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Landslides are regarded as significant geological hazards across the world, causing serious economic losses and casualties. The understanding on deformation characteristics and failure mechanisms of landslides plays the vital roles in slope stability evaluation and reinforcement design. In this study, the deformation characteristics and failure mechanism of the Xiaomiaoling talus slope were analyzed based on field monitoring data. In addition, as it was difficult to measure the shear strength parameters of the rock–soil mixture due to its complex spatial structure and variable material composition, a displacement back analysis based on the back-propagation neural network (DBA-BPNN) was proposed to determine the shear strength parameters of the rock–soil mixture. The analytical results show that deformation of the Xiaomiaoling talus slope was that of a typical traction landslide, which has the characteristics of progressive failure, and major slope deformation was triggered by excavation and rainfall. According to field monitoring data, the shear strength parameters of the rock–soil mixture could be determined. The predicted cohesion and internal friction angle of the rock–soil mixture were 10.84 kPa and 19.51°, respectively, and the predicted and test values were in good agreement. The method proposed in this paper can provide references for the design and construction in geotechnical engineering.

Accurate recognition of water inrush sources is important in mine water hazard control. In this study, 118 water samples of four water types in the Qinan coal mine were analysed by multiple logistic regression, and the 12 water samples that did not meet the requirements were removed. The remaining 106 were used as training samples to establish a Bayes recognition model (BRM). In addition, the BRM was used to complement multiple logistic regression analysis (MLRA) to discriminate other water samples in the mining area. The recognition accuracy of the combined model was 95.28%. The results from the model were consistent with the field water samples and showed that the combined MLRA-BRM approach fully considers the hydraulic relationships between different aquifers and a mixed water inrush source. Moreover, the MLRA-BRM combination improved water inrush source recognition accuracy and was more reliable than using MLRA or BRM alone.

In order to meet the growing energy needs, more and more people pay attention to the deep coal resource (>1000 m). Compared with shallow coal mining, deep coal mining is facing more complex problem...

Detecting the development height of water flowing fractured zone in the roof of coal seam is of great significance for the roof safety of working face with developed sandstone and thick loose layer. This paper analyzes the influence of the induced polarization effect of electrode on the traditional single-mode parallel electrical method. In order to avoid this interference, the dual-mode parallel electrical method is applied to monitor the roof deformation and failure of a coal mine in Ordos Basin. The monitoring results show that: under the influence of mining, the change of geoelectric field of coal seam roof is dynamic, the apparent resistivity of initial mining fracture is high, the apparent resistivity of surrounding rock water filling mining fracture is low, and the apparent resistivity of fracture water flowing to goaf is high again. According to the characteristics of geoelectric field, the maximum height of water flowing fracture zone is 122 M; The average mining coal thickness of the working face is 11 m, and the fracture / mining thickness ratio is 11.1. The results of plastic zone show that the maximum height of water flowing fractured zone above the working face is 122 m, which is consistent with the test results. The vertical stress response characteristics of roof monitoring points are zonal. Under the influence of mining, the loose layer near the surface above the working face is cracked. The edge type ground fissures exist for a long time, and there are a series of geological problems such as soil erosion, ground subsidence.

Determining the leakage location in an underground gas pipeline is a serious technical problem. Distributed fiber optic testing technology based on Brillouin optical frequency domain analysis (BOFDA) can be used for monitoring the change in the strain and temperature of the surrounding media caused by the gas leakage. In this study, a point source leakage model was built. The distributed fiber optic strain and actively heated fiber optic were installed around the pipeline to monitor the gas pipeline leakage. Variations in the strain and temperature characteristics of the pipeline under different gas pressure conditions were found to be in the range of −392–402 μϵ and 3.5 °C during the 1.0 MPa gas leakage, respectively. When the soil model was loaded with a gas leakage pressure of 0.3–1.0 MPa, a significant variation in strain and temperature was noticed. The copper mesh actively heated fiber optic can effectively identify the location of the leak. Meanwhile, the heating rate of the actively heated fiber optic cables in different media environments were obtained and ranked from fast to slow: air, followed by general soil, followed by leakage soil. This led to the generation of new technology to detect the leakage location of buried gas pipelines through actively heated optical cables.

The sandstone fissure aquifer of the Permian main mining coal seam is the direct water-filled aquifer of the coal mine in northern Anhui Province, China. Hydro-chemistry is important for solving the issue related to water disasters of coal mines, as it can be used for the determination of source of inrush water, and understanding the water-rock interaction process. In this paper, major ion concentrations of groundwater from the in the roof (RW) and floor (FW) in Haizi coal mine have been processed by statistical analysis and water-rock interaction analysis in order to study the differences of hydro-chemical characteristics and genesis. The results show that sandstone fissure water ion contents in RW and FW are obviously different; the chemistry type of FW is Ca-Mg-SO4, followed by HCO3-Cl and HCO3-Cl-SO4, and that of RW is HCO3-Cl, followed by SO4-HCO3-Cl. Compared with the fourth aquifer, Taiyuan group limestone water, goaf water, the proportion of Ca2+, Mg2+, SO42- in the FW and Cl-, HCO3- in the RW are significantly different. Factor analysis and water-rock interaction research show that the FW of 10th coal seam aquifer controlled by “removing sulfate,” silicate mineral dissolution and impact of groundwater runoff. However, the RW is mainly related with the dissolution of minerals, for example, salt minerals (e.g., mirabilite), bitter salt minerals (e.g., salt), silicate minerals, and the dissolution of gypsum.

This article puts forward the gray markov prediction model to predict mine gas emission by combining grey system theory and markov chain theory. And GM (1, 1) model is established in the first place for grey data by the model. To eliminate the error, and improve the prediction accuracy of the model, secondary parameters fitting was done on the basis of GM (1, 1) model. And we get second parameter fitting for trend prediction. Then using Markov state transfer probability matrix prediction method to do quadratic fitting for its predictive value, which can improve the prediction precision of the volatile random variables. It proves the prediction results of the model are satisfactory by analyzing history data of gas emission prediction. This conclusion broadens the application scope of grey forecast model and provides a new method for mine gas emission scientific forecast.

Mine water inrush is one of the major geological hazards that threaten safe production in coal mines. The accurate identification of mine water inrush sources plays a vital role in mine water disaster control, and it is the key to preventing mine water inrush incidents. Ninety-three water samples were extracted from the three types of aquifers in the Qinan coal mine. The cluster analysis method was then used to analyze 82 of the original water samples, and the other 11 water samples that did not meet the requirements were removed. Then, the remaining 82 water samples were regarded as training samples, and the principal component analysis was completed. Taking the scores of the principal components as the independent variable and the types of water inrush sources as the dependent variable, the multiple logistic regression recognition model was established. Meanwhile, this recognition model was used to recognize the types of mine water inrush sources and verify the recognition accuracy for the 82 training samples. The comprehensive recognition accuracy reached 86.6%, which is much higher than the traditional recognition methods of water inrush sources. Based on cluster analysis, the multiple logistic regression recognition model fully considers the ion content measurement errors and the complex relationships between the internal ions, and this recognition model is more reasonable and improves the accuracy of water inrush source recognition. This paper provides a new method for recognizing the problem of water inrush sources, which also provides an effective basis for mine water inrush prevention and control.

Because of the seepage problem of concrete dam foundation, the water head, pore water pressure, hydraulic gradient and seepage velocity of each point in the seepage field are obtained by drawing flow net, and finally the seepage flow is obtained to predict the seepage of dam foundation. In this paper, the flow net diagram is used to solve the pore water pressure at the fixed point of the dam foundation soil layer under three different conditions: homogeneous isotropy, homogeneous anisotropy and layered soil. The calculation accuracy of the flow net method is verified through the calculation with the finite difference software FLAC3D numerical analysis. The results indicated that the pore water pressure of the three kinds of dam foundation soil layers calculated by the flow net method is more similar from that calculated by the numerical method, which indicates that the flow net method has high accuracy and can be used in some engineering practice with difficult modeling.

To investigate the geochemical characteristics of thermally-altered coals formed by igneous intrusion, a series of roof and floor mudstone, thermally-altered coal, and igneous rock samples were collected from a representative borehole profile of the Pansan No. 4-2 coals of the Huainan Coalfield, Anhui Province, Eastern China. Furthermore, seven unaltered coal samples from the same coalbed were collected from seven coal mines of the Huainan Coalfield for comparison. The minerals were analyzed using X-ray diffractometry and optical microscopy. The major element oxides and trace elements were determined by X-ray fluorescence (XRF), inductively coupled plasma mass spectrometry (ICP-MS), ICP-MS with collision/reaction cell technology, an ion-selective electrode (ISE), and a DMA-80 mercury analyzer. The results indicate that kaolinite and quartz are the dominant minerals in the thermally-altered coals, and pyrite is dominant at the contact zone between the thermally-altered coal and igneous rock. The unaltered Huainan No. 4-2 coals were slightly enriched in only Cr, Se, and W compared to the Chinese coals. However, the thermally-altered coals were slightly enriched in F and V and depleted in Co, Ni, Hg, Sb, Zn, and W compared to the Chinese coals. Although the igneous intrusion reached a thickness of 1.97 m, it had a limited effect on the trace elements of terrigenous origin, i.e., Li, F, Sr, Sn, V, Cr, Co, Ni, Cs, Sc, Ga, Rb, Zr, Nb, Ag, Cd, In, Ba, Hf, Ta, W, Th, U, and rare earth elements and Y (REY). Contact metamorphism resulted the devolatilization and redeposition of coal organic matter, which increased the Be and Ge concentrations of the thermally-altered coals adjacent to the intrusion. Copper, As, Sb, Tl, and Bi were transported by the igneous intrusion and redeposited in the thermally-altered coals. The coals were almost completely stripped of Zn, Se, Mo, Pb, and Hg by the igneous intrusion.

The geochemistry of source rock–controlled coals from the Late Triassic Daqiaodi Formation in the Dabaoding Coal Mine of the southwestern Sichuan Province was investigated. A total of 15 coal samples, 6 parting samples, and 4 roof and floor rock samples from the D15–3 and D18 coals were subjected to proximate and ultimate analyses for coal quality, by using LTA-XRD with Siroquant software for minerals, XRF for major element oxides, ICP-MS for trace elements, ICP-CCT-MS for As and Se, ISE for F, and a DMA-80 analyzer for Hg. Results indicate that the Dabaoding D15–3 and D18 coals are classified as low ash and low-volatile bituminous coal. Minerals in coals are dominated by quartz, kaolinite, illite, and dolomite. Most trace elements in the Dabaoding coals are depleted or at normal levels relative to Chinese coals or world hard coals (except for Cr with slight enrichment). The Dabaoding coals and host rocks are characterized by weakly positive Ce and Y anomalies, strongly positive Gd anomalies, and weakly negative Eu anomalies. Based on paleogeographical, mineralogical, and geochemical evidence, the sediment source rocks of the Daqiaodi coal-bearing strata are identified as the Panzhihua trachyte at the top of the Kangdian Upland, which exerted crucial impacts on the Dabaoding coals.

The Donglin Coal Mine, an important mine in the Nantong coalfield in southwestern Chongqing, is immediately adjacent to the Songzao coalfield. To investigate the mineralogy and geochemistry of Donglin coals, profile channel samples from the mineable Nos. 4 and 6 coals were collected. The minerals, major element oxides, and trace elements were analyzed by LTA-XRD in combination with Siroquant software, XRF, ICP-MS, ICP-CCT-MS (As and Se), ISE (F), and DMA-80 Hg analyzer (Hg). The results indicate that the Donglin low volatile bituminous coals were characterized by low ash yields and high total sulfur contents. The minerals varied between the Nos. 4 and 6 coals, differentiated by montmorillonite, calcite, ankerite, and pyrite. Despite the low boron concentration, Donglin coals were influenced by brackish water during coal accumulation. Selenium (10.9 mg/kg), F (431 mg/kg), Hg (0.350 mg/kg), Nb (20.7 mg/kg), and Ta (3.68 mg/kg) in the No. 4 coals, and F (425 mg/kg), Be (7.11 mg/kg), Ga (14.5 mg/kg), Ge (22.5 mg/kg), Zr (218 mg/kg), Nb (35.5 mg/kg), and Ta (4.42 mg/kg) in the No. 6 coals are slightly enriched. Four vertical distributions of elements were demonstrated, i.e., roof, floor, roof and floor, and middle enrichments. The slightly enriched trace elements and UCC-normalized REY distribution patterns of the Donglin coals jointly eliminated the impact of various volcanic ashes and terrigenous input from the Kangdian Oldland during the Late Permian. However, the roof rock samples shared the geochemical feature of mafic tonstein (enriched Sc, V, Cr, Co, and Ni), while the floor rock of the No. 6 coals resembles alkali tonsteins (enriched Ga, Zr, Nb, Ta, Hf, and REY). Lithium, Ga, Zr, Ag, Cd, In, Sn, Cs, Hf, Bi, Th, and U occurred in kaolinite and/or montmorillonite in the No. 4 coals, while Li, Nb, Ag, Cd, In, Cs, Ba, and Th occurred in kaolinite in the No. 6 coals. Vanadium, Sr, and Mo in the No. 4 coals, and Sr and Sc in the No. 6 coals were associated with calcite and/or ankerite. Arsenic, Se, Hg, Sb and Tl, and Hg and Mo were associated with pyrite in the Nos. 4 and 6 coals, respectively. Lithium, Be, Ge, and Sb in the No. 4 coals and Be and Ge in the No. 6 coals might be of organic affinity.

In order to obtain the value of confined water progressive intrusion height of mining fracture floor, the analysis equation was deduced based on the fracture extension theory of the fracture mechanics. Furthermore, the influence of some parameters (e.g., advancing distance of working face, water pressure, initial fracture length and its angle) on confined water progressive intrusion height were analyzed. The results indicate that tension-shearing fracture of floor is extended more easily than compression-shearing fracture under the same conditions. When floor fracture dip angle is less than 90°, tension-shearing extension occurs more easily on the left edge of the goaf. If fracture dip angle is larger than 90°, it occurs more easily on the right edge of the goaf. The longer the advancing distance of working face is, the greater initial fracture length goes; or the larger water pressure is, the greater possibility of tension-shearing extension occurs. The confined water progressive intrusion height reaches the maximum on the edge of the goaf. Field in situ test is consistent with the theoretical analysis result. Keywords: Tension-shearing fracture, Compression-shearing fracture, Confined water progressive intrusion, Floor water invasion

Due to its environmental and resource impacts, the geochemistry of uranium in coal is of both academic and practical significance. In order to give a comprehensive summary about the geochemistry of uranium in coals, the abundance, distribution, and modes of occurrence of uranium in Chinese coals were reviewed in this paper. Although some coals from southwestern and northwestern China are significantly enriched in uranium, the common Chinese coals are of a comparable uranium concentration to the world coals. The roof and floor rocks, and parting of coalbeds, or coal benches that are close to the surrounding rock are favorable hosts for uranium in one coalbed. The uranium concentrations in coals of different ages decrease in this order, e.g., Paleogene and Neogene > Late Permian > Late Triassic > Late Carboniferous and Early Permian > Late Jurassic and Early Cretaceous > Early and Middle Jurassic. Uranium in Chinese coals is mainly associated with organic matter, and is correspondingly enriched in subbituminous coal and lignite.

Uranium, which is of chemotoxic and radiotoxic, is connately present in coals. In view of its dual roles in resource recovery and environmental impact, the geochemistry of uranium in Chinese coals and its atmospheric emission by coal-fired plants in China are discussed in this article. The average uranium concentration of the majority of Chinese coals is 2.43 mg/kg, which is comparable to that of world coals. However, in coals from certain local regions of China, i.e. Yunnan, Guizhou, Guangxi, Xinjiang, Inner Mongolia, and Chongqing Provinces, the uranium is significantly enriched; and even abnormally enriched to form coal-hosted uranium deposits, e.g. coals from the Yili and Tarim Basins of Xinjiang Province, Bangmai Basin of Yunnan Province, and Mabugang Basin of Guangdong Province. Uranium in Chinese coals is associated with organic matter, silicates, pores, pelitic components, phosphate minerals, and uranium minerals. The enrichment of uranium in Chinese coals is attributed to the weathering of source rocks, volcanic ashes, magmatic intrusion, marine water influence, groundwater, hydrothermal fluids, organic matter, palaeoclimate, and the geologic conditions of coal-accumulating basins. Uranium is prior to partition into fly ash. About 62.9 tons of uranium were released into atmosphere from Chinese coal-fired power plants in 2014. Some environmental and human health problems were historically related to uranium in coal worldwide.

The landfill leachate treated by sonication in presence of Fe2+ (US/Fe2+) and then by photo-Fenton achieved the highest total organic carbon (TOC) removal efficiency among the screened processes. The lower initial pH, dosage of Fe2+ and initial concentration of leachate were helpful in raising TOC removal efficiency of leachate by US/Fe2+. The optimal conditions for the US-photo-Fenton process were as follows: initial pH at 3.0, [H2O2]/[TOC0] at 2, [H2O2]/[Fe2+] at 5 and initial concentration of landfill leachate at 600 mg/L. The removal efficiency of TOC, chemical oxygen demand (COD) and 5-day biochemical oxygen demand (BOD5) were 68.3, 79.6 and 58.2%, while the BOD5/COD rose from 0.20 to 0.43 at optimum condition. Based on gas chromatography–mass spectrometry (GC-MS) results, 36 of a total of 56 pollutants were completely degraded by US-photo-Fenton treatment.

Under the current situation of energy demand, coal is still one of the major energy sources in China for a certain period of time, so the task of coal mine safety production remains arduous. In order to identify the water source of the mine accurately, this article takes the example from Renlou and Tongting coal mines in the northern Anhui mining area. A total of 7 conventional water chemical indexes were selected, including Ca2+, Mg2+, Na++K+, Cl-, SO4 2-, HCO3 - and TDS, to establish a multivariate matrix model for the source identifying inrush water. The results show that the model is simple and is rarely limited by the quantity of water samples, and the recognition effect is ideal, which can be applied to the control and treatment for water inrush.

Arsenic is one of the toxic trace elements in coals, which is harmful to both the ecological environment and human health. Based on published literature and the data obtained by our research group, a total of 5314 As concentrations of Chinese coals were analyzed. The arithmetic mean of arsenic content in Chinese coals is 6.97 mg/kg. Choosing the percentage of provincial coal resources in national coal resources as the weighting factor, the weighted average of arsenic content in Chinese coals is 5.33 mg/kg. The content of arsenic in Chinese coals increases from the north to the south. High arsenic content in coal primarily occurs in southwestern Yunnan and certain coalfields in the Guizhou Province. Additionally, arsenic is enriched in the coals from some regions, i.e., the western Yunnan, Guangxi, Tibet, southwestern Liaoning, Jilin, and Henan. The arsenic content in coals of different coal-forming periods shows an overall regularity: Paleogene and Neogene > Late Triassic > Late Permian > Late Jurassic and Early Cretaceous > Early and Middle Jurassic > Late Carboniferous and Early Permian. The modes of occurrence of arsenic in coals include sulfide-association, organic-association, arsenate-association, silicate-association, and soluble- and exchangeable-association. Generally, arsenic in Chinese coals exists predominantly in arsenic-bearing pyrite. Meanwhile, the organic arsenic content is relatively high in coal samples with a lower (