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Accurate picking of the first arrival of microseisms is the prerequisite for the estimation of source location. The traditional manual picking method is inefficient and time-consuming. The short time average long time average (STA/LTA) method, commonly used in automatic picking, has low picking accuracy for low signal-to-noise ratio signals. To address the above problems, a random forest-based automatic picking method of microseismic first arrival is proposed. Firstly, this study extracts the amplitude, energy and amplitude ratio of adjacent moments of microseismic data as features and mark each sample with feature categories. Secondly, a random forest model is constructed to identify microseismic first arrivals. Thirdly, the random forest model is used to calculate the probability of each test sample belonging to a certain category, and the first data sampling point with a probability of no less than 0.5 is defined as the microseismic first arrivals sampling point. In this experiment, microseismic monitoring data in deep boreholes of coal mine roadways is used. The results show that as the number of decision trees reaching 137 and the maximum depth reaching 6 in the random forest algorithm, the accuracy of the method for classifying microseismic data samples could reach 98.5%, and the average picking error for first arrivals of microseismic is 23.1 ms. Therefore, this method is better than the method of STA/LTA in terms of picking accuracy.

For disadvantages of traditional electronic sensors in mine safety monitoring system such as low precision, poor reliability, a large amount of maintenance, monitoring dead-zone and so on, it was proposed that using optical fiber sensors and wireless sensor to build mine monitoring system of human, machine and environment with multi-dimension, multi-parameter and large capacity. Advantages of optical fiber sensor applications in the mine safety monitoring system were introduced. The operational principle of optical fiber sensors developed for mine applications were described, which included laser spectral analysis based gas sensors, fiber Bragg grating based physical sensors and distributed optical fiber temperature sensor. Meanwhile, typical applications of optical fiber sensors in mine safety Internet of things were introduced including laser methane sensor in coal mine safety monitoring system, optical fiber acceleration sensor in micro-seismic monitoring system and distributed optical fiber temperature sensor in goaf fire monitoring and pre-alarming system.

Coal quality analysis is important to promote the rational and sustainable utilization of coal resources. The traditional method of selecting characteristic lines from the coal sample spectrum is time-consuming. This work aims to use laser-induced breakdown spectroscopy (LIBS) to reconstruct principal components (PCs) by utilizing the correlation between full spectrum wave point interpretation variance and calibration values. More components related to the features of interest are obtained and the regression metrics are maximized. Subsequently, quantitative models were established using ordinary least squares (OLS) for ash content, volatile matter, and calorific value. The coefficient of determination (R2) of the training set and test set of ash content and volatile matter was improved from 0.8125 and 0.8222, 0.6502 and 0.6483 to 0.9701 and 0.9818, 0.9458 and 0.9429, respectively. The root-mean-square error of cross-validation (RMSECV) and the root-mean-square error of prediction (RMSEP) were reduced from 1.7843% and 1.7696%, 1.1797% and 1.4301% to 0.7153% and 0.7037%, 0.5678% and 0.6628%, respectively. The training set and test set of calorific value R2 were also improved from 0.7086 and 0.7157 to 0.9857 and 0.9811. RMSECV and RMSEP were reduced from 0.9525 MJ kg−1 and 1.0178 MJ kg−1 to 0.1518 MJ kg−1 and 0.1613 MJ kg−1, respectively. To evaluate the interpretability of PC recombination, the cumulative correlations between ash content, volatile matter, calorific value and the PCs of each wave point in the LIBS spectrum were analyzed, and then the characteristic spectral line locations of the elements that contributed the most to the calibration model were extracted. The results of the analysis of metal and non-metal elements in coal samples show that PC recombination chooses more significant characteristics rather than noise as independent variables. The work demonstrates the feasibility and effectiveness of employing LIBS-based quantitative analysis for rapid coal quality detection in the coal industry. [ABSTRACT FROM AUTHOR]