Effects of strong ground motion on the susceptibility of gully type debris flows

Typhoon Herb in 1996 caused widespread debris flows in central Taiwan. The 7.3 Chi-Chi earthquake on September 21, 1999, which also took place in central Taiwan, induced many landslides in the region. These landslides turned into debris flows when Typhoon Toraji struck Taiwan in 2001. This research selects three regions which suffered a ground motion class of 5, 6, and 7 on the Richter scale during the Chi-Chi earthquake as study areas. Air photos from 1997 and 2001 of these regions are used to map the gully-type debris flows that took place after Typhoons Herb and Toraji, respectively. The gullies adjacent to the debris flow, but without a trace of debris flows, are also mapped as the non-debris flow data. The topography, hydrogeology, and rainfall factors – where debris flow occurred and in which there was no occurrence of debris flows in these gullies were retrieved from DTM, geological maps, and iso-countour maps, and of rainfall through GIS processing. These characteristic are introduced into a probabilistic neural network to build a predicting model for the probability of the occurrence of debris flows. Three series of cross analyses are conducted to compare the probability of the occurrence of debris flows of the same dataset predicted by different prediction models. The results reveal that the susceptibility of debris flows was elevated after the Chi-Chi earthquake struck. The upsurge of susceptibility was more obvious for the regions that received a higher class of ground motion.     

网络分析方法在GPS控制网优化设计中的应用

讨论了网络分析方法中的深度优先遍历算法在GPS控制网优化设计中资源配置问题中的应用,并在该算法的基础上,进行了一定的改进,提出了并行项目的解决建议,为网络的资源配置提出了一种新的解决方法。     

Present rate of uplift in Fennoscandia from GRACE and absolute gravimetry

Fennoscandia is a key region for studying effects of glacial isostatic adjustment. The associated mass variations can be detected by the Gravity Recovery and Climate Experiment (GRACE) satellite mission, which observes the Earth's gravity field since April 2002, as well as by absolute gravimetry field campaigns. Since 2003, annual absolute gravity (AG) measurements have been performed in Fennoscandia by the Institut für Erdmessung (IfE, Institute of Geodesy) of the Leibniz Universität Hannover, Germany, within a multi-national cooperation. This offers a unique opportunity for validation and evaluation of the GRACE results. In this preliminary study, the GRACE results are compared to secular gravity changes based on the surveys from 2004 to 2007 with the FG5-220 gravimeter of the IfE.The results from GRACE monthly solutions provided by different analysis centres show temporal gravity variations in Fennoscandia. The included secular variations are in good agreement with former studies. The uplift centre is located west of the Bothnian Bay, the whole uplift area comprises Northern Europe. Nevertheless, the differences between the GRACE solutions are larger than expected and the different centre-specific processing techniques have a very strong effect on possible interpretations of GRACE results. The comparison of GRACE to the AG measurements reveals that the determined trends fit well with results from GRACE at selected stations, especially for the solution provided by the GFZ. Variations of land hydrology clearly influence results from GRACE and the AG measurements.     

General seismic wave and phase detection software driven by deep learning

We developed an automatic seismic wave and phase detection software based on PhaseNet, an efficient and highly generalized deep learning neural network for P- and S-wave phase picking. The software organically combines multiple modules including application terminal interface, docker container, data visualization, SSH protocol data transmission and other auxiliary modules. Characterized by a series of technologically powerful functions, the software is highly convenient for all users. To obtain the P- and S-wave picks, one only needs to prepare three-component seismic data as input and customize some parameters in the interface. In particular, the software can automatically identify complex waveforms (i.e. continuous or truncated waves) and support multiple types of input data such as SAC, MSEED, NumPy array, etc. A test on the dataset of the Wenchuan aftershocks shows the generalization ability and detection accuracy of the software. The software is expected to increase the efficiency and subjectivity in the manual processing of large amounts of seismic data, thereby providing convenience to regional network monitoring staffs and researchers in the study of Earth's interior.     

青藏高原河流网络高分CubeSat遥感监测

河流网络是地表水循环的重要组成部分,如何实现河流网络动态监测已成为河流遥感研究的热点。近年来,以PlanetScope为代表的CubeSat小卫星已具备了米级空间分辨率、1 d重访周期的优势,这为河流网络高时空分辨率动态监测提供了可能。本文以青藏高原长江源区的通天河流域（227 km²）为研究区,选取2017-05—2017-10 5期3 m空间分辨率CubeSat遥感影像,增强河流横纵剖面特征自动化提取了河流网络,研究了通天河流域河流网络动态变化,对比分析了3 m CubeSat与30 m Landsat 8、10 m Sentinel-2所提取的河流网络,以及5种现有水体数据集（GRWL,GSW,FROM-GLC 2017,OpenStreetMap,HydroSHEDS）。研究结果表明：（1）研究区内河流网络5月水系密度较低（0.38 km^-1）,7—8月河流网络进入丰水期,水系密度显著增加至0.61 km^-1,9月河流网络进入平水期,水系密度趋于平稳（0.53 km^-1）,随后迅速退化并于10月开始冻结,水系密度迅速降低至0.37 km^-1;（2）采用高空间分辨率CubeSat所提取的河流网络能够识别更多细小河流（河宽3—30 m）,CubeSat所提取的河流总长分别为Landsat 8、Sentinel-2所提取河流总长的1.6倍和1.3倍;（3）CubeSat所提取的河流网络水系密度高于现有水体数据集（2.9—12.4倍）,弥补了现有水体数据集无法反映细小河流的不足。     

联合卷积神经网络与集成学习的遥感影像场景分类

针对人工设计的中、低层特征难以实现复杂场景影像的高精度分类以及卷积神经网络依赖大量训练数据等问题，结合迁移学习与集成学习，提出了一种联合卷积神经网络与集成学习的遥感影像场景分类算法。首先基于迁移学习的思想，利用在自然影像数据集上训练好的多个深层卷积神经网络模型作为特征提取器，提取图像多个高度抽象的语义特征；然后构建由Logistic回归和支持向量机组成的Stacking集成模型，对同一图像的多个特征分别训练Logistic模型，将预测概率结果融合构建概率特征；最后利用支持向量机对概率特征训练和预测，得到场景影像的分类结果。利用UCMerced_LandUse和NWPU-RESISC 45两种不同规模的遥感影像数据集进行试验，即使在只有10%的数据作为训练样本情况下，本文方法能够分别达到90.74%和87.21%的分类精度。     

POS辅助光束法区域网平差

介绍POS数据与摄影测量观测值联合平差的基本原理,推导了平差基础误差方程,并利用自行研制的POS辅助光束法区域网平差系统WuCAPS对摄自两个平坦地区的1:2 500和1:60 000比例尺两种航摄影像进行了试验.结果表明,利用POS系统测定的未检校与经检校场检校后的影像定向参数辅助光束法区域网平差的结果基本是一致的.此时,仅需在区域四角各布设1个平高地面控制点就能满足航空摄影测量加密的精度要求,这不但减少摄影测量加密野外像片控制点的联测工作量,而且避免了使用POS系统时必须设置专门检校场的繁琐检校,可以明显提高航空摄影效率,节省POS使用成本,有利于POS系统的大规模推广应用.     

地表下沉系数选取的LWPSO-BP方法研究

针对BP神经网络自身收敛速度慢、容易陷入局部极小点的缺点,本文以线性下降惯性权重粒子群优化算法(LLWPSO)为前处理器,优化BP网络的权值和阈值,利用实测资料数据,建立LWPSO-BP的地表下沉系数预计模型,并同普通BP模型预计结果对比结果表明:LWPSO-BP神经网络不仅训练速度快,而且预测精度明显提高,该模型对地...     

道路网增量更新中拓扑冲突的检测方法

为提高空间数据增量更新中拓扑冲突的检测效率,针对道路网数据,首先分析了增量要素进行更新时可能产生的拓扑冲突的类型和特点,运用规则格网进行邻近区域的表达;然后使用5元组模型描述增量要素与邻近区域要素间的拓扑关系,与设定的拓扑冲突表达进行比较,判断是否存在拓扑冲突。实验结果表明,本方法对于道路网数据增量更新中的拓扑冲突的类型区分准确全面,检测效率较高,具有很好的实用性和可靠性。