High-sensitivity ocean temperature sensor using a reflective optical microfiber coupler and machine learning methods

This paper proposes a novel seawater temperature sensor, to the best of our knowledge, that utilizes an optical microfiber coupler combined with a reflective silver mirror (OMCM). The sensor's sensitivity and durability are enhanced by encapsulating it in polydimethylsiloxane (PDMS). Additionally, a specially designed metal casing prevents the OMCM from responding to pressure, thus avoiding the challenge of multi-parameter demodulation and increasing its adaptability to harsh environments. The paper analyzes the advantages of the new sensor structure and evaluates its performance in terms of temperature sensitivity and compressive strength through experiments. Finally, the paper employs machine learning demodulation methods. Compared with traditional demodulation methods, the particle swarm optimization support vector regression (PSO-SVR) algorithm demonstrates a substantial reduction in the demodulation error. Specifically, the mean absolute percentage error (MAPE) relative to the full scale drops from 2.16% to 0.157%. This paper provides an effective solution for high-precision monitoring of the ocean environmental temperature.