欧洲大陆岩石圈动力学研究现状与进展

通过对ＥＵＲＯＰＲＯＢＥ计划中５个关键项目的主要研究目标和主要成果的介绍,简述了欧洲大陆岩石圈动力学研究现状及进展。通过研究,人们已以轾芬诺斯堪的亚地盾古元古代、太古代岩石圈演化特征有明显不同;横跨欧洲的缝合带既是前寒武纪地壳显一宙重新活动的结果,也是加里东和华力西地体增生作用的结果,乌拉尔造山带是古生代微大陆碎块进一步裂解、崩解、增生到东欧在陆边缘的结果;侏罗纪到现代的非洲－－阿拉伯板块及其间的     

Chemical mass balance of calcrete genesis on the Toledo granite (Spain)

The chemical mass balance of calcrete genesis is studied on a typical sequence developed in granite, in the Toledo mountains, Central Spain.

Field evidence and petrographic observations indicate that the texture and the bulk volume of the parent rock are strictly preserved all along the studied calcrete profile.

Microscopic observations indicate that the calcitization process starts within the saprolite, superimposed on the usual mechanisms of granite weathering: the fresh rock is first weathered to secondary clays, mainly smectites, which are then pseudomorphically replaced by calcite. Based on this evidence, chemical mass transfers are calculated, assuming iso-volume transformation from the parent rock to the calcrete.

The mass balance results show the increasing loss of matter due to weathering of the primary phases, from the saprolite towards the calcrete layers higher in the sequence. Zr, Ti or Th, which are classically considered as immobile during weathering, are also depleted along the profile, especially in the calcrete layer. This results from the prevailing highly alkaline conditions, which could account for the simultaneous precipitation of CaCO₃ and silicate dissolution.

The calculated budget suggests that the elements exported from the weathering profile are provided dominantly by the weathering of plagioclase and biotite. We calculate that 8–42% of the original Ca remains in granitic relics, while only 15% of the authigenic Ca released by weathering is reincorporated in the calcite. This suggests that 373 kg/m² of calcium (i.e., three times the original amount) is imported into the calcrete from allochtonous sources, probably due to aeolian transport from distant limestone formations.     

建国以来中国洪涝灾害成灾面积变化的小波分析

文章以小波诊断技术为基础,对建国以来中国洪涝灾害成灾面积变化进行了多时间尺度分析。研究表明,建国以来中国洪涝灾害成灾面积变化具有明显的3年左右、9年左右和17年左右的特征时间尺度和相应的周期性变化特征;并且特征时间尺度近年来逐渐缩短,即成灾面积的变化渐趋频繁。根据这几个特征时间尺度上小波系数的演变趋势发现,在未来几年内,中国洪涝成灾面积呈现下降的总体趋势。根据小波方差分析发现中国洪涝灾害成灾面积变化具有显著的3年、9年和17年的主要周期,并以9年和17年的周期尤为显著;同时,根据功率谱的比较分析同样发现,中国洪涝灾害成灾面积同样存在着3年左右、7~9年和17年的主要周期。     

Effects of social and climatic factors on building activity in the Czech lands between 1450 and 1950: a dendrochronological analysis

The development of settlement and building activity is the result of socioeconomic, political and demographic changes in the past. However, accurate information on temporal variation in building activity is rather limited. Dendrochronological databases containing dated historical wooden constructions provide an important resource. We used 6514 tree-felling dates to reconstruct building activity in the Czech lands for the period 1450–1950. Comparing felling dates with historical events demonstrated that building activity was negatively associated with intense wars, particularly during the Thirty Years' War (1618–1648). After the Peace of Westphalia (1648), socioeconomic renewal and demographic growth were reflected in an upsurge of building activity, especially ecclesiastical buildings. While the construction of ecclesiastical and noble buildings culminated around the 1720s, rural buildings peaked in the 1780s and the 1820s. Although no direct effect of climate was demonstrated, adverse climatic conditions leading to harvest failures and subsequent famines (e.g. the ‘Hunger Years’ 1770–1772) significantly contributed to declines in building activity. In contrast, a higher number of felling dates were detected when strong and/or frequent windstorms occurred. This study provides a comprehensive understanding of building activity in Central Europe and advocates the use of dendrochronological databases for the investigation of human activities in history.     

Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.