This paper proposes a methane sensor based on localized surface plasmon resonance (LSPR) of a hexagonal periodic gold nanoring array. The effects of structural parameters on the extinction spectrum and refractive index (RI) sensitivity are analyzed to obtain optimal parameters. In particular, the RI sensitivity can reach 550.08 nm/RIU through improvement of the sensor structure, which is an increase of 17.4% over the original value. After coating a methane-sensitive membrane on the inner and outer surfaces of the gold rings, the methane concentration can be accurately measured with a gas sensitivity of −1.02 nm/%. The proposed method is also applicable to quantitative analyses of components concentration and qualitative analyses of gas composition.

To provide an effective risk assessment of water inrush for coal mine safety production, a BP neural network prediction method for water inrush based on principal component analysis and deep confidence network optimization was proposed. Because deep belief network (DBN) is disadvantaged by a long training time when establishing a high-dimensional data classification model, the principal component analysis (PCA) method is used to reduce the dimensionality of many factors affecting the water inrush of the coal seam floor, thus reducing the number of variables of the research object, redundancy and the difficulty of feature extraction and shortening the training time of the model. Then, a DBN network was used to extract secondary features from the processed nonlinear data, and a more abstract high-level representation was formed by combining low-level features to find the expression of the nonlinear relationship between the characteristics of water in bursts. Finally, a prediction model was established to predict the water inrush in coal mines. The superiority of this method was verified by comparing the prediction of the actual working face with the actual situation in typical mining areas of North China. The prediction accuracy of coal mine water inrush obtained by this algorithm is 94%, while the prediction accuracy of traditional BP algorithm is 70%, and the prediction accuracy of SVM algorithm is 88%.

A high-sensitive and transverse-stress compensated methane sensor based on a photonic crystal fiber long-period grating (PCF-LPG) is proposed. The outermost layer of the PCF consists of six large sideholes, five of which are coated with methane-sensitive compound film to achieve methane measurement. Such side-hole structure is helpful for gas sensitive reaction, but not conducive to avoiding external stress interference. Therefore, the last large hole is plated with silver layer to eliminate the cross-sensitivity effect through adding a Surface Plasmon Resonance (SPR) sensing channel with the consideration of photo-elastic effect and material deformation. Results show that the methane gas sensitivity can reach up to 6.39nm/% with the transverse-stress compensation. The sensor is very simple and effective, which provides a new method of gas measurement combined with different actual conditions.

Dual-parameter measurements of refractive index and methane concentration based on electromagnetic Fano resonance are proposed. Two independent Fano resonances can be produced through electric dipole and toroidal dipole resonance in an all-dielectric metasurface separately. The linear relationship between the spectral peak-shifts and the parameters to be measured will be obtained directly. The refractive index (RI) sensitivity and gas sensitivity are 1305.6 nm/refractive index unit (RIU), −0.295 nm/% for one resonance peak (dip1), and 456.6 nm/RIU, −0.61 nm/% for another resonance peak (dip2). Such a metasurface has simpler structure and higher sensitivity, which is beneficial for environmental gas monitoring or multi-parameter measurements.