P‐wave transmission across fractures with nonlinear deformational behaviour

Stress wave attenuation across fractured rock masses is a great concern of underground structure safety. When the wave amplitude is large, fractures experience nonlinear deformation during the wave propagation. This paper presents a study on normal transmission of P‐wave across parallel fractures with nonlinear deformational behaviour (static Barton–Bandis model). The results show that the magnitude of transmission coefficient is a function of incident wave amplitude, nondimensional fracture spacing and number of fractures. Two important indices of nondimensional fracture spacing are identified, and they divide the area of nondimensional fracture spacing into three parts (individual fracture area, transition area and small spacing area). In the different areas, the magnitude of transmission coefficient has different trends with nondimensional fracture spacing and number of fractures. In addition, the study reveals that under some circumstances, the magnitude of transmission coefficient increases with increasing number of fractures, and is larger than 1. Copyright © 2006 John Wiley & Sons, Ltd.     

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.     

Identification of physical processes inherent in artificial neural network rainfall runoff models

The emergence of artificial neural network (ANN) technology has provided many promising results in the field of hydrology and water resources simulation. However, one of the major criticisms of ANN hydrologic models is that they do not consider/explain the underlying physical processes in a watershed, resulting in them being labelled as black‐box models. This paper discusses a research study conducted in order to examine whether or not the physical processes in a watershed are inherent in a trained ANN rainfall‐runoff model. The investigation is based on analysing definite statistical measures of strength of relationship between the disintegrated hidden neuron responses of an ANN model and its input variables, as well as various deterministic components of a conceptual rainfall‐runoff model. The approach is illustrated by presenting a case study for the Kentucky River watershed. The results suggest that the distributed structure of the ANN is able to capture certain physical behaviour of the rainfall‐runoff process. The results demonstrate that the hidden neurons in the ANN rainfall‐runoff model approximate various components of the hydrologic system, such as infiltration, base flow, and delayed and quick surface flow, etc., and represent the rising limb and different portions of the falling limb of a flow hydrograph. Copyright © 2004 John Wiley & Sons, Ltd.     

形变大地测量学的进展、问题与地震预报

简要概括了形变大地测量学的革命性进展，研讨了它的科学特色、功能和定义以及对地球科学和防灾减灾的推动。形变大地测量学有助于从根本上击破多年来制约地震预报的“瓶颈”，但也存在不少急待解决的问题。着重研讨了在21世纪前10年，形变大地测量学如何依托多年的学科积累并充分受益于人造卫星和数字化等新技术，开展创新性研究和试验以推进地震预报。为此，对当前的研究工作提出了12条科学技术途径。最后对学科名称提出了建议。     

Comments on ‘A comparative study of ANN and neuro-fuzzy for the prediction of dynamic constant of rockmass’

Singh et al (2005) examined the potential of the ANN and neuro-fuzzy systems application for the prediction of dynamic constant of rockmass. However, the model proposed by them has some drawbacks according to fuzzy logic principles. This discussion will focus on the main fuzzy logic principles which authors and potential readers should take into consideration.     

南海西部海域新生代构造古地理演化及其对油气勘探的意义

南海西部海域构造复杂,主要发育有北东—北东东向、北西向和近南北向3组深大断裂。其中,北西向断裂与板块汇聚、碰撞有关,多具走滑性质;北东—北东东向断裂具有与中国东部裂谷盆地相似的发育特点,呈张扭性质;近南北向断裂可能是南海在扩张活动期间于洋、陆壳过渡部位形成的走滑调节断裂,是洋盆扩张的西部边界。新生代里,南海经历了4次成盆事件与3期扩张活动,盆地经历了古新世—中始新世陆缘断陷、渐新世—早中新世扩展与中中新世以来的热沉降3个演化阶段。陆缘断陷阶段的充填系列主要是北东—北东东向与北西向的河流—冲积扇、湖泊沼泽等陆相沉积及火山岩等;盆地扩展阶段表现为中-小型断陷、断-坳陷逐渐复合与联合为大-中型坳陷,古地理格局逐渐由河流与湖沼陆相环境演变为滨海至浅海相的沉积环境;热沉降阶段的成盆活动逐渐减弱以至停止,地层表现超覆,盆地出现联合迹象。结合以往勘探与油气资源调查成果分析,认为南海西部海域陆架陆坡区发育的大-中型沉积盆地石油地质条件良好,蕴藏着丰富的油气资源,勘探潜力巨大。     

智能潜器的集成仿真系统

本文介绍了智能潜器集成仿真系统的硬、软件结构．通过这一系统所展示的水下虚拟仿真环境能够在研究和开发智能潜器的控制体系结构、潜器载体的水动力学、信息融合和目标识别等工作中发挥巨大作用     

赤点石斑鱼人工配合饵料中蛋白质最适含量的研究

本文探讨了不同蛋白质含量的配合饵料对赤点石斑鱼（Epinephelusakaara）生长的影响。通过60d度验得出：在各配合饵料组中，蛋白质含量为49．52％的试验的相对增重率（46．84％）体长相对增长率（15．96％）、蛋白质效率（60．35％）和饵料转化率（29．85％）等均为最佳。配合饵料组鱼体肌肉营养成分（蛋白质、脂肪、氨基酸和鲜味氨基酸）也均优于对照组，而更接近野生鱼。经回归方程分析，赤点石斑鱼人工配合饵料中蛋白质最适含量为48．37％～49．24％。     

地形坡度对多金属结核分布的控制作用

选取我国东太平洋多金属结核开辟区内的一个区域作为研究区,利用人工神经网络中应用最为广泛的BP网络,建立控制多金属结核分布的地质因素与多金属结核分布之间的映射关系,探讨地形坡度对多金属结核分布的控制作用.结果表明,在经度、纬度、水深、坡度四个因素中,坡度对结核分布影响程度最大;多金属结核主要分布于坡度小于5°的地方;当坡度小于5°时,丰度与地形坡度呈正相关,品位与地形坡度呈负相关,丰度与品位呈负相关;品位与坡度似呈指数关系,坡度增大,品位降低.     

人造海洋涌升流和海洋渔场

针对渔业资源日趋减少和鱼产品需求不断增加的矛盾，探讨了建造海底山脉、人工制造海洋涌升流、从而人工制造优良海洋渔场的可行性，分析了我国建造海底山脉的水深、海底和海流条件。介绍了建造海底山脉的建筑方法和国际上建造海底山脉的最新动态。论述了我国在200n mile以内建造海底山脉的有利条件。