划分潜在震源区的地震地质规则研究

根据近年来发震构造地质条件研究的进展，本文探讨了划分潜在震源区主要的地震地质规则，具体包括潜在震源区的识别；。它的位置，长度，宽度和震级上限的确定等内容。规则的归纳充分考虑了活动构造性质的差异和资料完备程度的不同。     

新地震区划图潜在震源区划分的主要技术特色

简要介绍了新地震区划图潜在震源区划分方案的形成过程,重点分析了潜在震源区三级划分、东西部地区潜在震源区划分技术途径的差异、不同级别活动块体边界带对高震级潜在震源区划分的控制作用、发震构造模型及其在潜在震源区划分中的应用等主要技术特色.共划分出29个地震带、77个地震构造区和1199个潜在震源区.与中国地震动参数区划图(2001)中综合方案相比,东西部地区潜在震源区的个数都有较大的增加,其中东部地区体现在震级上限6.0、6.5和7.0级的中强潜在震源区个数的明显增加,与划分工作中加强了该地区中强地震发震构造的判识研究相关;而西部地区体现在震级上限7.5和8.0级的高震级潜在震源区个数的大幅度增加,与划分工作中注重了活动块体边界带高震级潜在震源区划分,以及强调应用发震构造模型指导潜在震源区划分的技术特色相协调.     

应用人工神经网络实现潜在震源区的定量划分

本文提出了应用人工神经网络原理, 确定反映潜在震源区特征的地震构造和地震活动因素、定量划分潜在震源区的方法。对华北地区的实际资料计算表明, 该方法充分反映了潜在震源区与其控制特征之间的规律。原理简明, 便于应用, 计算结果合理, 分区细致, 资料具实用性。      

孤立中强地震的潜在震源区划分

提出了孤立中强地震的潜在震源区划分其参数确定的思路和方法，即在综合考虑孤立中强地震所在地震区的总体地震构造特征与地震活动水平的基础上，利用历史地震等震线，余震分布，区域应力场及震源机制等资料，确定其潜在震源区的参数。     

新地震区划图地震构造区划分的原则和方法——以中国东部中强地震活动区为例

在新编全国地震区划图潜在震源区划分工作中,采用了三级划分的技术思路,即在地震统计区内先划分出地震构造区,以控制地震统计区内地震构造和地震活动的差异性,然后,在地震构造区内再进行潜在震源区划分。地震构造区划分是新编全国地震区划图潜在震源区划分工作中的一个关键环节。本文论述了地震构造区的定义、作用、划分原则和依据等。介绍了中国东部地区地震构造区划分方案,并以东北地震区和华南沿海地震带为例,对地震构造区划分方案进行了详细论述。     

当前潜在震源区研究的主要方向

本文讨论了当前潜在震源区研究中强震发生的构造条件、潜在震源区的两级划分、确定潜在震源区的原则、潜在震源区的方向和范围及其震级上限的确定以及数值方法在确定潜在震源区中的应用等主要方面的研究现状，并提出了今后工作中应重点研究的内容。     

四川盆地潜在震源区的细致划分

四川盆地包括川中台拱和川东陷褶束的范围，具有相对稳定的大地构造环境.鉴于该地区地震活动水平相对较低、发震构造不甚明确的特点，在编制1990年版中国地震烈度区划图时，划分的潜在震源区面积也相对较大.本文在总结四川盆地中强地震（Ms=4.7-5.7）发震构造条件的基础上，用地震构造类比原则对四川盆地的潜在震源区进行了重新划分.确定潜在震源区边界时，充分考虑了背斜轴走向、背斜构造的影响带宽度、小震活动密集条带和余震的分布范围.得到了四川盆地更加细致的潜在震源区划分方案.这对于低地震活动水平地区的潜在震源区的细化研究具有一定参考意义。     

智能辅助地震区划系统IASHES

本文概述了“场地地震危险度评估专家系统”课题组６年工作的主要成果和智能辅助地震区划系统ＩＡＳＨＥＳ的组成及测试结果，着重叙述了潜辰源区两级划分的思想，Ａ级潜在辰原区地震活动趋势估计和Ｂ级潜在震源区划分及震级上限判定所依据的专家知识，以及适用于专家系统的Ｂ级潜在震源区加权系数的确定方法。     

山东某场地概率地震危险性分析

利用概率地震危险性分析(CPSHA)方法,对山东某场地进行地震危险性分析,通过对该场地划分潜在震源区,确定地震活动性参数及地震动衰减关系,计算分析地震危险性概率,基本确定对该场地地震动峰值加速度起主要贡献的几个潜在震源区及贡献值,并确定该场地50年超越概率10%的水平向基岩地震动加速度峰值。结果发现,CPSHA方法以具体的构造尺度和更加细致的构造标志来划分潜在震源区,使潜在震源区规模缩减,从而更能反映地震活动在空间分布上的不均匀性。     

大青山山前活动断裂带分段与潜在震源区划分

潜在震源区的划分主要包括潜在震源区范围的划定以及震级上限的确定,目前遵循地震构造类比和地震活动重复等原则。而活断层的分段特性也是潜在震源区划分时必须考虑的一个重要因素。大青山山前断裂带至今有3种不同的分段方案,文中比较分析了前人对大青山山前断裂带的分段,并在此基础上对大青山及山前盆地的潜在震源区作了新的划分。鄂尔多斯块体周缘被拉张性断陷盆地围绕,这些断裂系地震构造相似,且除呼包盆地外均有历史8级以上地震记录。文中将大青山山前断裂带与鄂尔多斯块体周缘断裂系进行了构造对比,特别是与华山山前断裂进行了断裂活动性定量对比,得出雪海沟到土左旗段的震级上限为8级,断裂两端潜源震级上限均为7.5级     

应用人工神经元网络方法识别近震与远震

利用人工神经元网络方法,提出了一种在实时系统中识别近震与远震的算法,并用广东省台网记录的地震波形数据进行训练和测试。结果表明,该算法能准确可靠地识别远、近震,可以应用于台网的实时处理系统。     

供水系统震害预测专家系统

本文建立了供水系统震害预测专家系统,把计算分析方法和专家经验相结合,形成了知识库和推理判断。其中,计算方法考虑了管段的地震反应机理和管土相互作用的特征,包括在地震地质灾害场地的管道震害预测方法。经验判断来的于震害数据和专家经验,较全面地考虑了影响管道震害的主要因素。在ＧＩＳ软件平台上形成了专用程序,界面友好,数据交换方便,已应用于实际工程。     

人工神经元网络方法对首都圈地区潜在震源区的划分

指标选取是人工神经元网络方法应用中最基础，最重要的五环，是判别工作成败的关键。但目前尚没有比较成熟的指标选取或指标显著性评价方法。因此，指标问题入往成了方法效果进一步提高的“瓶颈”问题。该问题在潜在震源区划分应用中也同样存在。     

Application of an intelligent model developed from experimental data to building design for fire safety

Computational fluid dynamics (CFD) techniques are widely adopted to simulate the behavior of fire. However, CFD suffers from the shortcoming of requiring extensive computer storage and a lengthy computational time. In practical applications, although comprehensive field information on velocities, temperatures, pressure, and the fractions of different constitutes can be obtained from CFD simulations, the user may only be interested in few important parameters that index the performance of a compartment design in the event of a fire. The height of the thermal interface (HTI) is one such key index, and refers to the average height above floor level inside a fire compartment at which the temperature gradient is highest. In practice, a fire compartment is considered untenable when the HTI drops below the respiratory level of the occupants, and in optimizing the design of a fire system, another set of design parameters (e.g., the width of the door opening) must be examined if the HTI of a fire compartment design is evaluated by CFD as being too low. This trial and error exercise then continues until a close to optimum set of design parameters is achieved. This approach is theoretically feasible, but requires lengthy computational time. This paper proposes the application of an Artificial Neural Network (ANN) approach as a fast alternative to CFD models to simulate the behavior of a compartment fire. A novel ANN model named GRNNFA has been specially developed for fire studies. It is a hybrid ANN model that combines the General Regression Neural Network (GRNN) and Fuzzy ART (FA). The GRNNFA model features a network structure that grows incrementally, stable learning, and the absence of the noise embedded in experimental fire data. It has been employed to establish a system response surface based on the training samples collected from a full-scale experiment on compartment fire. However, as the available training samples may not be sufficient to describe the behavior of all systems, and especially those involving fire data, this paper proposes that extra knowledge be acquired from human experts. Human expert network training has thus been developed to remedy established system response surface problems. After transforming the remedied system response surface to the problem domain, a Genetic Algorithm (GA) is applied to evaluate the close to optimum set of design parameters.     

基于人工神经网络的珠江三角洲地壳稳定性研究

应用BP神经网络方法建立了珠江三角洲地壳稳定性的评价模型。首先在前人研究基础上,分析了珠江三角洲的地震活动、断层发育程度、地壳垂直形变、第四系厚度及地热分布等特征,然后收集相应的数据,建立这些影响因子与地壳稳定性之间相关性的转化原则,对这些数据进行标准化转化,建立并训练BP人工神经网络模型,由模型的实际输出插值得出珠江三角洲地壳稳定性的等值线图。与前人对该区地壳稳定性的定性及定量分析对比表明,该模型的评价结果与前人研究结果基本一致,因此基于神经网络的珠江三角洲地壳稳定性评价模型是比较可靠的。     

Conceptual fuzzy neural network model for water quality simulation

Artificial neural networks (ANNs) have been applied successfully in various fields. However, ANN models depend on large sets of historical data, and are of limited use when only vague and uncertain information is available, which leads to difficulties in defining the model architecture and a low reliability of results. A conceptual fuzzy neural network (CFNN) is proposed and applied in a water quality model to simulate the Barra Bonita reservoir system, located in the southeast region of Brazil. The CFNN model consists of a rationally‐defined architecture based on accumulated expert knowledge about variables and processes included in the model. A genetic algorithm is used as the training method for finding the parameters of fuzzy inference and the connection weights. The proposed model may handle the uncertainties related to the system itself, model parameterization, complexity of concepts involved and scarcity and inaccuracy of data. The CFNN showed greater robustness and reliability when dealing with systems for which data are considered to be vague, uncertain or incomplete. The CFNN model structure is easier to understand and to define than other ANN‐based models. Moreover, it can help to understand the basic behaviour of the system as a whole, being a successful example of cooperation between human and machine. Copyright © 2006 John Wiley & Sons, Ltd.     

An improved neural network approach to the determination of aquifer parameters

In this paper, an artificial neural network (ANN) approach to the determination of aquifer parameters is developed. The approach is based on the combination of an ANN and the Theis solution. The proposed ANN approach has advantages over the existing ANN approach. It avoids inappropriate setting of a trained range. It also determines the aquifer parameters more accurately and needs less required training time. Testing the existing and the proposed ANN approaches by 1000 sets of synthetic data also demonstrates these advantages. As to the comparison between the proposed ANN approach and the type-curve graphical method, an application to actual time-drawdown data shows that the proposed ANN approach determines the aquifer parameters more precisely. The proposed ANN approach is recommended as an alternative to the type-curve graphical method and the existing ANN approach.     

磁流变变阻尼半主动控制结构的仿真分析

由于高阶单步法已成功地应用于结构非线性分析及考虑时滞的主动控制等，显示了它的稳定、精度高和计算迅速等特点。用于结构振动控制的磁流变阻尼器属于速度相关型变阻尼半主动控制器，已有的一些算法，或因计算时间过长不能用于实际控制，或因需要事先经过学习训练，控制效果取决于训练学习的情况。因此，本文将高阶单步法应用于磁流变变阻尼半主动控制系统，采用开关控制和连续控制两种控制率对磁流变液的剪切强度进行调节。仿真计算表明，该控制算法是一种能用于结构实际控制的变阻尼有效算法.     

潜在地震灾害预测和评价神经网络系统及其若干问题

结合人工神经网络自身的特性和地震灾害预测研究的特点,本文应用神经网络模型,建立了潜在地震灾害预测和评价系统。针对网络模型参数设置、数据归一化、中间层神经元最优数目以及泛化分类评价指标等若干实际问题给出了实际可行的解决方案。通过大样本数据对网络的训练,形成了有识别和记忆功能的非线性预测和评价系统。对网络的测试和检验,论证了该系统在预测潜在地震灾害上的可行性和有效性。同时,从测试精度出发,探讨了这种预测网络存在的不足,并给出了相应的改进建议,为开展进一步的研究工作提供了参考。     

A prototype earthquake warning system for strike-slip earthquakes

A prototype expert system has been developed to provide rapid warning of earthquakes while they are occurring. Warning times of up to 100 seconds will be possible. In the complete system, several accelerometers are distributed at intervals within a few kilometers of a known fault; data are telemetered to a central computer which implements the expert system. The expert system incorporates specific information about the type of fault to be monitored, and includes simple rules for estimating the fault slip, rupture length, and seismic moment, all in real time. If the seismic moment exceeds a preset value, an alarm may be issued. The prototype is designed for deployment on near-surface strike-slip faults such as the San Andreas and has been successfully tested with data from the 1979 Imperial Valley and 1984 Morgan Hill earthquakes. Crucial concepts have also been tested using synthetic data calculated for a model of the 1857 Fort Tejon earthquake. Parkfield, California, could be used as a test site.