying these learned noise characteristics to simulations, we create synthetic data that closely resemble real measurements. This approach not only helps overcome the scarcity of experimental datasets but also improves the robustness of ML models, enabling them to recognize and classify defects more accurately in varied pipeline scenarios. In doing so, we move beyond piecemeal solutions and present a comprehensive strategy that brings together fiber optics, guided waves, and ML-based domain adaptation. Our work sets the stage for more accurate, reliable, and data-efficient pipeline integrity assessments, ultimately improving infrastructure safety and reducing the need for extensive experimental data collection.