Cluster regression model and level fluctuation features of Van Lake, Turkey

Lake water levels change under the influences of natural and/or anthropogenic environmental conditions. Among these influences are the climate change, greenhouse effects and ozone layer depletions which are reflected in the hydrological cycle features over the lake drainage basins. Lake levels are among the most significant hydrological variables that are influenced by different atmospheric and environmental conditions. Consequently, lake level time series in many parts of the world include nonstationarity components such as shifts in the mean value, apparent or hidden periodicities. On the other hand, many lake level modeling techniques have a stationarity assumption. The main purpose of this work is to develop a cluster regression model for dealing with nonstationarity especially in the form of shifting means. The basis of this model is the combination of transition probability and classical regression technique. Both parts of the model are applied to monthly level fluctuations of Lake Van in eastern Turkey. It is observed that the cluster regression procedure does preserve the statistical properties and the transitional probabilities that are indistinguishable from the original data.     

Circulation and Brackish Water Dispersal in a Coastal Lagoon of Southwest Taiwan

Modest observations and numerical experiments were conducted to investigate circulation and brackish water dispersal in a coastal lagoon southwest of Taiwan. The Chi-Ku Lagoon, partially shielded from the sea by a string of sandbar barriers with two entrances among them, receives modest and episodic runoff from Chi-Ku Stream. Flood and ebb tidal streams entering and leaving the two entrances are found to converge and diverge in a flow stagnation area inside the lagoon. Under weak wind conditions, brackish water is preferentially retained in the flow stagnation area. Besides the observations, scenario runs using a three-dimensional numerical model also indicate strong modulation by monsoon winds. Both summer southwest monsoon and winter northeast monsoon reduce brackish water retention; the latter is found to be more effective. This revised version was published online in August 2006 with corrections to the Cover Date.     

南极格罗夫山陨石的磁化率

我国在南极格罗夫山发现并收集到大量南极陨石,需要一种无损、快速简单的分类方法。陨石磁化率(χ)的主要贡献是其中的铁镍金属,因而有可能成为一种简便有效的分类参数。同时,磁化率是陨石的一个重要物理参数。我们在国内首次开展陨石磁化率的研究,通过对模拟陨石磁化率样品的测量,证明可以通过不同取向的测量平均,将样品的大小和形状等几何因素的影响减小在仪器的测量精度范围之内。完成了首批613块南格格罗夫山陨石的磁化率测量,除普通球粒陨石外,还包括火星陨石、灶神星陨石、碳质球粒陨石、中铁陨石、橄榄陨铁、橄辉无球粒陨石等特殊类型。根据质量磁化率,可以划分大部分H、L、LL群陨石。特别重要的是,磁化率对于划分非平衡的普通球粒陨石化学群提供了更为可靠的参数。格罗夫山H群陨石的磁化率分布与南极其他地区的陨石十分相似,二者相对降落型陨石均向低质量磁化率方向平移0.2（logχ, 10^-9m³/kg）,反映了风化作用对南极陨石磁化率的平均影响程度;格罗夫山L群陨石的质量磁化率分布同样较降落型陨石偏低0.2左右,但南极其他地区的陨石与沙漠陨石的磁化率分布相似,二者均更为离散和偏低,可能反映了不同的风化程度。     

福建邵武地区大坪—张厝一带萤石矿地质特征及下一步工作方向

正研究区位于福建省邵武市大坪—张厝一带,区内断裂构造发育,萤石矿化强烈,物化探异常明显,萤石矿床(点)众多。综合区内成矿地质特征及成矿规律,区内萤石矿找矿潜力大,新发现的张厝、新坪萤石矿通过进一步工作,有望达到中大型萤石矿床规模。1 区域地质     

Geochemical signatures of Mesoproterozoic siliciclastic rocks of the Kaimur Group of the Vindhyan Supergroup,Central India

The Upper Kaimur Group of the Vindhyan Supergroup in Central India, primarily consists of three rock types-DhandraulSandstone, Scarp Sandstone and Bijaigarh Shale. Mineralogically and geochemically, they are quartz arenite, sublitharenite to litharenite and litharenite to shale in composition, respectively. The A-CN-K ternary plot and CIA and ICV values suggest that the similar source rocks suffered severe chemical weathering, under a hot-humid climate in an acidic environment with higher P _{CO 2}, which facilitated high sediment influx in the absence of land plants. Various geochemical discriminants, elemental ratios like K₂O/Na₂O, Al₂O₃/TiO₂, SiO₂/MgO, La/Sc, Th/Sc, Th/Cr, Gd_N/Yb_N and pronounced negative Eu anomalies indicate the rocks to be of post-Archean Proterozoic granitic source, with a minor contribution of granodioritic input, in a passive margin setting. The sediments of the Upper Kaimur Group were probably deposited in the interglacial period in between the Paleoproterozoic and Neoproterozoic glacial epochs.     

基于Monte Carlo-BP神经网络TBM掘进速度预测

预测隧道工程中TBM掘进速度,主要有完全经验的、半理论半经验的模型和人工智能等方法,所用参数均为确定性的,未考虑参数存在的随机性,故导致预测结果的不准确性。基于此,提出了Monte Carlo-BP神经网络TBM掘进速度预测模型,着重考虑了一些重要输入参数的随机性, 其中输入参数重要性的大小通过粗糙集进行计算排序。采用Monte Carlo产生随机数时,由于参量的样本数据的有限,分布函数均采用阶梯形经验分布函数。如果采用的数据是来自不同类型的 TBM,则应当考虑机器性能参数,并重新对参数重要性进行排序。实例计算表明,Monte Carlo-BP神经网络模型预测结果和实测值总体趋势和均值比较一致。     

基于PSO-PP的围岩稳定性评价

围岩的稳定性评价是一个复杂的不确定系统问题。结合投影寻踪方法、粒子群算法和逻辑斯谛曲线函数,建立了围岩稳定性评价的粒子群优化投影寻踪(projection pursuit based on particle swarm optimization,PSO-PP)模型。该模型一方面采用粒子群算法优化投影指标函数及逻辑斯谛曲线函数参数,确保了模型的准确性;另一方面利用逻辑斯谛曲线函数建立投影值与经验等级之间的非线性关系。模型的测试结果显示了良好的精度,实例分析结果与实际状态完全一致,表明该模型在围岩稳定性评价中的可行性和有效性。     

GIS支持下的土地资源环境承载力评价——以荣成市为例

随着人类生产活动的日益加剧,土地资源环境已逐渐成为制约社会经济发展的主要因素。基于荣成市土地资源环境要素,利用层次分析法构建了区域土地资源环境承载力评价体系。对土地资源进行基础性评价,在GIS技术支持下计算各项指标分值,并得出承载状态压力指数;在基础性评价结果的基础上,对水资源、生态条件与环境质量进行修正性评价。结果显示,荣成市土地资源环境基础性评价为可载,基于水资源系统的综合承载状态为临界,基于生态条件与环境质量系统的综合承载状态为可载。