新疆博斯腾湖沉积岩心的~(210)Pb、~(228)Th、~(239,240)Pu和~3H的分布及意义

作者测定了博斯腾湖沉积岩心~(210)Pb、~(228)Th和核试验释放核素~(239,240)Pu、~3H的分布,并据~(210)Pb、~(228)Th、~(239,240)Pu的结果估算湖泊的沉积速率,分别为0.31cm/a,0.26cm/a和0.33cm/a。它表明该湖泊近百年来沉积环境十分稳定。沉积物岩心极好地保存了人类大气核试验的历史记录。~(239,240)Pu分布在1963±2出现峰值,与60年代核试验高峰期相当吻合。沉积物孔隙水~3H分布的峰值出现在1969年,与湖水的混合过程有关。     

Hamilton系统数值计算的新方法

系统地介绍了近年来对Ｈａｍｉｌｔｏｎ系统数值计算新建立的辛算法和线性对称多步法，并对它们在动力天文中的应用作了一简要回顾。     

矩形虾池环流的流动特点与流动阻力

对矩形虾池在水车式增氧机作用下产生的环流特点进行理论分析，结果表明，在转角处会形成涡旋，边壁附近在一定条件下会产生回流；利用ANSYS软件的FLOTRAN CFD进行数值计算，获得与理论分析一致的结果，与现场实际情况吻合。并进一步对虾池阻力进行分析，提出了一些减阻措施。     

莱州湾南岸潍河下游地区咸水入侵灾害成因及特征

通过对莱州湾南岸咸水入侵较严重地潍河下游地区晚更新世以来沉积特征及现代自然环境条件变化的分析，探讨了沉积相对咸水入侵产生及空间范围特征的环境机理。晚更新世以来的三次海平面升降变化造成了潍河下游地区海陆沉积环境交替，形成了巨厚的海陆交互相沉积层。海进时期，大面积的滨海平原被淹没，在近海平原洼地滞留的海水经过蒸发、浓缩变为卤水，成为咸水入侵的物源；海退后陆源碎屑在滨海地区沉积形成了巨厚的古河道砂层。20世纪70年代末期以来，随着对地下淡水的过度开采，淡咸水水头压力差减小．卤水通过古河道砂层快速南侵。通过对潍河下游地区100余个地质钻孔水化学连续监测资料分析，阐明了咸水入侵的特征，有针对性地提出了咸水入侵的防治措施。     

一种从经纬度网格资料获得正方形网格资料的插值方案

本文提出了一种把经纬度网格点上的气象要素值转化到正方形网格点上的多项式插值方案。对插值方案作了稳定性讨论和定量及定性分析。结果表明,这种插值方案方便可行,而且有广泛适用性。     

22nd IAS Meeting of Sedimentology

The 22nd IAS Meeting of Sedimentology was held in the Convention Centre of the Grand Hotel Adriatic, in a small touristic town Opatija, located on the eastern Adriatic coast where Central Europe and the Mediterranean meet. It was the second IAS Meeting held in Croatia--the first one, 4th IAS Regional Meeting of Sedimentology, was held in Split in 1983. The Meeting in Opatija was organized by the Institute of Geology (Zagreb) and the Croatian Geological Society. The sponsor was Ministry of Science and Technology of the Reoublic of Croatia.     

SIMULATION AND PREDICTION OF DEBRIS FLOW USING ARTIFICIAL NEURAL NETWORK

Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural haz-ard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting de-bris flow have been proposed, however, the accuracy of these methods is not high enough for practical use because of the stochastic and non-linear characteristics of debris flow. Artificial neural network has proven to be feasible and use-fill in developing models for nonlinear systems. On the other hand, predicting the future behavior based on a time se-ries of collected historical data is also an important tool in many scientific applications. In this study we present a three-layer feed-forward neural network model to forecast surge of debris flow according to the time series data collect-ed in the Jiangjia Ravine, situated in north part of Yunnan Province of China. The simulation and prediction of debris flow using the proposed approach shows this model is feasible, however, further studies are needed.