The magnetic field is a vital physical quantity in nature that is closely related to human production life. Magnetic field sensors (namely magnetometers) have significant application value in scientific research, engineering applications, industrial productions, and so forth. Accompanied by the continuous development of magnetic materials and fiber-sensing technology, fiber sensors based on the Magneto-Refractive Effect (MRE) not only take advantage in compact structure, superior performance, and strong environmental adaptability but also further meet the requirement of the quasi-distributed/distributed magnetic field sensing; they manifest potential and great application value in space detection, marine environmental monitoring, etc. Consequently, the present and prevalent Magneto-Refractive Magnetic Field Fiber Sensors (MR-MFSs) are briefly summarized by this paper, proceeding from the perspective of physicochemical properties; design methods, basic performance and properties are introduced systematically as well. Furthermore, this paper also summarizes key fabrication techniques and future development trends of MR-MFSs, expecting to provide ideas and technical references for staff engaging in relevant research.

In the process of ocean exploration, highly accurate and sensitive measurements of seawater temperature and pressure significantly impact the study of seawater’s physical, chemical, and biological processes. In this paper, three different package structures, V-shape, square-shape, and semicircle-shape, are designed and fabricated, and an optical microfiber coupler combined Sagnac loop (OMCSL) is encapsulated in these structures with polydimethylsiloxane (PDMS). Then, the temperature and pressure response characteristics of the OMCSL, under different package structures, are analyzed by simulation and experiment. The experimental results show that structural change hardly affects temperature sensitivity, and square-shape has the highest pressure sensitivity. In addition, with an input error of 1% F.S., temperature and pressure errors were calculated, which shows that a semicircle-shape structure can increase the angle between lines in the sensitivity matrix method (SMM), and reduce the effect of the input error, thus optimizing the ill-conditioned matrix. Finally, this paper shows that using the machine learning method (MLM) effectively improves demodulation accuracy. In conclusion, this paper proposes to optimize the ill-conditioned matrix problem in SMM demodulation by improving sensitivity with structural optimization, which essentially explains the cause of the large errors for multiparameter cross-sensitivity. In addition, this paper proposes to use the MLM to solve the problem of large errors in the SMM, which provides a new method to solve the problem of the ill-conditioned matrix in SMM demodulation. These have practical implications for engineering an all-optical sensor that can be used for detection in the ocean environment.