2014年于田M_S7.3地震序列的频谱特征分析及其前震识别

利用新疆区域数字地震台网的波形资料,对2014年2月12日于田MS7.3地震前震和余震序列的频谱特征进行了分析。基于Brune模型,对观测记录进行仪器响应、传播路径和场地响应的校正,使用遗传算法对于田地震序列103次ML≥3.0地震的震源谱进行了反演计算。结果表明:1前震序列的拐角频率相对偏低,扣除地震矩影响后,余震的拐角频率有明显增高的趋势;2距离震中最近的YUT台资料分析显示,于田地震几次前震活动的台站观测谱相对较为独立,与主震以及彼此之间的相关系数均不高;3序列事件和主震的台站观测谱相关系数的变化反映了序列发展的动态破裂过程,相关系数较低的事件基本上偏离了主震破裂区而发生在分支断裂上;4前震的震源谱中存在异常的高频成份。     

济阳坳陷特低渗透油藏地质多因素综合定量分类评价

在定量表征特低渗透油藏开发效果及地质特征的基础上,探讨特低渗透油藏开发效果主控地质因素。应用灰色关联分析法对济阳坳陷特低渗透油藏开展地质多因素综合定量分类评价,形成了一套以开发效果表征特低渗透油藏综合特征、从单井到区块基于灰色关联分析的特低渗透油藏地质多因素综合定量分类评价方法。研究结果表明,济阳坳陷特低渗透油藏主要为滩坝砂、浊积岩、砂砾岩沉积,具有埋藏深度大、粒度细、分选较好、孔喉微细、单层厚度薄、分布零散、比储量丰度低、流动性差的地质特点。定义累积产油差表征开发效果以反映特低渗透油藏综合特征,其主控地质因素为油层厚度层数比、砂岩厚度层数比、平均粒径、采油段中深、渗透率、孔隙度、比储量丰度。通过地质多因素综合定量分类评价,将济阳坳陷特低渗透油藏分为3类：Ⅰ类综合评价分数大于0.6,累积产油差大于4 500,开发效果最好;Ⅱ类综合评价分数0.4~0.6,累积产油差-2 500到4 500,开发效果中等;Ⅲ类综合评价分数小于0.4,累积产油差小于-2 500,开发效果差。利用不同区块已有开发数据检验,分类评价结果与实际生产情况一致性良好。Ⅰ类、Ⅱ类特低渗透油藏应该作为现阶段特低渗透油藏直井、水平井优先开发的对象。     

Verifying a biotope classification using benthic communities – An analysis towards the implementation of the European Marine Strategy Framework Directive

The HELCOM Red List biotopes project proposed a Baltic Sea wide classification consisting of six levels: The HELCOM Underwater biotopes/habitats classification system (HELCOM HUB). We present a case study from the south-western Baltic Sea where we tested the applicability of this system. More than 500 sampling stations were analyzed regarding macrozoobenthic communities and their linkage to environmental parameters. Based on the analyses of biotic and abiotic data, 21 groups were assigned to 13 biotopes of the classification. For some biotopes varying states of communities were recognized. Even though not all abiotic parameters are considered directly in the hierarchy of the classification in general, all soft-bottom communities could be allocated to a corresponding biotope. The application of the HELCOM HUB for the south-western Baltic Sea is feasible, in regard to the implementation of the European Marine Strategy Framework Directive as well as the Baltic Sea Action Plan.     

Influence of river channel morphology and bank characteristics on water surface boundary delineation using high‐resolution passive remote sensing and template matching

Accurate mapping of water surface boundaries in rivers is an important step for monitoring water stages, estimating discharge, flood extent, and geomorphic response to changing hydrologic conditions, and assessing riverine habitat. Nonetheless, it is a challenging task in spatially and spectrally heterogeneous river environments, commonly characterized by high spatiotemporal variations in morphology, bed material, and bank cover. In this study, we investigate the influence of channel morphology and bank characteristics on the delineation of water surface boundaries in rivers using high spatial resolution passive remote sensing and a template‐matching (object‐based) algorithm, and compare its efficacy with that of Support Vector Machine (SVM) (pixel‐based) algorithm. We perform a detailed quantitative evaluation of boundary‐delineation accuracy using spatially explicit error maps in tandem with the spatial maps of geomorphic and bank classes. Results show that template matching is more successful than SVM in delineating water surface boundaries in river sections with spatially challenging geomorphic landforms (e.g. sediment bar structures, partially submerged sediment deposits) and shallow water conditions. However, overall delineation accuracy by SVM is higher than that of template matching (without iterative hierarchical learning). Vegetation and water indices, especially when combined with texture information, improve the accuracy of template matching, for example, in river sections with overhanging trees and shadows – the two most problematic conditions in water surface boundary delineation. By identifying the influence of channel morphology and bank characteristics on water surface boundary mapping, this study helps determine river sections with higher uncertainty in delineation. In turn, the most suitable methods and data sets can be selectively utilized to improve geomorphic/hydraulic characterization. The methodology developed here can also be applied to similar studies on other geomorphic landforms including floodplains, wetlands, lakes, and coastlines. Copyright © 2014 John Wiley & Sons, Ltd.     

云南鲁甸M_S6.5地震余震重定位及其发震构造

整合了鲁甸震区周边的云南省地震台网、昭通市地震台网、巧家台阵,以及流动台站2个月的震相观测数据,对鲁甸地震序列进行了重新定位,得到了1 750个地震的震源参数。重定位结果显示,余震有2个优势分布方向,分别为SE向和SW向,具有不对称的共轭分布特征。2个余震条带的展布长度相当,约为16km,夹角约100°。余震分布显示鲁甸地震的发震断层为高倾角的走滑断层。主震位于2个余震条带中间略偏西南的位置,早期余震主要沿NW-SE向垂直于昭通-鲁甸断裂分布,主震西南侧的余震可能为后期触发的。根据余震分布与周边断层的关系、主震震源机制、烈度分布的长轴方位,以及滑坡分布等资料,认为鲁甸地震的发震断层为NW向的包谷垴-小河断裂。包谷垴-小河断裂南北两侧无论是在地震活动、深部速度结构,还是块体运动方向和速率方面都存在显著差异,断裂北侧的高速异常可能是阻止余震向北继续扩展的主要原因。     

2014年新疆于田MS7.3地震序列的深度测定

An Ms7. 3 earthquake occurred on February 12, 2014 in Yutian County, Xinjiang Uygur Autonomous Region, which was followed by a series of aftershocks. This paper calculates the depth of the Ms7. 3 earthquake sequence by using the deterministic approach with the seismic phase data from the catalog database of the China Earthquake Networks Center, combined with original seismic wave records from the Digital Seismic Network Center of the Earthquake Administration of Xinjiang Uygur Autonomous Region.     

基于最邻近事件距离算法的丛集地震识别方法及应用

引入Zaliapin等发展的基于最邻近事件距离算法的丛集地震识别方法,首先使用不同类型的数据对方法进行检验,然后对胶东半岛和沂沭带的区域小震活动进行分析。结果表明,1996年以来沂沭带的小震活动近似满足泊松随机过程,主要是背景地震活动;而胶东半岛地区小震则存在明显的成丛活动。最后将该方法应用到1976年唐山MS7.8地震序列,通过序列发展不同时段的对比分析,结果表明,随着时间的推移,唐山地震序列中受此前发生的主震和较大余震影响作用的事件越来越少,序列的丛集性明显减弱,更多的小震或许可视为反映区域应力状态的背景地震。     

The 20 April 2013 Lushan,Sichuan, mainshock,and its aftershock sequence: tectonic implications

Using the double-difference relocation algorithm, we relocated the 20 April 2013 Lushan, Sichuan, earthquake (M _S 7.0), and its 4,567 aftershocks recorded during the period between 20 April and May 3, 2013. Our results showed that most aftershocks are relocated between 10 and 20 km depths, but some large aftershocks were relocated around 30 km depth and small events extended upward near the surface. Vertical cross sections illustrate a shovel-shaped fault plane with a variable dip angle from the southwest to northeast along the fault. Furthermore, the dip angle of the fault plane is smaller around the mainshock than that in the surrounding areas along the fault. These results suggest that it may be easy to generate the strong earthquake in the place having a small dip angle of the fault, which is somewhat similar to the genesis of the 2008 Wenchuan earthquake. The Lushan mainshock is underlain by the seismically anomalous layers with low-V_P, low-V_S, and high-Poisson’s ratio anomalies, possibly suggesting that the fluid-filled fractured rock matrices might significantly reduce the effective normal stress on the fault plane to bring the brittle failure. The seismic gap between Lushan and Wenchuan aftershocks is suspected to be vulnerable to future seismic risks at greater depths, if any.     

基于数字钻进技术和量子遗传-径向基函数神经网络的围岩类别超前识别技术研究

围岩类别超前分类是隧道施工过程中必须开展的一项工作,其直接关系到后续的开挖及施工支护方案。为有效地进行隧道围岩类别超前分类,提出了基于数字钻进技术和量子遗传（QGA）-径向基函数（RBF）神经网络的围岩类别超前分类方法。以数字钻进技术为基础,从钻进参数中提取有用信息,构建围岩类别超前分类指标体系。采用量子计算原理对遗传算法进行改进,通过量子位编码和量子旋转门更新种群,以此来确定RBF神经网络的参数,建立了基于QGA-RBF神经网络的围岩类别超前识别系统。最后将该方法应用于青岛胶州湾海底隧道的围岩类别超前识别中,结果表明,该方法具有较高的准确性,其结果为围岩类别超前分类提供了一种新思路。     

华南地区晚三叠世含煤岩系层序—古地理

在对露头及钻孔剖面沉积特征研究的基础上,建立了华南地区晚三叠世含煤岩系层序地层格架,恢复了基于三级层序的岩相古地理,并分析了聚煤规律。根据岩相特征及岩相组合类型,在区内晚三叠世含煤地层中识别出陆相和海陆过渡相两大沉积类型,并可进一步识别出冲积扇、河流(包括辫状河和曲流河)、三角洲、湖泊、潮坪—潟湖、滨海平原和滨浅海等7种沉积类型。陆相沉积主要发育在上扬子地区的四川盆地;海陆过渡相沉积主要发育在东南部湘赣粤滨浅海。在晚三叠世含煤岩系中识别出区域性不整合面和构造应力转换面、砂砾岩体底部冲刷面和岩性突变面等类型的层序界面,将含煤岩系划分为5个三级层序。以三级层序为古地理作图单元,恢复了研究区的古地理格局。由煤层厚度与岩相古地理平面展布规律可知,最有利的成煤环境为三角洲沉积体系,其次为河流、潮坪—潟湖沉积体系,聚煤中心主要分部在四川盆地的乐威煤田以及华蓥山煤田、湘赣粤滨浅海地区的湘东南至赣西萍乡一带。