Currently, there is a contradiction between coal mining and protection of water resources, meaning that there is a need for an effective method for discriminating the source of mine gushing water. Ningtiaota Coal Mine is a typical and representative main coal mine in the Shennan mining area. Taking this coal mine as an example, the self-organizing feature map (SOM) approach was applied to source discrimination of mine gushing water. Fisher discriminant analysis, water temperature, and traditional hydrogeochemical discrimination methods, such as Piper and Gibbs diagrams, were also employed as auxiliary indicators to verify and analyze the results of the SOM approach. The results from the three methods showed that the source of all the gushing water samples was surface water. This study represents the innovative use of an SOM in source discrimination for the first time. This approach has the advantages of high precision, high efficiency, good visualization, and less human interference. It can quantify sources while also comprehensively considering their hydrogeochemical characteristics, and it is especially suitable for case studies with large sample sizes. This research provides a more satisfactory solution for water inrush traceability, water disaster prevention and control, ecological protection, coal mine safety, and policy intervention.

No abstract

Mine water inrush is a major type of disaster in coal mine production in China, it causes heavy casualties and serious economic losses and threatens coal mine safety. To quickly and accurately identify mine water inrush source, according to the hydrochemical characteristics of different aquifers in the Donghuantuo mining area, this paper systematically analyzes the hydraulic connection of the aquifers in main coal mining areas before and after mining activities. Collected four types of hydrochemical data of No. 5 coal seam roof water, No. 8 coal seam roof water, No. 122 coal seam floor water, and No. 1214 coal seam aquifer water in the Donghuantuo mining area. In addition, based on the hydrochemical data, the parameter selection of LightGBM was optimized by Particle Swarm Optimization (PSO) and constructed the PSO-LightGBM water inrush source identification model. The recognition accuracy of PSO-LightGBM model was compared with LightGBM model, classification regression tree (CART) model, and random forest (RF) model. The results showed that coal mining activities would have a significant impact on the water quality characteristics of the roof sandstone fissure water of No. 5 coal mine. Mining activities had a certain impact on the accuracy of the identification model. In addition, compared with the four recognition models, PSO-LightGBM model had the highest recognition accuracy of 97.22%. It showed that the model had high accuracy, stability, generalization ability, and important reference value for the identification of mine water inrush source.

No abstract

There is a coupling relationship between surrounding rock stress, deformation, and fracture evolution, especially in the microdynamics of the crust caused by mining activities and earthquakes. Previous research has investigated many cases regarding the coseismal water level responses and proposed a method to calculate the aquifer parameters by tidal analysis. However, to date, measurement of the degree of rock damage in the field has not been reported. Quantifying the fracture characteristics is essential for accurate evaluation of rock stability. This study has analyzed the relationship between the seismograms and hydroseismograms in response to the Mw 7.8 Solomon Islands earthquake and the Mw 7.8 Kaikōura earthquake, both events occurring in 2016. The calculated and measured changes in water level in the X10 well were fitted in order to study the relationships among the volumetric strain, the deviatoric strain, and the oscillations in the pore pressure. Then, we further estimate the degree of rock damage and the hydraulic characteristics of the aquifer. The results showed that the values for the rock damage parameter, 0.662 < αD < 0.754, and the Skempton coefficient, −0.100 < A < 0.026, estimated for the Solomon Islands earthquake signified higher damage and dilatancy in the X10 well. Also, the respective values for the parameters, 0.293 < αD < 0.363 and 0.226 < A < 0.251, calculated for the Kaikōura earthquake signified a lower degree of rock damage. It is concluded that the changes in the pore pressure were influenced by both the volumetric strain and the deviatoric strain. The degree of rock damage and the hydraulic properties of the aquifer estimated from the water level fluctuations in the wells which were induced by the seismic waves represent the actual aquifer characteristics.

There are numerous coal mines around the Nansi Lake Provincial Nature Reserve, and the mineral resources are extremely rich. Therefore, it is necessary to effectively assess the impact of mining activities on the topsoil. Based on a focused investigation of the contamination status and ecological risks of the Nansi Lake Nature Reserve assisted by GIS, principal component analysis was combined with positive matrix factorization to quantitatively identify the sources and contributions of heavy metal(loid)s in the topsoil before conducting uncertainty analysis. The results showed that coal mining caused higher Cu, Zn, and As contamination levels, while Hg and Cd had higher eco-toxicity and biological sensitivity. Meanwhile, principal component analysis (PCA) and positive matrix factorization (PMF) modeling displayed that Hg (59.2%) was primarily generated by industrial sources (fossil fuel combustion and mercury-containing wastewater); As (70.2%), Ni (65.6%), Cr (63.5%), Pb (61.3%), Cu (60.3%), and Zn (55.8%) were generated mainly from coal mining and processing, coal fossil fuel combustion, and coal gangue dumps; Cd (79.8%) came mainly from agricultural sources. Through uncertainty analysis, the contribution of contamination sources to the heavy metal(loid)s in the topsoil, as estimated by the PMF model, was shown to be quite different. Moreover, heavy metal(loid)s with lower contributions had errors in source identification and factor quantification. This study innovatively warned management to control the hazards of heavy metal(loid)s caused by mining engineering to protect the environment of the Nansi Lake Nature Reserve and revealed the potential harmful pathways of heavy metal(loid)s.

No abstract

No abstract