The improvement of the one-dimensional Mellor-Yamada and K-profile parameterization turbulence schemes with the non-breaking surface wave-induced verticalmixing

Both the level 2.5 Mellor-Yamada turbulence closure scheme(MY) and K-profile parameterization(KPP) are popularly used by the ocean modeling community.The MY and the KPP are improved through including the non-breaking surface wave-induced vertical mixing(Bv),and the improved schemes were tested by using continuous data at the Papa ocean weather station(OWS) during 1961–1965.The numerical results showed that the Bv can make the temperature simulations fit much better with the continuous data from Papa Station.The two improved schemes overcame the shortcomings of predicting too shallow upper mixed layer depth and consequently overheated sea surface temperature during summertime,which are in fact common problems for all turbulence closure models.Statistical analysis showed that the Bv effectively reduced the mean absolute error and root mean square error of the upper layer temperature and increased the correlation coefficient between simulation and the observation.Furthermore,the performance of vertical mixing induced by shear instability and the Bv is also compared.Both the temperature structure and its seasonal cycle significantly improved by including the Bv,regardless of whether shear instability was included or not,especially for the KPP mixing scheme,which suggested that Bv played a dominant role in the upper ocean where the mean current was relatively weak,such as at Papa Station.These results may provide a clue to improve ocean circulation models.     

The physical simulation of wave groups and their variations in a wave flume

The physical simulation method of wave groups in a wave flume is proposed and verified by the exper- iments. The experimental results demonstrate that random waves with desired wave groupiness, which simultaneously includes the wave group height and length, can be generated satisfactorily at the specified position in a wave flume using the proposed method. Furthermore, the transformation properties of the wave groupiness along the fiat-bottomed wave flume are investigated based on the physically simulated waves. Associated proposals with the physical simulation of wave groups are given.     

新疆哈尔里克山后碰撞期构造-岩浆活动特征及年代学证据

新疆地区后碰撞期构造-岩浆活动非常活跃,其同位素年龄集中在290～270Ma。天山东段的后碰撞期构造-岩浆活动研究相对薄弱,高质量的定年数据较少。在野外地质调查的基础上,笔者选择东天山哈尔里克地区的小堡花岗岩体进行了锆石的LA-ICP-MSU-Pb定年,获得两组谐和年龄,分别为297±2Ma和295±2Ma,代表哈尔里克地区后碰撞早期岩浆作用的形成时代。小堡岩体矿物组合以斜长石、石英、钾长石、角闪石和黑云母为主,副矿物有锆石、磷灰石和磁铁矿,与I型高钾钙碱性花岗岩矿物组合基本一致。与小堡花岗岩体同时发育、空间并存的还有基性岩墙群和碱性花岗岩,构成后碰撞初始阶段三组合地质标志。结合区域地质资料,认为小堡早二叠世花岗岩体是后碰撞初期碰撞造山、地壳增厚引发下地壳物质部分熔融的产物,其构造属性为板内岩浆活动。     

基于遗传算法优化的小波神经网络在地下水位预测中的应用

地下水位是衡量生态环境优劣和地下水资源的一个重要指标.地下水位下降,将引发地面沉降、地面塌陷和降落漏斗等.因此,地下水位预测对保护地质生态环境和实现地下水资源严格管理至关重要.由于BP算法存在极易收敛于局部极小点与过拟合等缺点,导致网络泛化能力不足,本文在构建小波神经网络基础上并引入遗传算法加以优化,以解决上述不足,并与BP和WNN对比预测了天津市深层承压水水位.预测结果表明,GA-WNN模型拟合精度较高,模型的预测能力有较大幅度提高.     

三峡重庆库区生态监测规划研究

本文在简要介绍三峡重庆库区基本情况的基础上,分析了库区生态问题,科学合理地提出了重庆库区生态监测的布站原则、站点选择、监测方式、监测项目和监测方法,为三峡库区生态监测进行了前瞻性的研究.     

跨越法采煤控制矿坑涌水量及防治岩溶地面塌陷的研究——以湖南辰溪孝坪煤矿桠杉坡井为例

湖南辰溪孝坪煤矿桠杉坡井为岩充水煤矿井,采用跨越法采煤,辅以封闭突水点、废弃巷道、采空区和将矿坑水排泄补给浅部溶洞水,控制了矿坑涌水量,保持了地下水基本平衡,防止了矿坑疏干排水诱发岩溶地面塌陷。其成功经验可供借鉴。     

青海西部祁漫塔格成矿带找矿新进展及其意义

青海省祁漫塔格成矿带位于东昆仑造山带北西段,带内以往开展了以铁为主的找矿工作,发现一批矽卡岩型中型矿床。近年来带内找矿取得了重大突破:①发现一种新类型矿床——"虎头崖"式层控型大型多金属矿床;②低缓磁异常区多金属和铁矿找矿效果显著,发现四角羊-牛苦头大型多金属矿床,野马泉矿区多金属矿规模达到中型,尕林格矿区发现深部厚大的铁矿体。在系统地综合总结这些成果的基础上,探讨了找矿新进展的意义,划分了3处成矿远景区,并初步讨论了各成矿远景区内进一步找矿的方向。     

鹤岗煤田深部煤炭资源潜力分析研究

根据矿区已有地质资料,从构造、沉积古地理环境分析,认为鹤岗煤田深部与煤田现生产区属同一个沉积环境,其古构造条件、沉积体系及成煤环境相同,煤田深部预测区是生产区的延深。依据钻探、物探对深部构造及煤层的控制,证实煤田深部煤层发育较稳定,具有一定的煤炭资源潜力,值得进一步勘查和开发利用。     

Identification of temporal and spatial variations of water quality in Sanya Bay,China by three-way principal component analysis

A large data set obtained by a 1-year monthly determination of water quality from Sanya Bay, South China Sea, was treated by three-way principal component analysis aimed at exploring the spatial and temporal patterns of water quality in Sanya Bay. Tucker3 model of optimum complexity (2, 2, 1) explaining 33.18% of the data variance, allowed interpretation of the data information in three modes. The model explained spatial and temporal variation trends in terms of water quality variables during the study period. Water quality in sampling station (S2) Sanya River was mainly influenced by Sanya River, and water quality in other stations (S1, S3–S10) were mainly influenced by the waters in South China Sea. The results delineated the mouth of Sanya River as critical from pollution point of view. The dry season from October to the next April and rainy season from May to September have different influences on water quality in Sanya Bay. The information extracted by the three-way models would be very useful to regional agencies in developing a strategy to carry out scientific plans for resource use based on marine system functions.