Contrast between the Climatic States of the Warm Pool in the Indian Ocean and in the Pacific Ocean

Based on the analysis of Levitus data, the climatic states of the warm pool in the Indian Ocean (WPIO) and in the Pacific Ocean (WPPO) are studied. It is found that WPIO has a relatively smaller area, a shallower bottom and a slightly lower seawater temperature than those of WPPO. The horizontal area at different depths, volumes, central positions, and bottom depths of both WPIO and WPPO show quite apparent signals of seasonal variation. The maximum amplitude of WPIO surface area‘s seasonal variation is 58% larger over the annual mean value. WPIO‘s maximum volume variation amplitude is 66% larger over the annual mean value. The maximum variation amplitudes of the surface area and volume of WPPO are 20.9% and 20.6% larger over the annual mean value respectively. WPIO and WPPO show different temporal and spatial characteristics mainly due to the different wind fields and restriction of ocean basin geometry. For instance, seasonal northern displacement of WPIO is, to some extent, constrained by the basin of the Indian Ocean, while WPPO moves relatively freely in the longitudinal direction. The influence of WPIO and WPPO over the atmospheric motion must be quite different.     

矿渣堆上起良田

正是麦收季节，在义马市马庄村附近义煤集团北露天排渣场新复垦的土地上，家住马庄村的马宗新老汉心情格外的高兴，因为今年他家刚分的七亩地里种的麦子长的还不赖。麦收前，他和老伴正忙着种花生，说起收成，他乐了：“去年又分了七亩地，我种了一半的油菜，长的也不赖，打了100多斤油。几亩麦子最少也能打1000多斤麦……”和马宗新不同，马庄村二组的马志敏承包了100多亩地种烟叶，他投资了几万元建起了5个可炕20亩烟叶的大烟炕。马志敏看重这个地方，是因为这里条件好，他说：“这里空气好，污染小，刚复垦的土地土层均匀，适合种烟叶……”     

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.     

有限元法与伪谱法混合求解弹性波动方程

在地震波场数值模拟中,有限差分法、有限元法和伪谱法都是常用的基本方法,但它们各有不同的适应性和优缺点,如有限差分法、有限元法都存在减弱网格频散和提高计算效率的矛盾,而伪谱法的网格频散小且计算效率高.有限差分法和伪谱法在处理地表结构复杂或地表剧烈起伏以及地下结构复杂的情况时存在较大的难度,而有限元法可较为理想地拟合起伏地表和任意弯曲界面,且可方便地处理自由边界条件和界面边界条件.尝试将有限元法和伪谱法相结合,形成地震波场数值模拟的一种混合方法,利用二者的优点,克服二者的缺点,达到既减弱网格频散又提高计算精度和效率的目的.并采用所谓的‘过度区域‘技术解决两种不同算法的衔接问题.模拟实例表明,给出的混合模拟方法不失为弹性波场数值模拟的一种有效方法.     

HD 82943行星系统的2:1共振与稳定性

用直接数值积分方法通过模拟不同的行星构型探讨了HD82943行星系统(由两颗共振的巨行星组成)的长期动力演化,同时,还研究了在相空间的轨道运动.在对系统长达107年数值积分中,发现所有的稳定轨道均与2:1共振相关联.典型地,在相同的时标内,两个共振幅角θ1和θ2同时(或其中之一)存在秤动.由于共振幅角在一定范围内的秤动,因而使两颗行星轨道半长径被约束而表现为规则运动模式.另外,利用分析模型(包含了外行星偏心率e2的因素),还讨论了对于不同取值的e2和相对近星点经度θ时,内行星在相空间的运动,并发现2:1轨道共振对于相对较小的e2以及当θ=0°时易于保持.此外,适中的e2将导致系统的两颗行星进入深度共振状态.再者,分析模型和数值计算的结果吻合得很好,两者都揭示了行星系统的2:1共振结构.     

重力插值方法研究

为了根据离散观测数据构制连续空间重力变化图像，分析和讨论了３种数值插值方法，计算结果表明多面函数方法插值精度最高。由于逐步回归分析筛选核函数中心点的计算繁琐，文中提出根据分形理论和Ｓｈａｎｎｏｎ取样定理来确定核函数中心点。对滇西试验场进行模拟试算，插值精度可达到４～５（１０－８ｍｓ－２）。