我国地震地下水温度动态观测与研究

本文系统介绍了我国地震地下水温度动态观测技术、正常动态类型与震前异常的统计特征，并初步讨论了水温异常的机制问题。     

藏北阿木岗群中放射虫生物群的发现及有关问题的讨论

我们在原划归前武或前泥盆纪，并认为是所谓羌塘古陆块基底的阿木岗中发现了中－晚古叠世放射虫生物群，说明该地层单位的定义及其划分与对比都值得商榷。本文在此基础上，结合其他地质资料，就有关问题做了讨论。     

应力奇异问题自适应有限元分析初探

讨论了应力奇异问题的ｈ型自适应分析非收敛性和伪收敛性，并从理论上进行了分析。从工程应用出发，在保证一定计算粘度的前题下，提出了一种避免自适应分析失败的方法。算例验证了其适用性。     

前馈神经网络隐性单调与泛化能力

应用前馈人工神经网络对广域单调的两组样本进行了模拟反演，引入单调前馈网络的概念对其权值和阈值定解问题和泛化能力进行了较诉研究。表明前馈人工神经网络是一个表达形式简单的复杂系统，其单调特征是隐性的，而且训练网络的成熟性对样本数量和样本内在规律性有一定依赖。强调了前馈人工神经网络的应用效果，指出单调与复合问题还需进一步深入研究。     

基于主成分分析和贝叶斯正则化方法的神经网络年最大洪峰流量预测模型探讨

针时水文预测建模中输入因子过多而导致神经网络结构规模过大,泛化能力差的问题,利用主成分分析和贝叶斯正则化方法对神经网络进行改进,优化网络结构,从而提高泛化能力。以洮儿河流域镇西站年最大洪峰流量预测为例,研究结果表明,改进的神经网络预测方法与传统的神经网络方法相比,泛化能力有显著提高,而且网络的收敛也比较稳定,实际预测中效果良好。     

基于融合残差注意力机制的卷积神经网络地震信号去噪

由于U型卷积神经网络(Unet)在地震数据去噪中存在计算量大、网络退化和泛化能力弱等问题,本文为了提高去噪效果以及增强模型的泛化性,提出了一种融合残差注意力机制的卷积神经网络(RAUnet)。该网络结构主要由编码和解码两部分构成,网络的每个卷积层之后都加入了批标准化和带泄露整流激活函数。在编码器中,为了提高对噪声的提取能力,引入了残差结构和卷积块注意力模块。残差结构利用残差跳跃连接的方式减弱了网络退化,降低了特征映射的难度。卷积块注意力模块使用通道和空间的混合注意力权重,能提升相关度高的特征并抑制相关度低的特征。在解码器中,为了提升特征融合的维度恢复能力,选用双线性插值方式进行上采样。实验测试结果表明,对于合成地震信号,本文方法对简单模型和复杂模型随机噪声的压制效果均更有效,并且更好地保护了有效信号;对于实际地震信号,本文方法仍然能在去噪的同时尽量保持有效信号中的细节,对叠前数据和叠后数据都展现出了良好的泛化性。     

前陆盆地挠曲过程模拟的理论模型

本文讨论了前陆盆地形成的主要控制因素，包括逆冲负荷、盆地沉积物负荷、地壳内部水平挤压力和地壳力学性质，介绍了前陆盆地弹性和粘弹性挠曲力学模型的基本特征。在此基础上，结合典型实例，探讨了运用粘弹性和弹性挠曲模型模拟前陆盆地沉降和沉积过程的基本方法和基本原理，揭示了造山带与前陆盆地系统演化的动力作用过程。     

试论前寒武纪变质岩古地磁研究的可行性：以闽北地区的研究为例

本文从变质作用与岩石矿物获得剩磁的关系和剩磁获得时间与同位素记年的相关性，岩石的形变对磁化方向的影响等方面讨论了前寒武纪变质岩古地磁研究的可行性；并列举部分国外前寒武纪早期岩石的例证；同时重点以闽北地区前寒武纪变质岩的古地磁结果为例，讨论了获得磁性可信性及其在地质构造方面的意义。     

造山带古地理和盆地分析基础：露头的复原与复位

造山带地层露头非原地性广为标识，但其复原与复位并未引起重视。以扬子地台西缘前龙门山中北段泥盆系为例，进行露头展平宽度、剥蚀宽度复原，对露头原始位置进行尝试性复位，并讨论的存在问题和可能的发展方向。     

气候学研究进展

回顾了80年代到90年代中期，气候学研究在几个方面的重要进展。共分析了五个问题：①20世纪气候变率，重点讨论年代际变率及气候突变；②ENSO模拟与预测，总结比较了各种模式的模拟与预测结果，着重讨论了90年代前半期ENSO发展的特点；③ENSO与季风，指出亚洲夏季风在ENSO循环中的重要作用；④气候可预报性研究，总结了月、季预报试验，介绍了用观测下边界强迫AGCM的研究结果；⑤气候变化成因分析，着重说明温室效应研究的不确定性及自然因子如太阳活动、火山活动在气候变化中的作用。     

Neuro-fuzzy modeling based genetic algorithms for identification of geochemical anomalies in mining geochemistry

A genetic algorithm (GA)-based neuro-fuzzy approach is used for identification of geochemical anomalies by implementing a Takagi, Sugeno and Kang (TSK) type fuzzy inference system in a 5-layered feed-forward adaptive artificial neural network. This paper investigates the effectiveness of GA-based neuro-fuzzy for separating zone dispersed mineralization (ZDM) from blind mineralization, and its application for identification of geochemical anomalies in the arid landscape of the Lut metallogenic province in eastern Iran. Other classification algorithms such as metallometry, zonality, criteria, and back-propagation artificial neural network classifiers are also used for comparison. The genetic operators are carefully designed to optimize the artificial neural network, avoiding premature convergence and permutation problems. The results show that the GA-based hybrid neuro-fuzzy model can provide accurate results in comparison with those results obtained by other techniques. Neuro-fuzzy and GA-based neuro-fuzzy techniques appear to be well-suited for routine exploration geochemistry applications. In conjunction with statistics and conventional mathematical methods, hybrid approaches can be developed and may prove a step forward in the practice of applied geochemistry.     

区域水资源水环境保护治理的符点目标推定

探讨了基于水基系统概念的区域水资源水环境保护治理思路,定义了具有持续性、随律性和变化性的定尺度水基系统演进的符点目标,从水资源承载力和水环境承载力的角度建立了区域水资源水环境取排水控制关系,用BP人工神经网络智能方法提出了对水量水质耦合过程以及水价调节作用进行学习、训练的架构,从而为推求区域水资源水环境保护治理的符点目标进行了步骤与方法的探索.     

Recognizing Correlated Patterns from Remotely Sensed Images: A New Learning Rule Based on Spin Glass Theory

Classification of remotely sensed images is a rich research field wherein techniques from conventional statistics to recent developments such as Artificial Neural Network, Fuzzy logic etc. has wide applications. Conventionally remotely sensed image classification referred to pixel classification based on broad categories such as vegetation and water bodies. With the availability of high-resolution imageries, shape analysis of macro structures contained in images becomes an important and difficult task. Although conventional statistical pattern recognition techniques give a reasonable result, Artificial neural network methods seem to be giving better results. In this paper, we give a survey of feed-forward neural network used for shape classification and a Hopfield model with an improved learning rule, for a typical shape analysis problem.     

基于差异进化算法的前馈神经网络在大坝变形监测中的应用

针对当前大坝安全监测中广泛采用的回归模型欠拟合的不足,提出了基于差异进化算法的前馈神经网络模型。差异进化算法是基于种群策略的全局优化搜索算法,具有应用简单、收敛快的优点。采用该法训练的神经网络可以有效避免常规BP(back propagation)神经网络收敛于局部极小点的缺陷。将提出的方法应用于某拱坝的变形监测,通过计算表明,应用DE(differential evotntion)神经网络模型预报大坝变形的精度比常规回归模型和BP神经网络模型均有所提高。     

Landsat-TM遥感影像岸线变迁解译研究--以九龙江河口地区为例

利用遥感与GIS技术相合的手段，在分析九龙江河口地区地物波谱的基础上，对研究区两期遥感影像（1986年6月与2000年5月的陆地卫星Landsat—TM资料）进行解译和岸线提取。通过与20世纪70年代的地形图进行对比分析，发现九龙江北、西溪过河口大沙洲后分成北、中、南3个支流入海，其中以浒茂洲两侧的支流岸线变化最大；海门岛至口门段河口两侧岸线也有不同程度的变化；厦门西港海区是本研究区岸线变化最大的区域。文章最后对岸线变迁和河口淤积进行了初步的分析。     

Comparison of three artificial neural network approaches for estimating of slake durability index

Slake durability index (I _d2) is an important engineering parameter to assess the resistance of clay-bearing and weak rocks to erosion and degradation. Standard test sample preparation for slake durability test is difficult for some rock types and the test is time-consuming. The paper reports an attempt to define I _d2 using other parameters that are simpler to obtain. In this study, three different artificial neural network approaches, namely feed-forward back propagation (FFBP), radial basis function based neural network (RBNN), and generalized regression neural networks (GRNN) were used for estimating I _d2. The determination coefficient (R ²), root mean square error and mean absolute relative error statistics were used as evaluation criteria of the FFBP, RBNN, and GRNN models. The experimental results were compared with these models. The comparison results indicate that the GRNN models are superior to the FFBP and RBNN models in modeling of the slake durability index (I _d2).     

Application of Generalized Regression Neural Networks in Predicting the Unconfined Compressive Strength of Carbonate Rocks

Measuring unconfined compressive strength (UCS) using standard laboratory tests is a difficult, expensive, and time-consuming task, especially with highly fractured, highly porous, weak rock. This study aims to establish predictive models for the UCS of carbonate rocks formed in various facies and exposed in Tasonu Quarry, northeast Turkey. The objective is to effectively select the explanatory variables from among a subset of the dataset containing total porosity, effective porosity, slake durability index, and P-wave velocity in dry samples and in the solid part of samples. This was based on the adjusted determination coefficient and root-mean-square error values of different linear regression analysis combinations using all possible regression methods. A prediction model for UCS was prepared using generalized regression neural networks (GRNNs). GRNNs were preferred over feed-forward back-propagation algorithm-based neural networks because there is no problem of local minimums in GRNNs. In this study, as a result of all possible regression analyses, alternative combinations involving one, two, and three inputs were used. Through comparison of GRNN performance with that of feed-forward back-propagation algorithm-based neural networks, it is demonstrated that GRNN is a good potential candidate for prediction of the unconfined compressive strength of carbonate rocks. From an examination of other applications of UCS prediction models, it is apparent that the GRNN technique has not been used thus far in this field. This study provides a clear and practical summary of the possible impact of alternative neural network types in UCS prediction.     

GIS技术在太原西山地区岩溶水系统研究中的应用

查明太原西山地区岩溶水系统的结构及其循环演化规律,正确评价岩溶地下水资源,对于太原市工农业的发展具有十分重要的意义.本文详细介绍了地理信息系统(GIS)技术,在准确快速描绘西山地区的区域隔水底板等值线图中的应用方法,将有利于对该岩溶水系统的结构、循环演化规律及地下水资源空间分布的了解.     

An analysis of coastal erosion in the tropical rapid accretion delta of the Red River, Vietnam

The largest plain in the North Vietnam has formed by the redundant sediment of the Red River system. Sediment supply is not equally distributed, causing erosion in some places. The paper analyzes the evolvement and physical mechanism of the erosion. The overlay of five recent topographical maps (1930, 1965, 1985, 1995, and 2001) shows that sediment redundantly deposits at some big river mouths (Ba Lat, Lach, and Day), leading to rapid accretion (up to 100 m/y). Typical mechanism of delta propagation is forming and connecting sand bars in front of the mouths. Erosion coasts are distributed either between the river mouths (Hai Hau) or nearby them (Giao Long, Giao Phong, and Nghia Phuc). The evolvement of erosion is caused by wave-induced longshore southwestward sediment transport. Meanwhile sediment from the river mouths is not directed to deposit nearshore. The development of sand bars can intensively reduce the erosion rate nearby river mouths. Erosion in Hai Hau is accelerated by sea level rise and upstream dams. Sea dike stability is seriously threatened by erosion-induced lowering of beach profiles, sea level rise, typhoon, and storm surge.