四川省房屋建筑易损性研究

研究房屋建筑易损性特征是防震减灾工作必不可少的环节之一，本文通过对多年工作的系统总结，得出四川省不同地区的房屋建筑易损性矩阵，全省房屋建筑抗震性能总体水平不高，这是造成地震灾害加重的主要原因之一，随着经济发展实力的提高，适当提高全省房屋建筑抗震设防水平可大大减轻特别是中强地震所造成的经济损失。     

历史地震资料与地震的中长期预测

故障诊断模型的基本内容是根据动态系统的外部特征来判断系统内部是否发生故障及确定故障发生的部位、时间和大小。由于故障诊断技术在监测及诊断故障的思路上和地震预报有很多相似之处，因此，把故障诊断技术应用于地震预报是可行的。由于该模型与其他数学模型一样，需要有较多的学习过程，所以历史地震资料在该模型中有重要作用。而其中的鲁棒性故障诊断模型在抑制各子模型的个性，凸现其共性方面有其特有的性质，所以把它作为一种综合模型，能抑制各子模型的个性，突出在地震预测方面的共性，从而提高预测的精确性。本文根据一个实例，说明了这种综合性模型的可行性。在文章的最后，由信息量的分析，说明了模型的鲁棒性特征。     

加卸载响应比理论在华南地区中强震预测中的应用

用加卸载响应比理论对华南地区12例中强以上地震进行研究，结果表明有8例在震前响应比值有明显的高值（Y≥2.0）异常显示，通过对响应比值随时间变化特征的分析发现，响应比对5级左右地震有较好的中短期预测效果，因此响应比理论可作为本区地震预测的新方法。     

福建漳州、华安、龙岩地区地震时间分布结构信息维的特征

岩石破裂实验，地球岩石层断裂构造运动，地震孕育、发生和发展过程等研究表明，地震活动的时间、空间和强度分布特征具有分形结构。作为一种分维理论的应用，本文利用厦门数字化地震遥测台网的数据，采用信息的分析方法，对福建省的地震多发区-漳州、华安、龙岩地区的地震活动进行了分析和研究，讨论了信息维随时间变化的特征。     

无线局域网技术在地震应急指挥中的应用

通过对1987年寻乌地震时应急指挥通信方面的回顾，结合当前无线通信技术在地震现场应急指挥和快速反应过程中的最新应用，阐述了作为通信技术的重要组成部分之一的无线局域网技术的技术标准和特点。及其在地震现场应急指挥工作的作用和应用模式等。     

隔震及超高层建筑的地震反应观测

2001年5月24日和6月20日分别发生了宜良4.2级和呈南3.6级两次地震，架设在云南省抗震培训中心隔震大楼和昆明佳华广场的结构强震观测台阵记录了这两栋建筑物对这两次地震反应。本介绍了获取的记录，并分析了两栋建筑物的地震反应情况。     

Computational modeling of cyclic mobility and post-liquefaction site response

Under seismic excitation, liquefied clean medium to dense cohesionless soils may regain a high level of shear resistance at large shear strain excursions. This pattern of response, known as a form of cyclic mobility, has been documented by a large body of laboratory sample tests and centrifuge experiments. A plasticity-based constitutive model is developed with emphasis on simulating the cyclic mobility response mechanism and associated pattern of shear strain accumulation. This constitutive model is incorporated into a two-phase (solid–fluid), fully coupled finite element code. Calibration of the constitutive model is described, based on a unique set of laboratory triaxial tests (monotonic and cyclic) and dynamic centrifuge experiments. In this experimental series, Nevada sand at a relative density of about 40% is employed. The calibration effort focused on reproducing the salient characteristics of dynamic site response as dictated by the cyclic mobility mechanism. Finally, using the calibrated model, a numerical simulation is conducted to highlight the effect of excitation frequency content on post-liquefaction ground deformations.     

The use of modern statistical theories in the assessment of earthquake hazard, with application to quiet regions of eastern North America

It is generally accepted that both deterministic and statistical approaches are useful for the characterization of earthquake hazard. Although the most reliable estimates of seismic hazard can only be based on an improved understanding of the earthquake mechanism, efficient utilization of the appropriate methods provided by recent statistical theories is also important in seismic risk analysis. This is especially true in regions where the connection between seismicity and geologic structure is tenuous at best. We are particularly interested in developing better statistical treatments of data for regions with little known seismic activity. To this end, we have applied three statistical methods to the historical record of seismicity in relatively quiet regions of eastern North America. These are: (1) the threshold method for tail inference, a new theory for modeling earthquakes with sizes above a given threshold, (2) the ‘bootstrap’ technique in which the characteristics of an unknown population are simulated by replacing the true population by an estimated one, and (3) a technique to estimate the number of earthquakes below a given size, in order to compensate for the under-reporting of small earthquakes in most catalogs. A combination of these techniques has been used to estimate the probabilities of future large earthquakes for the regions studied. Because of limitations imposed by existing catalogs, the size estimate used has been maximum intensity.     

Fuzzy neural network models for liquefaction prediction

Integrated fuzzy neural network models are developed for the assessment of liquefaction potential of a site. The models are trained with large databases of liquefaction case histories. A two-stage training algorithm is used to develop a fuzzy neural network model. In the preliminary training stage, the training case histories are used to determine initial network parameters. In the final training stage, the training case histories are processed one by one to develop membership functions for the network parameters. During the testing phase, input variables are described in linguistic terms such as ‘high’ and ‘low’. The prediction is made in terms of a liquefaction index representing the degree of liquefaction described in fuzzy terms such as ‘highly likely’, ‘likely’, or ‘unlikely’. The results from the model are compared with actual field observations and misclassified cases are identified. The models are found to have good predictive ability and are expected to be very useful for a preliminary evaluation of liquefaction potential of a site for which the input parameters are not well defined.     

Ground truth seismic events and location capability at Degelen mountain, Kazakhstan

We utilized nuclear explosions from the Degelen Mountain sub-region of the Semipalatinsk Test Site (STS), Kazakhstan, to assess seismic location capability directly. Excellent ground truth information for these events was either known or was estimated from maps of the Degelen Mountain adit complex. Origin times were refined for events for which absolute origin time information was unknown using catalog arrival times, our ground truth location estimates, and a time baseline provided by fixing known origin times during a joint hypocenter determination (JHD). Precise arrival time picks were determined using a waveform cross-correlation process applied to the available digital data. These data were used in a JHD analysis. We found that very accurate locations were possible when high precision, waveform cross-correlation arrival times were combined with JHD. Relocation with our full digital data set resulted in a mean mislocation of 2 km and a mean 95% confidence ellipse (CE) area of 6.6 km² (90% CE: 5.1 km²), however, only 5 of the 18 computed error ellipses actually covered the associated ground truth location estimate. To test a more realistic nuclear test monitoring scenario, we applied our JHD analysis to a set of seven events (one fixed) using data only from seismic stations within 40° epicentral distance. Relocation with these data resulted in a mean mislocation of 7.4 km, with four of the 95% error ellipses covering less than 570 km² (90% CE: 438 km²), and the other two covering 1730 and 8869 km² (90% CE: 1331 and 6822 km²). Location uncertainties calculated using JHD often underestimated the true error, but a circular region with a radius equal to the mislocation covered less than 1000 km² for all events having more than three observations.