The influence of slope angle on soil entrainment by sheetwash and rainsplash

Although a number of studies of the variation of soil transport with increase in slope angle have appeared, few include an information on the interaction of sheetwash and rainsplash on high slope angles, which is necessary to test Horton's proposed polynomial relationship. Virtually no studies are available which compare the influence of changes in soil type or antecedent moisture on established relationships. This paper reports the testing of eight soils from Alberta, Canada, under simulated rainfall on ten slopes from 3° to 30°. Material eroded was separated into that transported by rainsplash and that by sheetwash. In general, it was found that the influence of changes in slope angle on soil transport is best described by polynomial relationships, but these are shown to vary considerably between rainspash and sheetwash, between different soil types and for different antecedent moisture conditions. Despite careful control of all factors other than slope angle very high variability of results was experienced. Causes of variability are examined and the need for evaluation of the effect of test plot size on variability of results is suggested.     

Modelling local GPS/levelling geoid undulations using artificial neural networks

The use of GPS for establishing height control in an area where levelling data are available can involve the so-called GPS/levelling technique. Modelling of the GPS/levelling geoid undulations has usually been carried out using polynomial surface fitting, least-squares collocation (LSC) and finite-element methods. Artificial neural networks (ANNs) have recently been used for many investigations, and proven to be effective in solving complex problems represented by noisy and missing data. In this study, a feed-forward ANN structure, learning the characteristics of the training data through the back-propagation algorithm, is employed to model the local GPS/levelling geoid surface. The GPS/levelling geoid undulations for Istanbul, Turkey, were estimated from GPS and precise levelling measurements obtained during a field study in the period 1998–99. The results are compared to those produced by two well-known conventional methods, namely polynomial fitting and LSC, in terms of root mean square error (RMSE) that ranged from 3.97 to 5.73 cm. The results show that ANNs can produce results that are comparable to polynomial fitting and LSC. The main advantage of the ANN-based surfaces seems to be the low deviations from the GPS/levelling data surface, which is particularly important for distorted levelling networks.     

利用原有似大地水准面成果多项式拟合GPS/水准高程的精度估计

多项式拟合在GPS高程拟合中最常采用,但是直接拟合高程异常的方法在高程异常变化不平缓的地区精度较差。本文试图在利用已有局部似大地水准面资料的基础上拟合高程异常残差,进而确定局部内小地区的正常高。本文首先阐述了四次多项式拟合的基本原理以及精度评定方法,然后具体算例,实际计算分析了此种拟合方法的精度。同时接合得出的结果,对小地区似大地水准面的二次精化提出了有益的建议。     

基于GPS实测控制点的SPOT 5 1A数据几何校正方法精度比较

采用高精度的星基差分GPS实测控制点对SPOT 5 1A数据几何校正方法进行比较，对校正误差的产生原因进行分析。结果表 明，正射模型法的校正精度远高于多项式法的校正精度;多项式法在X方向上的误差远大于Y方向上的误差；正射模型法的误差在2个 方向上差别不大。卫星扫描角度引起的像元畸变是多项式方法产生较大误差的重要原因。     

基于目标检测的地质异常信息提取——以湖南香花岭地区为例

卷积神经网络在图像识别领域处于领先地位,在目标检测方面的应用也越来越广泛,其特点是可以提取点与点之间的相关关系。二维地质图中,点与点之间往往存在特定的空间关系,将卷积神经网络技术应用于地质异常信息的提取有其重要性。在讨论卷积神经网络以及基于该技术目标检测算法(YOLO)的基础上,以湖南香花岭地区为例,提取与锡矿成矿相关的构造信息并进行分析,结果该方法能够覆盖原有矿点,有效地定义点与点之间的相关关系,描述点与点之间的空间相关性,可靠地提取与成矿有关的构造线密度信息,在花岗岩体与构造复杂地区圈出地质异常信息。     

Unsupervised ore/waste classification on open-cut mine faces using close-range hyperspectral data

The remote mapping of minerals and discrimination of ore and waste on surfaces are important tasks for geological applications such as those in mining. Such tasks have become possible using ground-based, close-range hyperspectral sensors which can remotely measure the reflectance properties of the environment with high spatial and spectral resolution. However, autonomous mapping of mineral spectra measured on an open-cut mine face remains a challenging problem due to the subtleness of differences in spectral absorption features between mineral and rock classes as well as variability in the illumination of the scene. An additional layer of difficulty arises when there is no annotated data available to train a supervised learning algorithm. A pipeline for unsupervised mapping of spectra on a mine face is proposed which draws from several recent advances in the hyperspectral machine learning literature. The proposed pipeline brings together unsupervised and self-supervised algorithms in a unified system to map minerals on a mine face without the need for human-annotated training data. The pipeline is evaluated with a hyperspectral image dataset of an open-cut mine face comprising mineral ore martite and non-mineralised shale. The combined system is shown to produce a superior map to its constituent algorithms, and the consistency of its mapping capability is demonstrated using data acquired at two different times of day.     

利用基于残差多注意力和ACON激活函数的神经网络提取建筑物

针对目前主流深度学习网络模型应用于高空间分辩率遥感影像建筑物提取存在的内部空洞、不连续以及边缘缺失与边界不规则等问题,本文在U-Net模型结构的基础上通过设计新的激活函数（ACON）、集成残差以及通道-空间与十字注意力模块,提出RMAU-Net模型。该模型中的ACON激活函数允许每个神经元自适应地激活或不激活,有利于提高模型的泛化能力和传输性能;残差模块用于拓宽网络深度并降低训练和学习的难度,获取深层次语义特征信息;通道-空间注意力模块用于增强编码段与解码段信息的关联、抑制无关背景区域的影响,提高模型的灵敏度;十字注意力模块聚合交叉路径上所有像素的上下文信息,通过循环操作捕获全局上下文信息,提高像素间的全局相关性。以Massachusetts数据集为样本的建筑物提取实验表明,在所有参与比对的7个模型中,本文提出的RMAU-Net模型交并比与F1分数2项指标最优、查准率和查全率两项指标接近最优, RMA-UNet总体效果优于同类模型。通过逐步添加每个模块来进一步验证各模块的有效性以及本文所提方法的可靠性。     

综合利用多项式拟合和载波相位变化率探测单频GPS周跳

针对单频接收机单点定位的应用特点,提出了一种综合探测周跳的方法。处理对象是星间单差载波相位数据,采用的方法是综合利用载波相位变化率和多项式拟合来探测周跳。介绍了两种方法的数学模型,给出了具体实施步骤。最后用算例验证了方法的可靠性和有效性。     

结合深度学习的高光谱与多源遥感数据融合分类

高光谱数据具有丰富的光谱特征,但是其空间分辨率相对较低.一些遥感数据具有与高光谱数据互补的优势,例如提供更精细的空间信息的高空间分辨率数据和具有高度信息的激光雷达LiDAR(Light Detection and Ranging)数据.通过将高光谱数据与多源遥感数据进行融合,可以弥补高光谱数据空间分辨率相对较低,空间特...     

基于深层卷积神经网络的震级快速估算方法

当前地震预警中的震级估算方法是通过初至几秒地震波的特征参数与震级的经验关系来实现的, 这些特征参数依赖于人的经验和主观判断, 没有充分利用初至地震波中与震级相关的信息, 制约了震级估算效果.对此, 本文利用深层卷积神经网络(Deep Convolutional Neural Networks, CNN)直接从初至地震波中自动提取特征, 实现端到端的震级快速估算.CNN方法以单台站的初至竖向地震波作为主输入, 震中距、震源深度以及V_s30作为辅助输入, 震级作为输出.利用日本和智利的大量地表强震记录对CNN方法进行训练(98257条记录)、验证(31429条记录)和测试(40638条记录), 利用美国和新西兰的强震记录进行泛化性能测试(583条记录), 并与应用最为广泛的峰值位移Pd方法进行对比.结果表明, 当初至地震波时长为3s时, 在4~6.4级范围内, CNN方法估算震级的准确率是Pd方法的1.5倍, 在6.5~9级范围, CNN方法估算震级的准确率是Pd方法的1.2倍; 当初至地震波从3s增加到10s时, CNN方法能够随着地震波时长的增加不断提高估算震级的准确率, 并且始终高于Pd方法, 特别是对于4~6.4级地震, CNN方法在初至3s地震波时估算震级的准确率是Pd方法在初至10s地震波时的1.2倍; 随着地震波时长的增加, CNN方法对于震级饱和问题的改善效果优于Pd方法; CNN方法具有较好的泛化能力, 在训练数据集之外的区域, 比Pd方法估算震级更准确.相比于人为定义的特征参数, CNN方法从初至地震波中自动学习到了与震级更为相关的特征, 这些特征极大地改善了震级估算的准确性和时效性, 可以为地震预警系统提供更快速更准确的震级估算.