Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN)

An efficient and accurate method of generating landslide susceptibility maps is very important to mitigate the loss of properties and lives caused by this type of geological hazard. This study focuses on the development of an accurate and efficient method of data integration, processing and generation of a landslide susceptibility map using an ANN and data from ASTER images. The method contains two major phases. The first phase is the data integration and analysis, and the second is the Artificial Neural Network training and mapping. The data integration and analysis phase involve GIS based statistical analysis relating landslide occurrence to geological and DEM (digital elevation model) derived geomorphological parameters. The parameters include slope, aspect, elevation, geology, density of geological boundaries and distance to the boundaries. This phase determines the geological and geomorphological factors that are significantly correlated with landslide occurrence. The second phase further relates the landslide susceptibility index to the important geological and geomorphological parameters identified in the first phase through ANN training. The trained ANN is then used to generate a landslide susceptibility map. Landslide data from the 2004 Niigata earthquake and a DEM derived from ASTER images were used. The area provided enough landslide data to check the efficiency and accuracy of the developed method. Based on the initial results of the experiment, the developed method is more than 90% accurate in determining the probability of landslide occurrence in a particular area.     

双轴倾角传感器在钻孔测斜仪中测量算法的校正

将芬兰VTI公司生产的SCA100T双轴倾角传感器应用于钻孔测斜仪,其体积小,能提高倾角测量的稳定性和精度。双轴倾角传感器测量角度会引起算法校正问题,采用测得的标定数据得出倾角值的回归模型,测量倾角的实验结果和推算结果存在一定的误差,通过应用误差补偿方法来减小实际测量误差。理论分析和仿真计算表明,采用此法补偿后得到的均方差最大为0.15258,而没有补偿的结果最大均方差为4.81104。     

Seismicity re-evaluation of the northern Red Sea

The Aqaba subnetwork of five vertical short-period stations of the seismological observatory of King Saud University was installed in late 1986 along the eastern side of the Gulf of Aqaba, northern Red Sea. During the first six years (1986 August to 1992 July) of the subnetwork operation, 400 microearthquakes were detected. Of these, 93 events were recorded by most of the subnet stations and were located. Their epicentres lie in the northern part of the Red Sea between latitudes 25.5 N and 27.5 N and longitudes 33.5 E and 36 E along the axial depression of the Red Sea where the large intrusions (deeps) are located. Magnitudes of the locatable events ranged from 2.1 to 4.8. Two intensive swarms of about 200 microearthquakes occurred in February and June of 1992. The February swarm is the first intensive sequence observed in the surveying area since the establishment of the KSU network. Frequency-magnitude analysis of the recorded events for the period 1986-1992 yielded 3.543 for a and 0.658 for b . These relatively higher b values (0.658) are a good indication of the crustal heterogeneity under the spreading zone of the northern Red Sea. USGS and KSU data together show 3.41 for a and 0.49 for b . This study, together with historical data, confirms that the area is very seismically active and that the activity is mainly of swarm type, and may be attributed to the subsurface magmatic activity and spreading centres that are usually associated with strike-slip and normal faulting, respectively.     

2020年古冶MS5.1地震序列微震检测及活动性研究

2020年7月12日河北省古冶(39.78°N, 118.44°E)发生M_S5.1地震, 震源深度10 km.本文通过对其周边500 km范围内的84个固定台站连续观测地震数据, 采用基于图形处理器的模板匹配定位识别技术(GPU-M&L)对地震前后(2020年6月1日至2020年8月31日)共三个月的连续数据进行了微震检测和地震活动性分析.我们首先从台网目录的362个古冶地震序列事件中挑选了信噪比较高的118个地震作为模板, 通过模板扫描和检测, 获得共1017个地震, 约为台网目录的2.8倍, 然后运用双差定位方法(hypoDD)对669个地震事件进行了重定位.最后我们对古冶地震的事件序列进行时空分布、震源机制解以及b值空间分布等分析.结果显示古冶M_S5.1地震前, 震中区周边地震活动性显著, 地震前后地震序列分布优势方向为NE向, 震源深度方向近直立分布; 发震断裂为唐山—古冶断裂或其延伸部分, 与1976年唐山M_S7.8地震同为唐山断裂带, 发震断层走向基本一致.据此, 我们推测古冶M_S5.1地震是1976年唐山M_S7.8地震后余震区应力调整所触发.

     

电离层等离子体漂移的振荡

根据中纬Ｗａｋｋａｎａｉ站与低纬Ｏｋｉｎａｗａ站的垂测数据,得出电离层峰区垂直漂移的频谱结构．结果表明,漂移除具有周期在２４ｈ之内的潮汐分量外,低纬站还呈现出值得特别注意的２．１ｄ的周期振荡．作者认为,低纬垂直漂移的振荡行为,主要是电场的波动分量引起;这种波动电场包含潮汐振荡与行星波两日振荡的成分．峰值电子浓度的振荡最直接地是由中性大气温度控制;而峰高的波动起伏,则主要是电场振荡的结果．     

小良试验站三种植被类型地表径流效应的对比研究

本文分析研究了３种植被类型下的地表径流在汇流、洪峰及径流过程上的特征，差异是巨大的。在径流过程上桉树林和裸地的变地剧烈，几乎没有什么调蓄作用，洪峰流量占一次性降水径流量的绝大部分，涨水和退水流量都较小。本文应用灰色系统ＧＭ（２，１）模型，成功地预测了以年和以月为时间尺度的地表径流序列，其预测模型可以用来对这些时间尺度的地表径流进行预报。     

雍家沟泥石流活动特征与堵江

自2008年汶川地震以来,四川省绵竹市清平乡雍家沟已暴发过多场泥石流,造成了严重的堵断公路和堵塞河流事件,其中,2012年“8·18”暴发的泥石流规模最大,造成绵远河全部堵塞.通过分析2010年“8·13”与2012年“8· 18”两场泥石流的降雨过程发现,雍家沟暴发泥石流的前期累积雨量在增加,激发雨强在降低.2008年汶川地震导致雍家沟的主沟和支沟内堆积了大量的松散固体物质.野外调查发现,雍家沟多次暴发泥石流的活动路径并不一致,其中,2012年“8· 18”泥石流主要沿1号支沟活动,沟道的侵蚀宽度随着与主沟沟口距离的增加而变窄,沟道内剩余堆积体厚度随着距离的增加呈现先增后减的规律,且沟道内的物质粒径分布具有随机性.对雍家沟在不同暴雨频率下暴发泥石流可能引起的堵江情况进行了分析,结果表明,在暴雨频率大于20％的情况下暴发泥石流便可造成堵江事件,甚至可将主河全部堵塞.     

地球磁场对太阳风的加载响应与地震

地磁预报地震的方法越来越多,本文应用加卸载响应比方法,分析山西地区2009—2016年观测的加卸载响应比与山西地区4.0级地震之间的对应关系,初步给出适用于山西地区震例特征的磁异常判别指标。