以武深高速广东段沿线的粉状煤系土为研究对象,利用环境扫描电镜技术(ESEM)获得了不同含水率直剪试验后粉状煤系土剪切面的微观结构SEM图像;结合MATLAB及Image Pro Plus(IPP)软件,对剪切面微观结构特征进行了分析;基于分形理论,建立了...以武深高速广东段沿线的粉状煤系土为研究对象,利用环境扫描电镜技术(ESEM)获得了不同含水率直剪试验后粉状煤系土剪切面的微观结构SEM图像;结合MATLAB及Image Pro Plus(IPP)软件,对剪切面微观结构特征进行了分析;基于分形理论,建立了煤系土的分形模型,求出了二维空间内煤系土孔隙轮廓分维数、孔隙数量~孔径分布分维数。结果表明:煤系土微观结构多为片状颗粒集合体,接触关系主要为面-面接触和面-边接触;随着含水率的增加,剪切面粗糙度先增加后减小,力学强度参数先增大后减小,转折点在最优含水率附近(10%~15%之间);煤系土微观结构具有明显的分形特征,可用孔隙等效面积-等效周长分形模型、孔隙数量~孔径分布分形模型描述,其分维数介于1~2。展开更多
Landfill leaks pose a serious threat to environmental health,risking the contamination of both groundwater and soil resources.Accurate investigation of these sites is essential for implementing effective prevention an...Landfill leaks pose a serious threat to environmental health,risking the contamination of both groundwater and soil resources.Accurate investigation of these sites is essential for implementing effective prevention and control measures.The self-potential(SP)stands out for its sensitivity to contamination plumes,offering a solution for monitoring and detecting the movement and seepage of subsurface pollutants.However,traditional SP inversion techniques heavily rely on precise subsurface resistivity information.In this study,we propose the Attention U-Net deep learning network for rapid SP inversion.By incorporating an attention mechanism,this algorithm effectively learns the relationship between array-style SP data and the location and extent of subsurface contaminated sources.We designed a synthetic landfill model with a heterogeneous resistivity structure to assess the performance of Attention U-Net deep learning network.Additionally,we conducted further validation using a laboratory model to assess its practical applicability.The results demonstrate that the algorithm is not solely dependent on resistivity information,enabling effective locating of the source distribution,even in models with intricate subsurface structures.Our work provides a promising tool for SP data processing,enhancing the applicability of this method in the field of near-subsurface environmental monitoring.展开更多
文摘以武深高速广东段沿线的粉状煤系土为研究对象,利用环境扫描电镜技术(ESEM)获得了不同含水率直剪试验后粉状煤系土剪切面的微观结构SEM图像;结合MATLAB及Image Pro Plus(IPP)软件,对剪切面微观结构特征进行了分析;基于分形理论,建立了煤系土的分形模型,求出了二维空间内煤系土孔隙轮廓分维数、孔隙数量~孔径分布分维数。结果表明:煤系土微观结构多为片状颗粒集合体,接触关系主要为面-面接触和面-边接触;随着含水率的增加,剪切面粗糙度先增加后减小,力学强度参数先增大后减小,转折点在最优含水率附近(10%~15%之间);煤系土微观结构具有明显的分形特征,可用孔隙等效面积-等效周长分形模型、孔隙数量~孔径分布分形模型描述,其分维数介于1~2。
基金Projects(42174170,41874145,72088101)supported by the National Natural Science Foundation of ChinaProject(CX20200228)supported by the Hunan Provincial Innovation Foundation for Postgraduate,China。
文摘Landfill leaks pose a serious threat to environmental health,risking the contamination of both groundwater and soil resources.Accurate investigation of these sites is essential for implementing effective prevention and control measures.The self-potential(SP)stands out for its sensitivity to contamination plumes,offering a solution for monitoring and detecting the movement and seepage of subsurface pollutants.However,traditional SP inversion techniques heavily rely on precise subsurface resistivity information.In this study,we propose the Attention U-Net deep learning network for rapid SP inversion.By incorporating an attention mechanism,this algorithm effectively learns the relationship between array-style SP data and the location and extent of subsurface contaminated sources.We designed a synthetic landfill model with a heterogeneous resistivity structure to assess the performance of Attention U-Net deep learning network.Additionally,we conducted further validation using a laboratory model to assess its practical applicability.The results demonstrate that the algorithm is not solely dependent on resistivity information,enabling effective locating of the source distribution,even in models with intricate subsurface structures.Our work provides a promising tool for SP data processing,enhancing the applicability of this method in the field of near-subsurface environmental monitoring.
