comparison of locations of rushan earthquake swarm from large and small network

A notable swarm occurred in Rushan, Shandong Peninsula and its activities continue since Oct. 2013 till now. Up to Sept. 30, 2014, more than 7 000 events have been recorded, in which locatable shocks exceed 2000, and 18 events with M_L≥3.0. The swarm is rarely seen in East China for its extraordinary duration time and surprising high frequency of aftershocks. 18 temporary seismometers have been deployed around the swarm since May 6, 2014, and composed a seismic array for monitoring the swarm activities. Based on data from permanent networks and temporary array, we relocated the earthquake sequence by using hypoDD method. It has been shown that, there is obvious difference between permanent network results and temporary array results. The permanent network of Shandong has a relative large coverage gap(more than 200°)for this swarm. Its location results therefore should not be reliable. There are maybe other errors in the permanent network result due to some problems in the raw data, such as too few stations for most locatable events(3 stations), and relative lower proportion of located events in final result(74.3%, while 95.1% in temporary array result). It can be found by comparing location results from permanent network and temporary array that, using temporary array's data can improve the location accuracy significantly. The results of temporary array are: aftershocks distribution of Rushan swarm is in NWW direction, the dip-direction of fitted fault plane is SW, and the strike and dip angle agree with focal mechanism of the mainshock. Focal depths of aftershocks are at 4.5～8km; the swarm is restricted in a small area about 3km×3km×1km, and has some characteristics such as clustering, staged activities, and etc; the aftershock activities are in accord with crack growth behavior pattern, hence we deduced that there may be fluid intrusion in source area. Finally, we discussed the seismogenic structures and active mechanisms of this swarm combined with relative geologic knowledge. We draw some conclusions as follows: 1)Rushan swarm probably occurred at the boundary of rock bodies of Duogu Mountain and Haiyangsuo super-unit; 2)The seismogenic structure is a blind fault, which should be a part of adjacent Heishankuang-Jilincun Fault, or might be a new fault at rock body boundaries; 3)Rushan swarm might be an evidence for the existence of the disputed Shidao Fault.     

吉林松原5.8级震群震相特征

选取2013年松原5.8级震群23个M_S≥3.0地震事件,通过地震波的振动持续时间、速度、周期、振幅等,进行震相识别,综合分析发现：该区域土层较厚,且震源相对较浅,面波较发育,波列的振动持续时间较长;地震震相主要以Pn、Sn、Pg、Sg、PmP、SmS为主：其中PmP、SmS震相在震中距70—110 km范围内较易识别;Pn、Sn震相在震中距150 km以上可较清晰的识别,随着震中距增大,Sn震相在震中距350 km以上将不易识别。     

团体粒子群与最大能量串行融合算法转换波静校正

估计转换波的静校正量是一个复杂的非线性问题,常规的线性静校正方法无法取得好的效果.粒子群算法是一种很好的非线性全局最优化方法,但其缺点是"早熟"现象严重.最大能量法是一种常规求取静校正量的方法,局部寻优能力强且收敛速度快是其优点,但是当地震记录含有大的静校正量时易收敛于局部极值.本文在标准粒子群算法的基础上发展出了一种改进的粒子群算法:团体粒子群算法.并且通过对Rastrigin函数的寻优实验证明了其全局寻优能力优于标准粒子群算法.同时为了解决转换波静校正问题串行融合了团体粒子群算法和最大能量法.最后,建立了含一个水平反射层的模型并合成地震记录,加入随机值作为检波点静校正量.对合成的地震数据分别利用团体粒子群和最大能量的串行融合算法、标准粒子群算法和最大能量法求取静校正量并进行静校正.结果证明串行融合算法得到的静校正量与理论值误差很小,静校正后的叠加剖面连续性较好.     

长岛震群震源谱参数反演和震源参数特征

采用高频截止(High-Cut)震源模型,以均方根误差最小原则稳健地求解震源谱参数,并由此推算震源尺度和静态应力降。实际应用显示,该模型的理论谱对观测谱有很好的拟合,可明显改善拐角频率识别准确度。计算了长岛震群内71次M_L≥2.5地震事件的震源参数,结果表明：(1)拐角频率处于2～10 Hz范围,与震级大小存在一定的相关性,截止频率范围处于10～30 Hz之间,与地震大小的相关性不明显;(2)地震矩M₀分布在10¹²～10¹⁴ N·m,与震级M_L存在正相关关系：logM₀=0.977M_L+10.186;M_L与矩震级M_W之间的关系为：M_W=0.651M_L+0.766;(3)根据相对应力降时域演化发现,自2017年3月3日M_L4.5地震之后应力快速释放,应力降水平在均值附近波动,而且多数M_L≥3.5地震发生于...     

Earthquake prediction in northeast India—A review

A multidimensional geophysical approach is made to study earthquake precursors in northeast India. Resistivity, gravity and seismological precursor anomalies for the two recent damaging earthquakes, one on December 30, 1984, magnitude 5.8, and the other on August 6, 1988, magnitude 7.5, are very encouraging to intensify these studies in the region. Improved methods of magnetic and geohydrological precursor studies are suggested.     

云南地区地震序列分类的定量讨论

本文通过对云南地区１９６５提以来１１６次大小地震序列的清理和归纳，引入了地震序列分类的时空强定量尺度，提出；表明震源区进人失稳状态的前震序列主要发生在主震前１０天，前震序列的最大震级５．５级；震群型地震序列在时间、地点上的确定应与震级有关，在时间上以同在地震孕期内为宜，７、６、５级分别不超过１年、半年、３个月，在地点上以同在地震孕震区内为２，７、６、５级分别不超过１００、５０、３０公里，震级上以小子等于０．５级为宜；主余震地震序列和孤立型地震序列的最大震级差分别是２．９级和３．３级     

Experimental assessment and prediction of temporal scour depth around a spur dike

Spur dikes are river training structures that have been extensively used worldwide for towards enhancing flood control and the stability of embankments and riverbanks.However,scour around spur dikes can be a major problem affecting their stability and hydraulic performance.The precise computation of temporal scour depth at spur dikes is very important for the design of economical and safe spur dikes.This study focuses on experimentally assessing the temporal variation of scour depth around a vertical wall spur dike and identifying the parameters,which mostly influence spur dike performance for a channel bed surface comprised of sand-gravel mixtures.In the current study,the authors did physical experiments in a flume based study to obtain new data,aimed at deriving a new predictive model for spur dike scour and comparing its performance to others found in the literature.It was found that the dimensionless temporal scour depth variation increases with an increase in(i)the threshold velocity ratio,(ii)the densimetric Froude number of the bed surface sediment mixture,(iii)the flow shallowness(defined as the ratio of the approach flow depth,y,to the spur dike’s transverse length,l),and(iv)the flow depth-particle size ratio.It is also concluded that the temporal scour depth variation in the sediment mixture is influenced by the non-uniformity of sediment and decreases with an increase in the non-uniformity of the sediment mixture.A new mathematical model is derived for the estimation of temporal scour depths in sand-gravel sediment mixtures.The proposed equation has been calibrated and validated with the experimental data,demonstrating a good predictive capacity for the estimation of temporal scour depth evolution.     

Sensitivity test of the geodetic network in Las Cañadas Caldera, Tenerife, for volcano monitoring

A 17-benchmark geodetic network in the volcanic area of the Teide Caldera, Canary Islands has been utilised several times since 1982 to detect crustal movements associated with volcanic activity within the network, as well as a procedure for solving configuration problems. The network is located on the mid-western side of the Caldera, where there are two different morphological zones that both have benchmarks. The authors performed a sensitivity test of this geodetic network for volcano monitoring purposes. To do so, we use a deformation model to calculate surface displacement caused by a dike intrusion in a homogenous half space. The depth and location of dike are changed to study the variation of the effects produced (displacements). The size and location of the intruded dike are found to play a major role in determining both the displacement pattern and magnitude. When the dike is close to the surface, there is an inversion of the surface displacement pattern and very large surface displacement at certain benchmarks. Such phenomena can serve as precursors of such dike eruptions. Our study show a clear need to extend the existing geodetic network to cover the full island for volcano monitoring purposes.     

武当地块基性岩墙群的Sm-Nd定年及其相关问题讨论

武当地块是卷入南秦岭构造带的前震旦纪基底岩块,该基底岩块中广泛发育变形变质的基性岩墙群,获得岩墙群的ＳｍＮｄ全岩等时年龄为ｔ＝７８２±１６４（２σ）Ｍａ,ＩＮｄ＝０．５１１７１±１６（２σ）,εＮｄ（ｔ）＝＋１．５±１．０。据此并综合区域地质实际,对南秦岭元古代古陆块的存在及其裂解等相关问题进行了讨论。     

基于免疫粒子群优化算法的影像纹理分类

粒子群优化算法是基于群智能的随机全局优化方法,它源于对鸟群简化社会系统的模拟。为了提高标准粒子群优化算法的收敛性能,将生物免疫系统的记忆能力和多样性引入标准粒子群优化算法,提出一种免疫粒子群优化算法。在提取纹理样本Laws纹理能量模板特征、小波特征等纹理特征的基础上,提出针对分类问题的粒子表达方法和群体寻优策略,实现了基于免疫粒子群算法的纹理分类。实验结果表明,与标准粒子群优化算法相比,免疫粒子群优化算法在获取训练样本类别中心时具有较好的收敛性能,并且基于该算法的影像纹理分类具有较高的分类精度。