球壳形磁性层磁异常正反演方法

为了适应卫星磁测资料解释的需要,作者在磁性界面和磁性层磁异常正反演方法研究的基础上,首次提出了球壳形磁性层磁异常正反演理论和方法。 作者首先阐述了垂直磁化球壳型磁性层模型,导出了场垂直分量Z_(λο)的球谐系数正演公式。然后,给出了磁性层上,下层面埋深变化值与磁化强度变化值的球谐系数反演公式。进而导出了迭代反演收敛条件。本文的磁化强度反演方法不同于目前国内外已有的方法,其反演对象是磁化强度变化值△J_(λθ),磁化强度平均值J是必须给定的定解条件。这种反演方法能给出尽可能满足人们解释要求的解,克服了前人反演方法总存在负值的困难。 为了增加球壳形磁性层反演方法的实用性,作者还在类似平面频率城反演方法所作假设的前提下,把这种利用球谐分析理论在全球面导出的方法,严格地推广应用于局部区域。这也是球谐分析理论在局部区域应用的一个实例,在理沦上具有一定的意义。     

广西北海银滩侵蚀动力研究

广西北海银滩是一处典型的强潮海滩,以滩面宽阔、砂质纯白为特征,为当地的一处著名旅游景点。近年来银滩也出现了岸线后退、滩肩消失、沉积粗化等侵蚀迹象。为了研究银滩侵蚀的动力机制,本文对银滩海水浴场当前的滩面高程、坡度以及滩面后退距离特征进行了调查和分析。并基于2016-2018年间的滩面高程动态变化,分析了冬、春季风浪和台风期间水位、波浪特征与滩面侵蚀之间的关系,并进而探讨了最有可能造成银滩侵蚀的台风路径。研究表明,1985年以来的滩面最大后退距离为80 m,侵蚀造成海滩上部滩面坡度降低,使得海滩上部的剖面形态向上凸状转化。冬、春季节的风浪叠加高水位可能会造成一定程度的滩面侵蚀,并在海滩上部形成脊槽地貌,但是这种侵蚀会被夏季西南向涌浪导致的回淤所恢复。造成银滩侵蚀后退的主要原因是2003年以来风暴潮重现频率增加。发源于西太平洋,经海南岛北部或雷州半岛进入北部湾的台风可以导致显著的海岸增水,最易于引起强烈的海滩侵蚀事件。银滩东侧的渔港码头阻断了自东向西的泥沙沿岸净输运,也是加剧银滩侵蚀的重要因素。     

Three-dimensional quantitative analysis on granular particle shape using convolutional neural network

To identify all desired shape parameters of granular particles with less computational cost, this study proposes a three-dimensional convolutional neural network (3D-CNN) based model. Datasets are made of 100 ballast and 100 Fujian sand particles, and the shape parameters (i.e., aspect ratio, roundness, sphericity, and convexity) obtained by conventional methods are used to label all particles. For the model training, by feeding the slice images of particles into the model, the contour of particles is automatically extracted, thereby the shape parameters can be learned by the model. Thereafter, the model is applied to predict shape parameters of new particles as model testing. All results indicate the model trained based on slice images cut from three orthogonal planes presents the highest prediction accuracy with an error of less than 10%. Meanwhile, the accuracy for concave and angular particles can be guaranteed. The rotation-equivariant of the model is confirmed, in which the predicted values of shape parameters are roughly independent of orientations of the particle when cutting slice images. Superior to conventional methods, all desirable shape parameters can be obtained by one unified 3D-CNN model and its prediction is independent of particle complexity and the number of triangular facets, thus saving computation cost.