Coal Thickness Prediction Method Based on VMD and LSTM

The change in coal seam thickness has an important influence on coal mine safety and efficient mining. It is very important to predict coal thickness accurately. However, the accuracy of borehole interpolation and BP neural network is not high. Variational mode decomposition (VMD) has strong denoising ability, and the long short-term memory neural network (LSTM) is especially suitable for the prediction of complex sequences. This paper presents a method of coal thickness prediction using VMD and LSTM. Firstly, empirical mode decomposition (EMD) and VMD methods are used to denoise simple signals, and the denoising effect of the VMD method is verified. Then, the wedge-shaped coal thickness model is constructed, and the seismic forward modeling and analysis are carried out. The results show that the coal thickness prediction based on seismic attributes is feasible. On the basis of VMD denoising of the original 3D seismic data, VMD-LSTM is used to predict coal thickness and compared with the prediction results of the traditional BP neural network. The coal thickness prediction method proposed in this paper has high accuracy and basically coincides with the coal seam information exposed by existing boreholes. The minimum absolute error of the predicted coal thickness is only 0.08 m, and the maximum absolute error is 0.48 m. This indicates that VMD-LSTM has high accuracy in predicting coal thickness. The proposed coal thickness prediction method can be used as a new method for coal thickness prediction.

Is There a Difference in Brain Functional Connectivity between Chinese Coal Mine Workers Who Have Engaged in Unsafe Behavior and Those Who Have Not?

(1) Background: As a world-recognized high-risk occupation, coal mine workers need various cognitive functions to process the surrounding information to cope with a large number of perceived hazards or risks. Therefore, it is necessary to explore the connection between coal mine workers’ neural activity and unsafe behavior from the perspective of cognitive neuroscience. This study explored the functional brain connectivity of coal mine workers who have engaged in unsafe behaviors (EUB) and those who have not (NUB). (2) Methods: Based on functional near-infrared spectroscopy (fNIRS), a total of 106 workers from the Hongliulin coal mine of Shaanxi North Mining Group, one of the largest modern coal mines in China, completed the test. Pearson’s Correlation Coefficient (COR) analysis, brain network analysis, and two-sample t-test were used to investigate the difference in brain functional connectivity between the two groups. (3) Results: The results showed that there were significant differences in functional brain connectivity between EUB and NUB among the frontopolar area (p = 0.002325), orbitofrontal area (p = 0.02102), and pars triangularis Broca’s area (p = 0.02888). Small-world properties existed in the brain networks of both groups, and the dorsolateral prefrontal cortex had significant differences in clustering coefficient (p = 0.0004), nodal efficiency (p = 0.0384), and nodal local efficiency (p = 0.0004). (4) Conclusions: This study is the first application of fNIRS to the field of coal mine safety. The fNIRS brain functional connectivity analysis is a feasible method to investigate the neuropsychological mechanism of unsafe behavior in coal mine workers in the view of brain science.

Implementation of Smart Technologies in the Mine

The basic methods and principles of mine safety systems have been considered in the paper. The algorithm of one possible smart device (smart helmet) is depicted. This algorithm describes the basic principles of this device. The device allows to find danger in the environment where the miner works, as well as monitors the condition of the miner. It can also quickly analyze this information and report the danger when it is detected. The system has been developed and programmed including basic modules for implementing this algorithm. The results of the comparative analysis of the new system showed an increase in the level of safety by 45% compared to other systems.

A calculation methodology of fault relative displacement used to study the mechanical characteristic of fault slip

Fault slip caused by mining disturbance is a crucial issue that can pose considerable threats to the mine safety. This paper proposes a point-by-point integration calculated methodology of fault relative slip and studies fault instability behavior induced by coal seam mining. A physical model with the existence of a fault and an extra-thick rock stratum is constructed to simulate the fault movement and calculate relative slip using the methodology. The results indicate that the fault relative slip can be regarded as a dynamic evolution process from local slip to global slip on the fault surface. The movement of surrounding rock masses near the fault experiences three stages, including along vertical downward, parallel to the fault and then approximately perpendicular to the fault. There will be an undamaged zone in the extra-thick rock strata when the mining face is near the fault structure. The collapse and instability of this undamaged zone could induce a violent fault relative slip. In addition, the influence of dip angles on the fault relative slip is also discussed. A formula for risk of fault relative slip is further proposed by fitting the relative displacement curves with different fault dip angles.

Multi-functional coal mine safety system: visualisation of events (mining processes) form the miner's workplace

The paper reviews the use of mobile video monitoring equipment in coal mines. The most common option is the use of stationary video cameras with real-time video streaming to the mine dispatcher's control monitor via cables. Despite all the benefits of the information obtained, this method has certain limitations due to the specific features of the mine atmosphere, i.e. high humidity and dust levels, as well as the impossibility to organize video monitoring over the entire length of the mine workings. Therefore, mobile video monitoring equipment, both portable and vehicle-based, is efficient supplement to the stationary video cameras. The portable devices include smart phones and the battery-powered head lights with an integrated video camera, which have recently become very popular. In both cases, an important consideration, in addition to the actual video capturing, is the issue of transmitting video data to the top level, i.e. to the mine dispatcher's control panel. The following options are possible: connection to the mine wireless network hotspots via radio channel, reading the information in the lamp rooms when leaving the mine and real-time broadcasting from the mine to the top level. The assumption is made that in order to implement the fastest (and the most efficient) way that works without delays between capturing and transmitting of video data to the daylight surface, such as the latter of the options above, a communications infrastructure based on wireless and cable networks needs to be deployed in the mine workings. The required infrastructure is present in a number of systems designed to locate miners inside the mine workings as part of a multifunctional security system, which enables continuous radio communication of individual devices with infrastructure nodes and, therefore, real-time video data transmission.

Research on Management and Control of Miners’ Unsafe Behavior Based on Gray Theory

A large number of accidents and scientific researches show that miners’ unsafe behavior affects coal mine safety production seriously. In order to effectively reduce the incidence of miners’ unsafe behavior, to improve their safety level, and reduce accidents caused by it, this paper used gray relational analysis method to analyze the miners’ unsafe behavior of W mine and quantitatively calculated the risk value of miners’ unsafe behavior. The results showed that the risk value of unsafe behavior in violation of labor discipline was 0.4358, which was much higher than that of other miners’ unsafe behaviors. Therefore, unsafe behavior in violation of labor discipline was determined as the key point of control in the next stage. Then, GM (1, 1) method was used to establish a predicted model for unsafe behavior, to predict the number of unsafe behaviors in violating labor discipline in next quarter, and to determine reasonable unsafe behavior control target. This study plays a driving role in controlling unsafe behaviors of miners and improving safe production water of coal mine.

The Stability of Roadway Groups under Rheology Coupling Mining Disturbance

The deep roadway groups play an important role in transportation and ventilation in coal mine production. Therefore, it is very important to comprehensively analyze the coupling effect of rheological deformation and coal mining on the stability of the roadway groups. In this paper, the disturbance effects of different stop-mining lines on roadway groups under long-term rheology were investigated by numerical simulation, and the failure mechanism of roadway groups with large sections and multiple disturbances in a deep well was revealed. The results show that the long working face will lead to the collapse of key strata, and the influence range will spread to the adjacent roadway groups. When the distance between the working face and the stop-mining line is 100 m, the roadway groups cannot be affected by the working face mining, and the reserved width of the coal pillar can be determined to be 100 m, which increases the stability of the roadway’s surrounding rock and maintains the mine safety production. This paper aims to provide a reference for groups design and control under similar conditions.