利用历史有感地震目录（M≥3（1／2））研究华北地区地震活动性特征

对新编的华北地区历史有感地震目录（３（１／２）≤Ｍ＜４（１／２））的精度和可信度进行了讨论。认为该地震目录从１４８４年后的记载率较高，它的发震时间、震中位置和震级的精度基本可以满足研究历史地震活动性的需要。它与中国地震目录（Ｍ≥４（３／４））比较，补充了相当数量的３（１／２）≤Ｍ≤４（１／２）的地震。利用历史有感地震目录分析了华北北部部分６级以上的历史地震，结果表明，这些地震在强震发生前具有明显的空区、条带、地震活动性增强、平静等前兆特征，同时，也显示了某些强＊发生前的前震活动和主震之后起伏的强余震活动     

Study on the characteristics of rock failure strain and acoustic emission field for two parallelling faults with the same slip direction including asperities

Introduction Concerning about research on grouped-earthquake seismogenic and occurrence, at present it is only limited on the stage of the theoretical analysis and numerical model. For instance, ZHANG (1987) simulated the grouped-earthquake generation and physical mechanism making use of spring-block principle. Applying for non-linear dynamic model, referring to Chinese continental plate as object, LI, et al (1997) performed the numerical simulation on the seismogenic and occurring of group…     

首都圈地区中等地震前小震活动异常特征分析

近年来首都圈地区以中等地震活动为主，本文用反映地震频率、能量、平均震级的基本参量如频率、缺震、断层总面积、地震活动度等研究该区中等地震前的活动性异常特征，发现这些异常对1999－2001年首都圈地区的中等地震有较好的对应关系，当同时出现三项低值异常后，有震预后准确率较高。这些方法对首都圈地区中等地震的短期预报有重要作用，2001年12月28日滦县M14．2级地震就是用该方法在震前做了较成功的预测。由于参量指标与地震间的映震效果具有一定的阶段性和区域性，需要用发展变化的眼光，时刻跟踪并不断调整改进所使用的参量指标，才能更好地捕获地震信息。     

河南及邻区地震前中小地震活跃-平静的定量指标研究

以累计频度定量计算方法，分区讨论了河南及邻区1970年以来地震活动非线性度ZL值的时间进程曲线，系统计算了M≥5．0级地震前的ZL值，结果表明：开始出现活跃(平静)异常的时间集中在地震前1—2年，结束异常的时间在地震前一年内，集中于0—4个月。同时提出了异常的定量指标以及发震类型。     

岳阳及周边地区地震构造与地震活动性研究

陈述了岳阳市及周边地区的主要断裂带及其地震活动，归纳了发震部位。认为利用小震条带预报本区地震具有积极意义，并运用数理统计法预测了本区今后50及100年内的地震活动水平。     

云南地区1995～1996年三次强震爆发的共性研究

论述了在１９９５～１９９６年云南地区发生的孟连西南７．３级，武定６．５级和丽江７．０级３次强烈地震的孕育过程中，近源区中小地震的异常增频特点和临震前大气压力的突变共性，并据此提出，强震的发生乃是地壳内外力涨落地加剧的结果。     

An estimate of hypocentre location accuracy in a large network: possible implications for tectonic studies in Italy

Data recorded by the Italian Telemetered Seismic Network (ITSN) of the Istituto Nazionale di Geofisica (ING) have been widely used in recent years to image slab structures and to find evidence for active processes along the Italian Peninsula. However, the use of seismic data for geostructural purposes may be affected by the well-known trade-off between earthquake location and seismic-velocity parameters. Furthermore, the confidence ellipse predicted by standard procedures may be inadequate for the representation of the probable error of a computed localization. This paper evaluates the probable errors on the hypocentre determinations of the seismic events recorded by the ITSN, using a Monte Carlo method.
We compute synthetic arrival times using a 1-D velocity model appropriate as an average for the Italian area. The hypocentres used are all those recorded by the ITSN during the period January 1992 to March 1994 (1972 events). Station locations are those of the current ITSN configuration. The synthetic arrival times are perturbed with a Gaussian distribution of errors and input to ING's standard hypocentral location procedure, but using crustal velocities differing by 10 per cent from those used to generate them. Each simulation is repeated at least 30 times. Average absolute shifts of hypocentres are assessed in grid cells of linear dimension 33 km covering the whole Italian region.
For regions within the ITSN, shifts are typically 5–10 km in location and up to 20 km in depth. However, for offshore and coastal regions, they are much greater: 50 km or more in both location and depth (far exceeding the equivalent uncertainties quoted by ING bulletins). Possible consequences of this are highlighted by producing a cross-section of subcrustal hypocentres from the Adriatic to the Tyrrhenian Sea, where the large uncertainty in depth precludes any confident interpretation of dipping tectonic features.     

Multifractal analysis of the spatial distribution of earthquakes in southern Italy

We evaluate the complete spectrum of the generalized fractal dimension of the spatial pattern of microearthquakes in Southern Italy, revealing a multifractal distribution structure. Our analysis is focused on the dependence of the multifractal distribution on the size of the selected area and the kind of seismicity in the area. As the size of the window varies, we observe that the capacity, information and correlation dimensions vary significantly, while both d _∞ and d _−infin; remain unchanged within their errors limits. We interpret this result in terms of the observation that our data are mainly clustered around a linear fault (the Sisifo fault). When we restrict the selected windows around the fault, clustering around a line (the fault) is highlighted. The capacity dimension changes from about 1.8 to about 1.4 and the correlation dimension decreases because we observe in detail the clustering of the seismicity along the fault, which approximates the maximum intense clustering of the whole data set. Although our results are strongly influenced by the fact that the data are dominated by the epicentres located on the fault, we can conclude that multifractal analysis can be a very useful tool to discriminate the seismicity linked to a particular fault in a given area.