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In view of the traditional mine transient electromagnetic instrument is easy affected by the underground human interference factors, a kind of mine in-hole transient electromagnetic instrument is designed. The transmitting part of the instrument is mainly composed of bridge circuit and hidden circuit. In order to shorten the turn-off time, the voltage regulator diode between the source and gate and the absorption and blank-elimination circuit on both sides of the transmitting coil constitute a fast turn-off circuit system, which quickly release the energy in the transmitting coil and weaken the inductance effect. The receiver part has the ability of full space signal acquisition, and the dynamic range of the device meets the needs by self-adjusting the gain multiple of the secondary field signal. The test show that the instrument have the ability to probe drilling radial water cut abnormal body, because the device is located in the hole, close to target body, easy to obtain high signal-to-noise ratio of the secondary field signal, and avoid the influence of human interference factors of roadway.

To solve the water hazard problem of Jurassic coal seam roof sandstone in Yinxing No.1 Coal Mine of Ningxia Province, the direct relationship between the sedimentary environment of Jurassic coal seam roof sandstone and water abundance was analyzed and the water abundance of sandstone aquifer was evaluated on the basis of combing the geological and hydro-geological conditions of the mine. The mechanism and characteristics of water disaster in Jurassic coal seam roof sandstone were analyzed based on the study of the mechanical properties, mineral composition, water stability characteristics and water-conducting fracture development law of coal seam roof strata. In this paper, we propose the idea of comprehensively applying various measures of “exploration, drainage, discharge, interception and monitoring”, and focusing on the basic exploration work, using hydrophobic and depressurizing technology to control water damage. The water damage control effect of 110301 working face show that it was feasible to control the sandstone water damage on the roof of Jurassic coal seam through hydrophobic depressurization.

The technology of microseismic monitoring, the first step of which is event recognition, provides an effective method for giving early warning of dynamic disasters in coal mines, especially mining water hazards, while signals with a low signal-to-noise ratio (SNR) usually cannot be recognized effectively by systematic methods. This paper proposes a wavelet scattering decomposition (WSD) transform and support vector machine (SVM) algorithm for discriminating events of microseismic signals with a low SNR. Firstly, a method of signal feature extraction based on WSD transform is presented by studying the matrix constructed by the scattering decomposition coefficients. Secondly, the microseismic events intelligent recognition model built by operating a WSD coefficients calculation for the acquired raw vibration signals, shaping a feature vector matrix of them, is outlined. Finally, a comparative analysis of the microseismic events and noise signals in the experiment verifies that the discriminative features of the two can accurately be expressed by using wavelet scattering coefficients. The artificial intelligence recognition model developed based on both SVM and WSD not only provides a fast method with a high classification accuracy rate, but it also fits the online feature extraction of microseismic monitoring signals. We establish that the proposed method improves the efficiency and the accuracy of microseismic signals processing for monitoring rock instability and seismicity. [ABSTRACT FROM AUTHOR]

The technology of microseismic monitoring, the first step of which is event recognition, provides an effective method for giving early warning of dynamic disasters in coal mines, especially mining water hazards, while signals with a low signal-to-noise ratio (SNR) usually cannot be recognized effectively by systematic methods. This paper proposes a wavelet scattering decomposition (WSD) transform and support vector machine (SVM) algorithm for discriminating events of microseismic signals with a low SNR. Firstly, a method of signal feature extraction based on WSD transform is presented by studying the matrix constructed by the scattering decomposition coefficients. Secondly, the microseismic events intelligent recognition model built by operating a WSD coefficients calculation for the acquired raw vibration signals, shaping a feature vector matrix of them, is outlined. Finally, a comparative analysis of the microseismic events and noise signals in the experiment verifies that the discriminative features of the two can accurately be expressed by using wavelet scattering coefficients. The artificial intelligence recognition model developed based on both SVM and WSD not only provides a fast method with a high classification accuracy rate, but it also fits the online feature extraction of microseismic monitoring signals. We establish that the proposed method improves the efficiency and the accuracy of microseismic signals processing for monitoring rock instability and seismicity.