Abstract Accurate extraction of ground fissures caused by intense coal mining has the potential to significantly improve the efficiency of environmental monitoring in mining areas. However, the extraction results using previous methods have often exhibited issues of discontinuity and substantial deviation from ground truth data, resulting in low extraction accuracy. In this study, a novel approach, ENVINet5‐OBIC, for extracting ground fissures in mining areas is proposed, which integrates object‐based image classification (OBIC) with the pixel‐based deep learning model ENVINet5. ENVINet5‐OBIC uses OBIC to segment high‐resolution unmanned aerial vehicle (UAV) images across different scales, effectively considering shape, texture and correlative information between adjacent pixels. Furthermore, by utilizing homogeneous objects as building blocks, it establishes a deep learning model for the automated extraction of ground fissures. Experimental results show that ENVINet5‐OBIC performs better when compared with OBIC, U‐Net, PSPNet and ENVINet5 methods in terms of continuity, accuracy and error reduction. In addition, the ground fissure area extracted by ENVINet5‐OBIC closely aligns with ground truth data. This study provides a more effective method for automatic extraction of ground fissures, which improves the efficiency of environmental monitoring in mining areas.
Shenhu Area is located in the Baiyun Sag of Pearl River Mouth Basin, which is on the northern continental slope of the South China Sea. Gas hydrates in this area have been intensively investigated, achieving a wide coverage of the three-dimensional seismic survey, a large number of boreholes, and detailed data of the seismic survey, logging, and core analysis. In the beginning of 2020, China has successfully conducted the second offshore production test of gas hydrates in this area. In this paper, studies were made on the structure of the hydrate system for the production test, based on detailed logging data and core analysis of this area. As to the results of nuclear magnetic resonance (NMR) logging and sonic logging of Well GMGS6-SH02 drilled during the GMGS6 Expedition, the hydrate system on which the production well located can be divided into three layers: (1) 207.8–253.4 mbsf, 45.6 m thick, gas hydrate layer, with gas hydrate saturation of 0–54.5% (31% av.); (2) 253.4–278 mbsf, 24.6 m thick, mixing layer consisting of gas hydrates, free gas, and water, with gas hydrate saturation of 0–22% (10% av.) and free gas saturation of 0–32% (13% av.); (3) 278–297 mbsf, 19 m thick, with free gas saturation of less than 7%. Moreover, the pore water freshening identified in the sediment cores, taken from the depth below the theoretically calculated base of methane hydrate stability zone, indicates the occurrence of gas hydrate. All these data reveal that gas hydrates, free gas, and water coexist in the mixing layer from different aspects.
A new retrogressive thaw slump (RTS) inventory in central Qinghai–Tibet Plateau (QTP) were generated based on visual interpretation of nine satellite images (WV-2, Google Earth image, Ziyuan-3, Gaofen-2, Gaofen-1) and field investigations. A total 459 RTSs were confirmed with an accumulative area of 1199.49 ha (in time slice of 2018-2020). To reduce the uncertainty in identifying the RTS boundaries, all of the images were spatially corrected based on a reference image, which was obtained on 29 December 2015. The RTS inventory published by Luo et al., (2022) and Xia et al., (2022) were refered when we conduct visual interpretation. Note: the RTSs were distingusihed into active RTSs (TYPE=Y) and non-active RTSs (TYPE=N). Those RTSs were neither active nor non-active RTSs when their area increase vary from 0 to 0.01 ha or less than 0 (TYPE=T). The field named "area_ha", "perime_km" are the area and perimeter of the RTSs in the attribute tables of shapefiles. The units of area and perimeters are hectare (ha) and kilometer (km), respectively. The field named "type" indicates the status of RTSs. The "Y" and "N" are means the RTS is belongs to active RTS and non-active, respectively.
Change of hydrological processes and its influence mechanisms are complex.On one hand,climate change and human activities affect hydrology and water resources;and on the other hand,hydrology and water resources show responses to the impact.The duality and uncertainty make the research on the issue more complex and developed very slowly.Domestic research in this field is still at an early stage.This paper summarizes the latest research progress of hydrology and water resources effects caused by climate change and human activities at home and abroad,points out the weak links of the research in this field,and looks forward to the main research direction in future.It is found that the existing researches were mainly focused on a single factor of climate change or human activities impacting hydrology and water resources.However,there were few researches combining these two factors and making a quantitative distinction.In future,quantitative research should be enhanced and the importance of each factor should be separately identified,which are of importance to strength the research in the field.
Using the physical deterministic model to analyze landslide stability has become a hotspot of landslide disasters research all over the world. The Digital Elevation Model (DEM) resolution has a great influence on the simulation effect of 3 D physical models. However, few researchers have studied the prediction performance of the 3 D models under different DEM resolutions. Therefore, based on the 3 D model Scoops3D, the spatial distribution of landslides was simulated and predicted under five different DEMs resolutions (2.5 m, 5 m, 10 m, 20 m, and 30 m). The optimal parameters of the model were obtained through field investigations and laboratory experiments, and then, the simulation results were compared with the actual landslides distribution. Receiver operating characteristic (ROC) analysis and %LRclass index were used to quantitatively evaluate the prediction performance of the 3 D model under five different DEMs resolutions. The results show that Scoops3D has good performance in landslide spatial distribution prediction. In addition, we also found that the simulation results of high-resolution DEM were not ideal, while the prediction results of medium resolution DEMs (i.e., 5 m and 10 m) were more accurate. Therefore, this study provide a reference to select the most suitable DEM resolution for landslide stability analysis.
This study was undertaken to produce landslide susceptibility maps by the frequency ratio (FR) and weight-of-evidence (WOE) methods for the Qingshui River Basin, and compare three combinations of different controlling factors to get the best number for analysis. Since conditioning factors create suitable conditions for landslides, 11 such parameters were used for this study: slope angle, aspect, altitude, valley depth, lithology group, distance to water bodies, stream power index, topographic wetness index, longitudinal curvature, cross-sectional curvature, and relief. Performances of models with 6, 8, and 11 of these factors were evaluated using two models to obtain reliable landslide susceptibility maps, investigate the effect of different numbers of factors, and determine the most effective. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) were used to verify the accuracy of the landslide susceptibility assessment results. AUCs for the prediction rate curve of FR and WOE, with 6, 8, and 11 landslide variables, were 0.765, 0.731, 0.702 and 0.771, 0.728, 0.717, respectively. The results indicate that WOE model performed better than the FR model in the basin and that accuracy of evaluation decreases (rather than increases) with an increase in number of variables.Abbreviations: FR: frequency ratio; WOE: weight-of-evidence
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