Dual-stage deep learning for sangac optical fiber sensing multi-event detection and localization

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This paper introduces a dual-stage deep learning structure for multi-event detection and localization (DDMDL) for a loop-based Sagnac interferometer optical fiber sensing system to overcome the difficulties of detecting and localizing multiple events. The DDMDL approach comprises of a combination of (i) a detection stage made of a multi-class classification model that quantifies the events, and (ii) a localization stage made of separate deep-learning regression models tasked with precisely localizing the events. By addressing the multi-event localization as a combined multi-class classification and multi-regression problem, our approach enables more accurate prediction of event locations. We evaluate our DDMDL model in a broad range of test scenarios across three signal-to-noise ratio (SNR) levels: low, medium, and high, all outside the model’s training dataset. The model had high detection (i.e., classification) accuracy in simulation, with rates of 90%–99% for single-event scenarios, 94%–98% for two-event scenarios, and 82%–99% for three-event scenarios at the three SNR levels. The precision of localization (i.e., regression) was evaluated by Mean Absolute Error (MAE). The results showed that single-event scenarios could be localized within a range of 35–50 m, two-event scenarios within a range of 82–106 m, and three-event scenarios within a range of 131–151 m. These findings were consistent across different SNR levels, ranging from low to high, over a 50 km sensing fiber length. Experimental validations confirmed the DDMDL model’s practical applicability, with detection accuracy of 80% in single-event scenarios and localization accuracy with MAEs ranging between 32 and 65 m. For two-event scenarios, the model achieved an 87% success rate, with MAEs ranging between 122 m and 204 m, emphasizing the model’s potential effectiveness in different applications.