Overcoming the Longstanding Challenge of Long-Range Raman Distributed Optical Fiber Sensing Through Golay-Encoded Autocorrelation and Waveform Reconstruction

The practical implementation of Raman-based distributed optical fiber sensing has been fundamentally constrained by the inherent low signal-to-noise ratio, particularly for operational ranges exceeding 30 km. We present a groundbreaking paradigm integrating Golay-encoded autocorrelation processing with advanced Raman scattering waveform reconstruction to transcend this physical limitation. A newly developed preprocessing framework simultaneously optimizes complementary sequence correlation and effectively mitigates disturbances of transient effects. Our experimental demonstration achieves record-breaking performance metrics: 70 km operational range with 1.58 m spatial resolution, while maintaining 0.88 °C measurement accuracy and 5.44 °C temperature resolution. This methodology establishes new technical benchmarks by successfully resolving the persistent trade-off between sensing distance and spatial resolution in distributed fiber-optic sensing systems, opening new possibilities for long-range infrastructure monitoring and environmental sensing applications.