Slowness-domain kinematical characteristics for horizontally layered orthorhombic media. Part II: Pre-critical slowness match

Part II of this paper is a direct continuation of Part I, where we consider the same types of orthorhombic layered media and the same types of pure-mode and converted waves. Like in Part I, the approximations for the slowness-domain kinematical characteristics are obtained by combining power series coefficients in the vicinity of both the normal-incidence ray and an additional wide-angle ray. In Part I, the wide-angle ray was set to be the critical ray (‘critical slowness match’), whereas in Part II we consider a finite long offset associated with a given pre-critical ray (‘pre-critical slowness match’). Unlike the critical slowness match, the approximations in the pre-critical slowness match are valid only within the bounded slowness range; however, the accuracy within the defined range is higher. Moreover, for the pre-critical slowness match, there is no need to distinguish between the high-velocity layer and the other, low-velocity layers. The form of the approximations in both critical and pre-critical slowness matches is the same, where only the wide-angle power series coefficients are different. Comparing the approximated kinematical characteristics with those obtained by exact numerical ray tracing, we demonstrate high accuracy. Furthermore, we show that for all wave types, the accuracy of the pre-critical slowness match is essentially higher than that of the critical slowness match, even for matching slowness values close to the critical slowness. Both approaches can be valuable for implementation, depending on the target offset range and the nature of the subsurface model. The pre-critical slowness match is more accurate for simulating reflection data with conventional offsets. The critical slowness match can be attractive for models with a dominant high-velocity layer, for simulating, for example, refraction events with very long offsets.     

伊洛瓦底盆地热-沉降史模拟及构造-热演化特征

本文首先运用EASY% Ro反演法对伊洛瓦底盆地由北向南进行了热史的恢复,北部钦敦凹陷的平均古地温梯度为13.0~15.0 ℃/km,中部沙林凹陷的平均古地温梯度为18.0~22.0 ℃/km,南部三角洲凹陷的平均古地温梯度为33.0~37.0 ℃/km.从模拟结果可以看出,盆地由北向南地温梯度逐渐升高,生烃门限的深度由深变浅.然后模拟了盆地的构造沉降史.模拟结果表明,盆地具有幕式构造沉降特征,这反映了伊洛瓦底盆地可能处于弧间或弧后的构造背景.伊洛瓦底盆地北部和南部具有不同幕次的构造沉降史,北部在早始新世时期（53~51 Ma）经历了一幕拉伸过程,然后进入了热沉降期,并伴随局部的快速隆升;南部则经历了两幕拉张过程,分别是在早始新世时期（53~51 Ma）和中中新世时期（21~13 Ma）.盆地的这种南北构造沉降的差异很可能是造成盆地地温梯度北低南高的原因.     

浙江沿岸春季上升流的数值研究

采用三维斜压非线性数值模式ROMS(Regional Ocean Modeling System),在浙江沿岸特殊地形的基础上综合考虑风场、台湾暖流、潮流以及长江径流等可变因子,对春季浙江沿岸上升流进行了数值研究。研究结果表明,浙江沿岸春季存在上升流,且上升流以带状分布在浙江近岸海域,在渔山列岛附近(28.40°N,122.00°E)以及舟山群岛附近(30.70°N,122.60°~123.00°E)存在2个上升流中心。风、台湾暖流和潮动力是影响浙江沿岸春季上升流的重要因子,其中风和台湾暖流对浙江沿岸整条上升流带均有影响;潮动力仅对28.60°N 纬度线以北至舟山群岛附近的上升流以及2个上升流中心的强度存在影响;长江径流对2个上升流中心、舟山群岛及长江口附近的上升流有一定影响。通过对浙江沿岸海域流场的分析可以发现,舟山群岛附近底层各等深线上水体向岸运动的速度比周围海域的大,跨越底层各等深线向上涌升的趋势更明显,间接反映了舟山群岛附近的上升流强度相对较强。对于舟山群岛附近的上升流而言,30 m深度以浅的海域,科氏力、水平平流作用与垂直粘性力是上升流形成的主要动力, 30 m深度以深的海域,上升流形成的主要动力为科氏力与压强梯度力,此时水平平流作用和垂直粘性力对上升流的影响较弱。     

山区公路滑坡灾害问题及对策分析--以云南元(江)磨(黑)高速公路为例

随着西部公路建设的蓬勃发展,越来越多的高等级公路必须穿越山区丘陵地带,这样必然导致大量的公路滑坡的出现,危及公路的正常运行及安全。本文以云南元(江)磨(黑)高速公路为例,在分析公路沿线自然环境条件及地质构造条件等的基础上,研究公路沿线的地质灾害的特点及危害,重点研究公路沿线所发育的滑坡的分布特征、危害、形成机制及整治对策和措施。研究表明,山区公路滑坡不仅与滑坡发育的工程地质条件有关,而且与工程设计方案及公路建设过程施工方案等人为因素有关。     

顺层岩质边坡开挖模型试验及稳定性影响因素分析

用地质力学模型试验方法,对顺层岩质边坡进行开挖破坏试验。同时在有限元分析的基础上,采用多层结构模型对顺层岩质路堑边坡稳定性的影响因素进行了分析。指出当边坡走向与岩层走向夹角超过30后,边坡发生顺层滑动破坏的可能性很小,基本上可不视为顺层边坡。同时也阐明了岩层倾角以及结构面抗剪强度对顺层岩质边坡稳定性的影响。文中结论可为顺层岩质边坡的科研、设计和施工提供参考。     

Preliminary estimation of the tsunami hazards associated with the Makran subduction zone at the northwestern Indian Ocean

We present a preliminary estimation of tsunami hazard associated with the Makran subduction zone (MSZ) at the northwestern Indian Ocean. Makran is one of the two main tsunamigenic zones in the Indian Ocean, which has produced some tsunamis in the past. Northwestern Indian Ocean remains one of the least studied regions in the world in terms of tsunami hazard assessment. Hence, a scenario-based method is employed to provide an estimation of tsunami hazard in this region for the first time. The numerical modeling of tsunami is verified using historical observations of the 1945 Makran tsunami. Then, a number of tsunamis each resulting from a 1945-type earthquake (M _w 8.1) and spaced evenly along the MSZ are simulated. The results indicate that by moving a 1945-type earthquake along the MSZ, the southern coasts of Iran and Pakistan will experience the largest waves with heights of between 5 and 7 m, depending on the location of the source. The tsunami will reach a height of about 5 m and 2 m in northern coast of Oman and eastern coast of the United Arab Emirates, respectively.     

Robustness of the holistic seismic risk evaluation in urban centers using the USRi

The Urban Seismic Risk index (USRi) published in a previous article (Carreño et al., Nat Hazards 40:137–172, 2007) is a composite indicator that measures risk from an integrated perspective and guides decision-making for identifying the main interdisciplinary factors of vulnerability to be reduced or intervened. The first step of the method is the evaluation of the potential physical damage (hard approach) as a result of the convolution of the seismic hazard with the physical vulnerability of buildings and infrastructure. Subsequently, a set of social context conditions that aggravate the physical effects is also considered (soft approach). According to this procedure, the physical risk index is evaluated for each unit of analysis from existing loss scenarios, whereas the total risk index is obtained by multiplying the former index by an impact factor using an aggravating coefficient, based on variables associated with the socio-economic conditions of each unit of analysis. The USRi has been developed using the underlying holistic and multi-hazard approach of the Urban Risk Index framework proposed for the evaluation of disaster risk in different megacities worldwide. This article presents the sensitivity analysis of the index to different parameters such as input data, weights and transformation functions used for the scaling or normalization of variables. This analysis has been performed using the Monte Carlo simulation to validate the robustness of this composite indicator, understanding as robustness how the cities maintain the ranking as well as predefined risk level ranges, when compared with the deterministic results of risk. Results are shown for different cities of the world.