The no. 11 coal seam in the deep area of Hancheng mining area is mining in recent years, which is threatened by the water inrush from the Ordovician limestone aquifer. Coal-floor water inrush is governed by the water abundance of coal-floor aquifer, the water-resisting performance of coal-floor aquitard, and the pathway connecting the water source and the working face. To make an accuracy risk assessment of water inrush from the no. 11 coal seam floor, a GIS-based vulnerability index method (VIM) is adopted for its superior comprehensive consideration of more controlling factors, powerful spatial analysis, and intuitively display functions. This study firstly established an index system including the water pressure of the coal-floor aquifer, the unit water inflow, the thickness, the core recovery percentage, the thickness ratio of brittle rocks to ductile rocks, the thickness of effective aquitard, and the accumulated length of faults and folds, of which the former six indexes governed the water abundance of the coal-floor aquifer which was combined with the last two factors to determine the risk of coal-floor water inrush. Secondly, the thematic map of each controlling factor is established by GIS using the geological prospecting data, and the weight of each factor is determined by the analytic hierarchy process (AHP) after consulting the expert review panel. At last, a vulnerability index is obtained and used to assess the risk of coal-floor water inrush of the no. 11 coal seam. The risk of water inrush of the no. 11 coal seam of the study area was ranked to three zones: the southeastern shallow area in red color is the dangerous zone, the wide northwestern area in green color is the safe zone, and the transition area in yellow color is the moderate-risk zone. Compared with the actual water-inrush incidents, the risk assessment result was verified to achieve an accuracy of 82.35%, which is proved to be a dependable reference for the prevention and controlling of coal-floor water inrush of the no. 11 coal seam in Hancheng mining area. 相似文献
GNSS coordinate time series data for permanent reference stations often suffer from random, or even continuous, missing data. Missing data interpolation is necessary due to the fact that some data processing methods require evenly spaced data. Traditional missing data interpolation methods usually use single point time series, without considering spatial correlations between points. We present a MATLAB software for dynamic spatiotemporal interpolation of GNSS missing data based on the Kriged Kalman Filter model. With the graphical user interface, users can load source GNSS data, set parameters, view the interpolated series and save the final results. The SCIGN GPS data indicate that the software is an effective tool for GNSS coordinate time series missing data interpolation. 相似文献
Geotechnical and Geological Engineering - The periodic reservoir water level (RWL) fluctuation on bank slopes during water impoundment in Three Gorges Reservoir (TGR) may lead to the reactivation... 相似文献
Microbes live throughout the soil profile. Microbial communities in subsurface horizons are impacted by a saltwater–freshwater transition zone formed by seawater intrusion (SWI) in coastal regions. The main purpose of this study is to explore the changes in microbial communities within the soil profile because of SWI. The study characterizes the depth-dependent distributions of bacterial and archaeal communities through high-throughput sequencing of 16S rRNA gene amplicons by collecting surface soil and deep core samples at nine soil depths in Longkou City, China. The results showed that although microbial communities were considerably impacted by SWI in both horizontal and vertical domains, the extent of these effects was variable. The soil depth strongly influenced the microbial communities, and the microbial diversity and community structure were significantly different (p < 0.05) at various depths. Compared with SWI, soil depth was a greater influencing factor for microbial diversity and community structure. Furthermore, soil microbial community structure was closely related to the environmental conditions, among which the most significant environmental factors were soil depth, pH, organic carbon, and total nitrogen.