Generally, there are two important types of microseismic (MS) signals caused by mining and blasting activities at coal mines. The waveform characteristics of MS signals using FFT, STA/LTA method, and envelope analysis were studied to distinguish these two types of MS signals. The main results are as follows: the dominant frequency and duration of two types of signals are significantly different. The following peak envelope curves of two types of MS signals fit a power function. The power exponent was obtained to describe the attenuated speed of the MS signals. The attenuation of the coal mining MS signals is slower and more fluctuant than that of the blasting signal. Waveform characteristics consisting of the dominant frequency, duration, and attenuation coefficient were extracted as the discriminating parameters. The discriminating performance of these parameters was compared and discussed. Based on the waveform characteristics, a discriminant model for coal mining MS and blasting signals was established by using Fisher linear discriminant method and its performance was checked. The results show that the accuracy of the discriminant model is more than 85%, which can meet the requirements of MS monitoring at coal mines.

This paper presents regional trends in vegetation greenness in Inner Mongolia Autonomous Region, semiarid areas of China. We used NOAA GIMMS NDVI data from 1982 to 2006 to explore the relationship between vegetation greenness change and climatic and anthropogenic factors based on the analyses of meteorological data, high spatial resolution remotely sensed data and local economic statistical data. This research indicates the long-term change of increasing vegetation greenness from 1982 to 2006 in a large portion of the Inner Mongolia Autonomous Region. 17% of the study area experienced a significant increase change of vegetation greenness over the study period at the 0.05 level. The increasing greenness showed no positive link with precipitation in the study area. The increase of both the cropland area and the millet yield is responsible for the significant increase of vegetation greenness. Where the integrated NDVI showed significant increase trend, most area is crop land. For the entire study area, the cropland area increased over 50% during the study period and the millet yield of cropland per unit showed a strong increase with the regression model significant at the 0.05 level.

Electromagnetic radiation, as a non-contact and real-time monitoring technology, has been widely used in coal rock fracture and rock burst disaster monitoring during coal mining. Electromagnetic radiation signals can reflect the loading state and fracture degree of coal rocks. However, when electromagnetic radiation is used to predict rock fracture, the current study mainly focuses on the trend change of signal strength, counts and other statistical indicators. There is a lack of research on rock fracture prediction based on the electromagnetic radiation signal itself. Therefore, experiments on monitoring electromagnetic radiation in uniaxial compression of rocks were carried out. Differences in the features of ordinary signals (corresponding to the 0–80% σ stage) and precursory signals (corresponding to the 80% σ—σ stage) of electromagnetic radiation during the loading process of rocks were analyzed. The results showed that different signal features distinguished the two types of electromagnetic radiation signals to different degrees. Automatic ranking of feature importance can be achieved by the random forest method. Adaptive boosting method was used to establish intelligent recognition models for two types of electromagnetic radiation signals. The model recognition accuracy was also analyzed when the feature sets were different. It was finally determined that the highest recognition accuracy (92.25%) of the intelligent recognition model for the two types of signals was achieved when the combination of four features was used as the feature set. The research results provide new ideas and methods for the rock fracture prediction using electromagnetic radiation.