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纸质出版日期： 2021-07-07 ，
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刘培，谷灿，李庆亭，张合兵，韩瑞梅，陈正超.2021.深度语义分割支撑下的尾矿库风险检测.遥感学报，25（7）： 1460-1472
Liu P，Gu C，Li Q T，Zhang H B，Han R M and Chen Z C. 2021. Deep learning semantic segmentation supported risk monitoring of tailings reservoir basin. National Remote Sensing Bulletin， 25（7）：1460-1472
尾矿库是矿山企业选矿的必要设施，同时对周围环境也是一个重大的危险源。为研究流域范围内尾矿库的溃坝路径以及对矿区地表造成的风险，以赤城县为例，利用GF-1高分辨率遥感影像，基于遥感RS（Remote Sensing）及地理信息系统GIS（Geographic Information System）手段对尾矿库的流域风险进行了监测分析。首先，通过分析尾矿库在遥感影像上的纹理、色调、形状以及大小等特征，制作了用于目标检测的尾矿库样本集，然后，在原始SSD目标检测网络基础上添加了反卷积模块和连接模块构建多尺度融合目标检测算法MSF_SSD，在目标检测结果基础上使用PSPnet算法实现尾矿库结构分割，得到了尾矿库内部结构—坝体以及库区，运用RS与GIS技术对尾矿库的上游汇水面以及事故可能径流进行提取，进而基于Arc Hydro模型模拟尾矿库的溃坝路径。最后，通过构建溃坝路径的缓冲区，得到尾矿库发生溃坝所造成的地物影响范围及面积。研究结果表明：赤城县尾矿库的溃坝路径总体是从西向东，从北向南，受溃坝影响的地物总面积达到480 km
2
。其中，林地176.52 km
2
，耕地175.52 km
2
，城市建设用地43.74 km
2
，农村建设用地2.47 km
2
，水体17.72 km
2
，草地和牧场分别为3.60 km
2
、1.22 km
2
。研究成果可用于分析尾矿库溃坝造成的地物损失以及影响范围面积等信息，提升尾矿库的风险管理水平及应急响应能力，为有关部门制定决策提供理论依据。
Tailings reservoir is a necessary facility for mining activity
and it also causes danger to surrounding environment. . Watershed risk of the tailings reservoir in Chicheng was monitored and analyzed using GF-1 high-resolution remotely sensed data with the help of multiscale fusion and deep learning method
as well as the support of Remote Sensing (RS) and Geographic Information System (GIS) technology for a comprehensive and detailed identification and extraction of the risk information of the tailings reservoir and to study the dam-break path of the tailings reservoir in watershed and the risk to land surface over mining area.
In this research
a sample set library for target detection was constructed by analyzing texture
hue
shape
and size of the tailings reservoir on the remotely sensed data. Subsequently
an improved multi-scale fusion algorithm (e.g.
Multi_Scale Feature Map_SSD (MSF_SSD)) was constructed by adding a deconvolution module and a connection module to the original single shot multiBox detector (SSD). Next
the Pyramid Scene Parsing network (PSPnet) algorithm was selected to achieve the structure of the tailings reservoir on the basis of the target detection results. The internal structure of the tailings reservoir was obtained. With the help of RS and GIS technology
the surface of upstream catchment and the possible danger runoff were extracted
and dam-break path of the tailings reservoir is simulated on the basis of the arc hydro model. Finally
the range area affected by the dam-break were obtained by constructing the buffer zone of the dam-break path.
The research results shown that the dam-break path of the tailings reservoir in Chicheng is generally from west to east and from north to south
and the total area affected by the dam-break was 480 km
2
. The combination analysis with land use/ cover classification indicated that forest land was 176.52 km
2
farm land was 175.52 km
2
urban land was 43.74 km
2
rural construction land was 2.47 km
2
water body was 17.72 km
2
grassland was 3.60 km
2
and pasture was 1.22 km
2
.
The sample library constructed using GF-1 remotely sensed data and Google Earth 16 level image can provide the basis for the automatic recognition of tailings reservoir with deep learning framework. With the help of improved MSF_SSD and PSPnet algorithm
the semantic segmentation accuracy of the test area for pixel accuracy
mean IoU
F1 score
and mean F1 score is 0.98
0.97
0.99
0.98
respectively. A comprehensive analysis of tailings reservoir dam-break range and possible damage to land surface types are performed with the help of hydrological analysis method and random forest classification results. Outcomes of this research can be used to analyze the impact area caused by dam-break
promote the capabilities of risk management and emergency response of tailings reservoir
and provide fundamental theories for decisions making in relevant departments.
尾矿库SSD多尺度融合PSPnet深度网络Arc Hydro模型风险分析
tailings reservoirSSD multi-scale fusionPSPnet deep networkArc Hydro modelrisk analysis
Breiman L. 2001. Random forests. Machine Learning, 45(1): 5-32 [DOI: 10.1023/A:1010933404324http://dx.doi.org/10.1023/A:1010933404324]
Buselli G and Lu K L. 2001. Groundwater contamination monitoring with multichannel electrical and electromagnetic methods. Journal of Applied Geophysics, 48(1): 11-23 [DOI: 10.1016/S0926-9851(01)00055-6http://dx.doi.org/10.1016/S0926-9851(01)00055-6]
Chen L and Ames D P. 2006. “Open hydro”-An open source GIS implementation of the arc hydro data model//Science, Ecology, Management, Policy. Eccles Conference Center: [s.n.]
Fang X J. 2013. Environmental Risk Assessment of Metal Tailing Ponds in Daye Based on RS and GIS-a Case Study on Tongshankou Copper Tailing Pond. Wuhan: China University of Geosciences
方雪娟. 2013. 基于RS和GIS的大冶市金属尾矿库环境风险评价研究——以铜山口铜矿尾矿库为例. 武汉: 中国地质大学
Fang X J, Ding L and Zhang Z. 2013. An analysis of distribution characteristics and environmental effect of small tailing ponds in Chengui town, Daye. Remote Sensing for Land and Resources, 25(1): 155-159.
方雪娟, 丁镭, 张志. 2013. 大冶陈贵镇小型尾矿库分布特征及其环境影响分析. 国土资源遥感, 25(1): 155-159 [DOI: 10.6046/gtzyyg.2013.01.27http://dx.doi.org/10.6046/gtzyyg.2013.01.27]
Gao Y Z, Chu Y and Liang W. 2015. Remote sensing monitoring and analysis of tailings ponds in the ore concentration area of Heilongjiang Province. Remote Sensing for Land and Resources, 27(1): 160-163
高永志, 初禹, 梁伟. 2015. 黑龙江省矿集区尾矿库遥感监测与分析. 国土资源遥感, 27(1): 160-163 [DOI: 10.6046/gtzyyg.2015.01.25http://dx.doi.org/10.6046/gtzyyg.2015.01.25]
Gislason P O, Benediktsson J A and Sveinsson J R. 2006. Random Forests for land cover classification. Pattern Recognition Letters, 27(4): 294-300 [DOI: 10.1016/j.patrec.2005.08.011http://dx.doi.org/10.1016/j.patrec.2005.08.011]
Jia H J, Wang L J, Ma G C, Tang Y and Jin X. 2017. Study on safety production red line of tailing pond. Industrial Safety and Environmental Protection, 43(10): 55-58
贾虎军, 王立娟, 马国超, 唐尧, 靳晓. 2017. 尾矿库安全生产红线划定研究. 工业安全与环保, 43(10): 55-58 [DOI: 10.3969/j.issn.1001-425X.2017.10.014http://dx.doi.org/10.3969/j.issn.1001-425X.2017.10.014]
Jing R, Gong Z N, Zhu W D, Guan H L, Zhao W J and Zhang T. 2020. Extraction of buildings from remote sensing imagery based on multi-scale SLIC-GMRF and FCNSVM. Journal of Remote Sensing, 24(1): 11-26
井然, 宫兆宁, 朱文定, 关鸿亮, 赵文吉, 张涛. 2020. 多尺度SLIC-GMRF与FCNSVM联合的高分影像建筑物提取. 遥感学报, 24(1): 11-26
Li C K, Zhang D C, Tao J J and Wang F. 2012. Information extraction and its application of urban road networks based on Google images. Remote Sensing Technology and Application, 27(1): 100-105
李朝奎, 张多才, 陶建军, 王芳. 2012. 基于Google影像的城市道路网提取及其应用. 遥感技术与应用, 27(1): 100-105
Li S Y, Chen J F, Lin M Z and Wang F. 2016. Exploration on the teaching reform of “GIS spatial analysis principles and methods”. The Science Education Article Collects, (1): 60-61, 64
李少英, 陈健飞, 林媚珍, 王芳. 2016. “GIS空间分析原理与方法”课程教学改革探索. 科教文汇, (1): 60-61, 64 [DOI: 10.3969/j.issn.1672-7894.2016.03.030http://dx.doi.org/10.3969/j.issn.1672-7894.2016.03.030]
Li X S, Ji C C, Zeng Y, Yan N N and Wu B F. 2009. Dynamics of water and soil loss based on remote sensing and GIS: a case study in Chicheng country of Hebei province. Chinese Journal of Ecology, 28(9): 1723-1729
李晓松, 姬翠翠, 曾源, 闫娜娜, 吴炳方. 2009. 基于遥感和GIS的水土流失动态监测——以河北省赤城县为例. 生态学杂志, 28(9): 1723-1729
Liu J F, Zhang X N, Geng Q Z and Tang Z W. 2006. Method for generation of river and lake buffer regions based on DEM. Journal of Hohai University (Natural Sciences), 34(1): 25-27
刘建芬, 张行南, 耿庆斋, 唐增文. 2006. 基于DEM的河网缓冲区生成方法研究. 河海大学学报(自然科学版), 34(1): 25-27 [DOI: 10.3321/j.issn:1000-1980.2006.01.007http://dx.doi.org/10.3321/j.issn:1000-1980.2006.01.007]
Luo J L, Niu Y L and Sun H G. 2006. Safety assessment application of circular arc Swedish method in tailing pond. Journal of Safety Science and Technology, 2(3): 84-87
罗建林, 牛跃林, 孙浩刚. 2006. 圆弧条分法在尾矿库安全评价中的应用. 中国安全生产科学技术, 2(3): 84-87 [DOI: 10.3969/j.issn.1673-193X.2006.03.019http://dx.doi.org/10.3969/j.issn.1673-193X.2006.03.019]
Lv J. 2014. Remote Sensing Research and Application of Tailings Pond-A Case Study of Tailings Pond in Hebei Province. Beijing: China University of Geosciences (Beijing).
吕杰. 2014. 尾矿库遥感监测技术研究与应用——以河北省尾矿库为例. 北京: 中国地质大学(北京).
Shakesby R A and Whitlow J R. 1991. Failure of a mine waste dump in Zimbabwe: causes and consequences. Environmental Geology and Water Sciences, 18(2): 143-153 [DOI: 10.1007/BF01704668http://dx.doi.org/10.1007/BF01704668]
Shelhamer E, Long J and Darrell T. 2017. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4): 640-651 [DOI: 10.1109/TPAMI.2016.2572683http://dx.doi.org/10.1109/TPAMI.2016.2572683]
Wei Y, Xu K L, Zheng X and Guo L J. 2010. The characteristics and problems of tailing reservoir in China//Proceedings of 2010 (Shenyang) International Colloquium on Safety Science and Technology. Shenyang: Northeastern University: 512-515
魏勇, 许开立, 郑欣, 郭利杰. 2010. 我国尾矿库现状特点以及问题//2010(沈阳)国际安全科学与技术学术研讨会论文集. 沈阳: 东北大学: 512-515
Wu F Y, Wang X, Ding J W, Du P J and Tan K. 2020. Improved cascade forest deep learning model for hyperspectral imagery classification. Journal of Remote Sensing, 24(4): 439-453
武复宇, 王雪, 丁建伟, 杜培军, 谭琨. 2020. 高光谱遥感影像多级联森林深度网络分类算法. 遥感学报, 24(4): 439-453 [DOI: 10.11834/jrs.20209190http://dx.doi.org/10.11834/jrs.20209190]
Wu W W, Gong Y T and Qin X S. 2011. Analysis on existing problems of small tailings pond and safety precautions. Nonferrous Metals (Mining Section), 63(5): 49-51
武伟伟, 龚宇同, 秦秀山. 2011. 小型尾矿库存在的问题及安全对策分析. 有色金属(矿山部分), 63(5): 49-51 [DOI: 10.3969/j.issn.1671-4172.2011.05.013http://dx.doi.org/10.3969/j.issn.1671-4172.2011.05.013]
Yang X H, Huang J, Tian L, Liu Z and Han L. 2015. A discussion on comprehensive governance of mine environment based on high resolution remote sensing data: a case of Maoniuping REE deposit, Mianning County. Remote Sensing for Land and Resources, 27(4): 115-121
杨显华, 黄洁, 田立, 刘智, 韩磊. 2015. 基于高分辨率遥感数据的矿山环境综合治理研究——以冕宁牦牛坪稀土矿为例. 国土资源遥感, 27(4): 115-121 [DOI: 10.6046/gtzyyg.2015.04.18http://dx.doi.org/10.6046/gtzyyg.2015.04.18]
Yu Y D, Lin G L, Chen B F, Tang X X and Zhang J L. 2014. Application of GNSS technology in deformation monitoring of tailings dam body. Bulletin of Surveying and Mapping, (S1): 60-62
於永东, 林国利, 陈炳富, 汤向行, 张家乐. 2014. GNSS技术在尾矿库坝体变形监测中的应用. 测绘通报, (S1): 60-62 [DOI: 10.13474/j.cnki.11-2246.2014.0656]
Zhang D J, Lei X J and Zhang X J. 2003. Stability analysis and treatment research of Longxingzhai impounding dam. Rock and Soil Mechanics, 24(4): 670-672
张电吉, 雷向进, 张兴唐. 2003. 龙形寨尾砂坝稳定性评价与综合治理研究. 岩土力学, 24(4): 670-672 [DOI: 10.3969/j.issn.1000-7598.2003.04.040http://dx.doi.org/10.3969/j.issn.1000-7598.2003.04.040]
Zhang H. 2011. GIS-Based Risk Analysis on Tailings Dam Failures. Tianjin: Nankai University
张会. 2011. 基于GIS技术的尾矿库溃坝事故风险分析. 天津: 南开大学 [DOI: 10.7666/d.y2002623http://dx.doi.org/10.7666/d.y2002623]
Zhang X N, Qi J and Zhang L. 2000. Method of deriving drainage network. Journal of Hohai University, 28(1): 26-31
张行南, 齐晶, 张丽. 2020. 流域流水网推导方法. 河海大学学报, 28(1): 26-31 [DOI: 10.3321/j.issn:1000-1980.2000.01.006http://dx.doi.org/10.3321/j.issn:1000-1980.2000.01.006]
Zhao H S, Shi J P, Qi X J, Wang X G and Jia J Y. 2017. Pyramid scene parsing network//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE: 2881-2890 [DOI: 10.1109/CVPR.2017.660http://dx.doi.org/10.1109/CVPR.2017.660]
Zhao L L and Xiao R L. 2013. Environmental risk assessment based on RS and GIS: a case study of Wengfu tailing pond. Geomatics World, 20(4): 70-77
赵林林, 肖如林. 2013. 基于RS/GIS环境风险评价——以瓮福尾矿库为例. 地理信息世界, 20(4): 70-77
Zhao Q B. 2018. Research on Improved Algorithm of Object Detection Based on SSD. Nanning: Guangxi University
赵庆北. 2018. 改进的SSD的目标检测研究. 南宁: 广西大学
Zhu S R and Wu H Y. 2006. Introduction of ERSI arc hydro data model. Geomatics and Spatial Information Technology, 29(5): 87-90
朱思蓉, 吴华意. 2006. Arc Hydro水文数据模型. 测绘与空间地理信息, 29(5): 87-90 [DOI: 10.3969/j.issn.1672-5867.2006.05.025http://dx.doi.org/10.3969/j.issn.1672-5867.2006.05.025]
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