The Hydrogeological Feature and Geological Hazard Prevention of M Coal Mine in Xinjiang Fukang

2013 ◽  
Vol 405-408 ◽  
pp. 562-565
Author(s):  
Chun Hui Yao ◽  
Qiu Hui Yao
Keyword(s):  
Coal Mine ◽  
Junggar Basin ◽  
Geological Structure ◽  
Geological Hazard ◽  
Geological Hazards ◽  
Hilly Terrain ◽  
Mine Area ◽  
Brittle Rocks ◽  
Mountain Front ◽  
Medium Type

M coal mine is located in the hilly terrain of mountain front in the southern margin of Junggar Basin in Fukang. The geological structure belongs to a medium type in the mine area where there are surface faults (two larger faults) and structural developments. The stratigraphic dips of south limb of Fukang syncline and southern Fukang anticline are large while that near F5 fault of anticline axis are larger and even upright. Brittle rocks develop fractures. In consideration of meteorology, earthquakes and other factors, mining may lead to such geological hazards as eboulement and surface subsidence, which should be highlighted.

Impacts of Coal Mining on Groundwater Environment in Northwestern Coal Mine Area

2013 ◽  
Vol 448-453 ◽  
pp. 823-829
Author(s):  
Hao Wang
Keyword(s):  
Coal Mining ◽  
Coal Mine ◽  
Water Cycle ◽  
Numerical Models ◽  
Pumping Test ◽  
Field Investigation ◽  
Groundwater Pumping ◽  
Mine Area ◽  
Groundwater Environment ◽  
Groundwater Flow Field

By conducting field investigation and tests, such as groundwater pumping test and rock mechanics test, and building numerical models to simulate damage of coal mining to aquifers, it was proved that coal mining in some coal mine area caused impacts to groundwater environment, including impact on water cycle, the structure of aquifers, and groundwater flow field, as a result of which some water supply sources in coal mine area become unavailable. In addition, a couple of solutions are presented to mitigate the impacts.

scholarly journals Deep Learning-Based Named Entity Recognition and Knowledge Graph Construction for Geological Hazards

2019 ◽  
Vol 9 (1) ◽  
pp. 15 ◽  
Author(s):  
Runyu Fan ◽  
Lizhe Wang ◽  
Jining Yan ◽  
Weijing Song ◽  
Yingqian Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Knowledge Graph ◽  
Geological Hazard ◽  
Geological Hazards ◽  
Named Entity ◽  
Corpus Construction

Constructing a knowledge graph of geological hazards literature can facilitate the reuse of geological hazards literature and provide a reference for geological hazard governance. Named entity recognition (NER), as a core technology for constructing a geological hazard knowledge graph, has to face the challenges that named entities in geological hazard literature are diverse in form, ambiguous in semantics, and uncertain in context. This can introduce difficulties in designing practical features during the NER classification. To address the above problem, this paper proposes a deep learning-based NER model; namely, the deep, multi-branch BiGRU-CRF model, which combines a multi-branch bidirectional gated recurrent unit (BiGRU) layer and a conditional random field (CRF) model. In an end-to-end and supervised process, the proposed model automatically learns and transforms features by a multi-branch bidirectional GRU layer and enhances the output with a CRF layer. Besides the deep, multi-branch BiGRU-CRF model, we also proposed a pattern-based corpus construction method to construct the corpus needed for the deep, multi-branch BiGRU-CRF model. Experimental results indicated the proposed deep, multi-branch BiGRU-CRF model outperformed state-of-the-art models. The proposed deep, multi-branch BiGRU-CRF model constructed a large-scale geological hazard literature knowledge graph containing 34,457 entities nodes and 84,561 relations.

scholarly journals Multistage identification method for real-time abnormal events of streaming data

2019 ◽  
Vol 15 (12) ◽  
pp. 155014771989454
Author(s):  
Hao Luo ◽  
Kexin Sun ◽  
Junlu Wang ◽  
Chengfeng Liu ◽  
Linlin Ding ◽  
...  
Keyword(s):  
Real Time ◽  
Coal Mine ◽  
Early Warning ◽  
Sliding Window ◽  
Geological Structure ◽  
Streaming Data ◽  
Event Monitoring ◽  
Monitoring Method ◽  
Identification Method ◽  
Abnormal Events

With the development of streaming data processing technology, real-time event monitoring and querying has become a hot issue in this field. In this article, an investigation based on coal mine disaster events is carried out, and a new anti-aliasing model for abnormal events is proposed, as well as a multistage identification method. Coal mine micro-seismic signal is of great importance in the investigation of vibration characteristic, attenuation law, and disaster assessment of coal mine disasters. However, as affected by factors like geological structure and energy losses, the micro-seismic signals of the same kind of disasters may produce data drift in the time domain transmission, such as weak or enhanced signals, which affects the accuracy of the identification of abnormal events (“the coal mine disaster events”). The current mine disaster event monitoring method is a lagged identification, which is based on monitoring a series of sensors with a 10-s-long data waveform as the monitoring unit. The identification method proposed in this article first takes advantages of the dynamic time warping algorithm, which is widely applied in the field of audio recognition, to build an anti-aliasing model and identifies whether the perceived data are disaster signal based on the similarity fitting between them and the template waveform of historical disaster data, and second, since the real-time monitoring data are continuous streaming data, it is necessary to identify the start point of the disaster waveform before the identification of the disaster signal. Therefore, this article proposes a strategy based on a variable sliding window to align two waveforms, locating the start point of perceptual disaster wave and template wave by gradually sliding the perceptual window, which can guarantee the accuracy of the matching. Finally, this article proposes a multistage identification mechanism based on the sliding window matching strategy and the characteristics of the waveforms of coal mine disasters, adjusting the early warning level according to the identification extent of the disaster signal, which increases the early warning level gradually with the successful result of the matching of 1/ N size of the template, and the piecewise aggregate approximation method is used to optimize the calculation process. Experimental results show that the method proposed in this article is more accurate and be used in real time.

scholarly journals Surface Deformation Monitoring in Coal Mine Area Based on PSI

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 29672-29678 ◽  
Author(s):  
Changjun Huang ◽  
Hongmei Xia ◽  
Jiyuan Hu
Keyword(s):  
Coal Mine ◽  
Surface Deformation ◽  
Deformation Monitoring ◽  
Mine Area

Polycyclic aromatic hydrocarbon (PAHs) pollutants in groundwater from coal gangue stack area: characteristics and origin

2009 ◽  
Vol 59 (5) ◽  
pp. 1043-1051 ◽  
Author(s):  
X. W. Wang ◽  
N. N. Zhong ◽  
D. M. Hu ◽  
Z. Z. Liu ◽  
Z. H. Zhang
Keyword(s):  
Spatial Distribution ◽  
Aromatic Hydrocarbons ◽  
Coal Mine ◽  
Coal Gangue ◽  
Pollution Effects ◽  
Mine Area ◽  
Groundwater Samples ◽  
Polycyclic Aromatic ◽  
High Concentrations ◽  
Total Concentrations

The concentrations of polycyclic aromatic hydrocarbons (PAHs) in the leachate from the gangue and 20 groundwater samples, which were collected from the 12th Coal Mine around gangue piles in Henan Province, China, were determined by SPE-GC-MS. The characteristics of PAHs pollutants in groundwater were investigated, and compared with the concentrations of PAHs in the leachate from different weathered gangues to discuss the pollution effects of PAHs from coal gangue on groundwater. The results showed that total concentrations of the 16 EPA preferentially controlled PAHs ranged from 146.9 ng/L to 1220.6 ng/L.The components of PAHs such as chrysene, benzo[a]anthracene, benzo[b + k]fluoranthene, indeno[1,2,3-c,d]–pyrene, and dibenz[a,h]anthracene were fairly high. The 2–4 rings PAHs such as naphthalene, phenanthrene, fluorene and chrysene were dominant in groundwater, which was similar to those of the leachate from the different weathered gangues. Therefore, it should be paid much more attention on the transport of lower ring numbered PAHs leached by rains from the coal mines after landfilling and dumping. Based on the spatial distribution of PAHs and the high concentrations of PAHs with 2–4 rings in groundwater and leaching samples, there might be other pollution sources of PAHs except for penetration from coal gangue into groundwater in the Pingdingshan coal mine area.

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