The microseismic monitoring signals which need to be determined in mines include those caused by both rock bursts and by blasting. The blasting signals must be separated from the microseismic signals in order to extract the information needed for the correct location of the source and for determining the blast mechanism. The use of a convolutional neural network (CNN) is a viable approach to extract these blast characteristic parameters automatically and to achieve the accuracy needed in the signal recognition. The Stockwell Transform (or S-Transform) has excellent two-dimensional time-frequency characteristics and thus to obtain the microseismic signal and blasting vibration signal separately, the microseismic signal has been converted in this work into a two-dimensional image format by use of the S-Transform, following which it is recognized by using the CNN. The sample data given in this paper are used for model training, where the training sample is an image containing three RGB color channels. The training time can be decreased by means of reducing the picture size and thus reducing the number of training steps used. The optimal combination of parameters can then be obtained after continuously updating the training parameters. When the image size is 180 × 140 pixels, it has been shown that the test accuracy can reach 96.15% and that it is feasible to classify separately the blasting signal and the microseismic signal based on using the S-Transform and the CNN model architecture, where the training parameters were designed by synthesizing LeNet-5 and AlexNet.

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.

It is of great significance to analyze the calorific value and carbon dioxide emission level of coal with different degrees of coalification for energy conservation and emission reduction in coal-fired power plants, and elemental carbon and fixed carbon are closely related to the carbon dioxide emission level and calorific value. Due to the complexity of matrix effects and differences in regression tasks, modeling only all coal samples will limit the quantitative performance of laser-induced breakdown spectroscopy (LIBS). In this work, we combine a dual-cycle variable selection mechanism with competitive adaptive reweighted sampling to optimize partial least squares regression (PLSR). According to the difference between coal samples in coal quality characteristics and prediction tasks, the matrix correction of elemental carbon and fixed carbon is realized by qualitative classification and by using generalized spectra (GS), respectively. The coefficient of determination (R²) of the training set and test set of elemental carbon was improved from 0.7178 and 0.7095 to 0.9534 and 0.9515, respectively. The root-mean-square error of cross-validation (RMSECV) and the root-mean-square error of prediction (RMSEP) decreased from 1.1723 wt% and 1.2488 wt% to 0.4504 wt% and 0.4871 wt%, respectively. The R² of the training set and test set of fixed carbon was also improved from 0.8252 and 0.8236 to 0.9814 and 0.9749. The RMSECV and RMSEP decreased from 1.2274 wt% and 1.3548 wt% to 0.4421 wt% and 0.4711 wt%, respectively. According to Chinese national standards and IPCC guidelines, the maximum error in CO₂ emission factors for all coal samples during the accounting period is 1.7581%. Taking into account the level of CO₂ emissions and the net calorific value, the selection of coal with different degrees of coalification can guide the combustion of mixed coal in power plants to achieve high efficiency and clean power generation. This work demonstrates the great potential of in situ LIBS for online applications in coal-fired power plants.

According to the characteristics of underground environment and microseismic signal in coal mine, a kind of optical fiber microseismic accelerometer based on cantilever beam structure is put forward in this paper, and its mechanical model is established, its sensitivity is analyzed, and tested by B&K shaking table test system. The experimental results show that the frequency response range of the sensor is 10Hz-100Hz, which meets the requirements of microseismic test..

A high sensitive micro-seismic fiber Bragg grating (FBG) sensor system based on narrow line width distributed feedback (DFB) laser is realized by using filtering demodulation. Sensitivity of micro-seismic FBG sensor lies on the structure of the sensor shell and the shape of the reflectivity spectrum of FBG. Experiments on the sensor system's frequency character have been done. Results show that this sensor has the smallest acceleration of 1mm/s2. The experiments on coal mine exhibit that the sensor can be used to measure micro-seismic sigmals. So the FBG sensor is feasible to have practical use to measure micro-seismic signals because the sensor has high sensitivity and is easy to be fabricated, it has good low frequency response and the interrogation system is simple, low cost