Retrogressive Microstructures of the Eclogites from the Northern Dabie Mountains, Central China： Evidence for Rapid Exhumation

The ultrahigh-pressure eclogites from the northern Dabie Mountains in central China occurred as tectonic lens or blocks within granitic gneisses or meta-peridotites. Petrologic studies suggest that the studied eclogites experienced strongly retrogressive metamorphism and produced a series of characteristic retrogressive microstructures. The retrograde structures mainly include: (1) oriented needle mineral exsolution, e. g. , quartz needles in Na-clinopyroxene and rotile, clinopyroxene and apatite exsolution in garnet formed under decreasing pressure conditions during exhumation; (2) symplectite, especially, two generations of symplectites developed outside the garnet grains, which are called ““double symplectite““ here; (3) compositional zoning of minerals such as garnet and clinopyroxene; (4) minerals with a reaction rim or retrograde rim, e.g. , omphacite with a diopside rim, diopside with an amphibole rim and rutile with a rim of ilmenite. These retrograde textures, especially mineral zoning and symplectite, provide important petrologic evidence for the exhumation process and uplift of high-grade metamorphic rocks such as eclogite in the northern Dabie Mountains, indicating a rapid exhumation process.     

人工快渗污水处理系统水力负荷周期的设计

人工快渗(CRI系统)源自快速渗滤污水土地处理系统(RI系统),并沿用了其中的干湿交替工作方式,因此, 水力负荷周期同样也成为CRI系统的重要设计参数,也是系统设计的一大难点。目前CRI系统水力负荷周期的设计,不仅需要根据已有的经验,而且还要通过一段调试运行才能确定,设计过程中缺乏理论依据和设计原则。为此,笔者研究了CRI与RI系统两者之间的差异,充分考虑水力负荷周期调整对CRI系统运行的各方面影响,分别从防止NH4 N穿透和保证系统NH4 N充分硝化两个角度,提出了水力负荷周期中淹水时间和落干时间确定新方法,并通过松岗试验实例进行了初步验证,为今后CRI系统水力负荷周期的设计提供了有效的理论依据和设计原则。     

硅灰石的X射线荧光光谱分析

将X射线荧光光谱法应用于硅灰石中SiO2、CaO、Al2O3、Fe2O3、P2O5、MgO、K2O、TiO2、MnO的同时快速测定。试样和混合熔剂的质量比为1：10，在此稀释比时试样在熔剂中的分散度（浓度）适当，可适应各个组分的测定。熔融法分解试样有效地消除了试样的粒度效应和矿物效应。采用理论α系数校正法克服了基体吸收及增强效应。用国家标准物质和样品验证了方法的精密度和准确度，其结果与化学分析值相符。     

厚煤层快速回采切顶卸压无煤柱自成巷工程试验

基于切顶卸压自动成巷技术原理,分析了沿空切顶巷道顶板结构演化过程,得出厚煤层快速回采切顶成巷围岩应力、位移传递机制,形成了人为主动构建“基本顶上位岩层-采空区碎胀矸石-巷道切顶短臂梁”围岩稳定结构的控制思路。提出厚煤层快速回采沿空切顶巷道围岩协同控制体系,以“恒阻大变形锚索+墩式单元支架”控制顶板,以“滑移式让位护帮结构+自移式动压防冲结构+波浪式多阻护帮锚杆”控制帮部,形成了应对顶板结构位态、碎石运动状态变化的针对性控制技术。以柠条塔煤矿S1201胶运顺槽为工程背景,完成了厚煤层快速回采条件下切顶卸压无煤柱自成巷现场工业性试验。研究表明,采高增大、采速加快后,基本顶上位岩层易引发少量不宜强制控制的回转变形,支护结构可控让位是构建围岩稳定结构的有效途径之一;巷内压力与采空区矸石运动状态密切相关,增强预裂效果有利于减少动压冲击;成巷过程中巷道顶压首先达到恒阻状态,而后位移趋于稳定,最后形成围岩稳定结构。所设计的配套支护结构可有效协同围岩运动,留巷效果良好。     

云辐射强迫效应对热带气旋发展和结构的影响

利用高分辨率中尺度WRF-ARW模式,进行云辐射强迫效应对热带气旋(Tropical Cyclone,TC)发展和结构影响的敏感性试验研究。结果表明,云辐射强迫效应主要通过改变TC云区的辐射分布影响对流活动,进而影响TC的发展和结构。在TC发展阶段,TC内区对流云区云顶的强烈辐射冷却作用和云层内部的辐射增温过程降低了TC中上层的静力稳定度,中下层的变化相对不明显。总体上考虑云辐射效应的试验更易激发出更多更强的对流活动,有利于TC的发展及TC尺度的增大。鉴于云辐射强迫对TC的影响,在TC数值预报中需要更加重视云辐射强迫效应。     

汶川8.0级大地震的长波辐射征象

文中分析了汶川8.0级地震发生前, 卫星遥感射出长波辐射(OLR)信息场的中期(年)、 短期(月)和临震(日)时空分布及变化特征。 通过计算发现该地震震中区附近的OLR信息有异常增强的现象, 这种现象可作为诊断强震蕴育地区的一种指征。     

汶川地震(Ms 8.0)地表破裂及其同震右旋斜向逆冲作用

2008年5月12日14时28分,青藏高原东缘龙门山地区发生了震惊世界的汶川地震（MS 8.0）,地震不仅造成巨大的人员伤亡和财产损失,并形成了迄今为止空间上分布最为复杂、长度最大的逆冲型同震地表破裂带。通过多次野外考查表明,汶川地震（MS 8.0）在龙门山断裂带上至少使两条NE走向、倾向NW的映秀-北川断裂和灌县-安县断裂同时发生地表破裂,并沿映秀-北川断裂产生的地表破裂带长度约275 km,以逆冲运动伴随右旋走滑为其破裂特征,最大垂直位移量约11 m,最大右旋走滑位移量至少约12 m;沿灌县-安县断裂产生的地表破裂带长度约80 km,表现为纯逆冲运动的破裂特征,最大垂直位移量约4 m;另外发育一条长约6 km呈NW走向连接于映秀-北川破裂带和汉旺破裂带的小鱼洞破裂带,以左旋走滑兼有逆冲运动为特征。地表破裂基本沿袭早先活动断裂带上,并使早先抬高的地貌更加抬高,表明龙门山地区地震在同一断裂带上重复发生过,并且无数次地震活动（包括类似汶川MS 8.0地震的强震）的累积,逐渐形成了现今的龙门山。根据同震断裂面以及断裂面上的擦痕分析表明,汶川地震是由两次破裂事件叠加而成,初期破裂以逆冲运动为主,后期破裂以右旋走滑为主,这种破裂过程与地震波数据反演结果（陈运泰等,2008;Ji, 2008;王为民等,2008）一致。在地表破裂带南段（映秀—清平段）叠加了两次不同性质的破裂过程,北段（北川—南坝段）只反映了第二次破裂事件的过程。利用长期滑移速率与汶川地震同震位移对比,估算出在龙门山断裂带上类似汶川地震（MS 8.0）的强震复发周期为3000~6000 a。通过对比研究,西昆仑山、阿尔金山和东昆仑山与龙门山具有很相似的转换挤压构造特征,斜向逆冲作用是青藏高原周缘山脉快速崛起的主要机制。     

阿尔金断裂带8Ma左右的快速走滑及其地质意义

阿尔金断裂带的走滑变形历史与青藏高原的抬升、变形密切相关。阿尔金断裂中段旁侧岩体中磷灰石的裂变径迹测试年龄结果集中在 8Ma±。区域资料显示 ,沿阿尔金主断裂带旁侧普遍存在 8Ma±的磷灰石裂变径迹年龄。为此推测 ,阿尔金断裂带在 8Ma±经历了一期快速的走滑变形事件。结合青藏高原抬升、变形研究资料 ,表明 8Ma±是青藏高原抬升、变形等重要构造事件的发生时间。     

一次罕见冬季强浓雾天气成因分析

利用加密观测资料和NCEP/NCAR 1°×1°的6 h再分析资料,对2006年12月25~27日发生在我国中东部地区的一次罕见强浓雾天气过程从大尺度背景、动力和热力机制等方面进行了诊断分析。结果表明:①本次过程大雾发生阶段近地面风速很小,在0.3~2.9 m/s之间变化;浓雾发生阶段风速在0.3~2.4 m/s之间变化;15 m能见度维持阶段风速在0.8~1.1 m/s之间变化;②虽然浓雾发生前的很长一段时间内水汽条件差,而且后期西风槽影响时也无降水,但是槽前西南气流的持续水汽输送使得强浓雾形成所必须的水汽条件得到满足;③在大雾发生前,稳定层结逐渐建立并在大雾期间稳定维持,稳定层结的建立和维持对浓雾的形成、持续有重要作用;日出后首先在较高层出现不稳定层结,继而下传到底层,稳定层结被破坏,大雾减轻或消散;④第1阶段(25日夜里至26日上午)强浓雾出现前,能见度出现多次急速大幅振荡,在第2阶段(26日傍晚至27日上午)则未出现类似现象。     

Machine learning for accelerating 2D flood models: Potential and challenges

Two-dimensional hydrodynamic models numerically solve full Shallow Water Equations (SWEs). Despite their high accuracy, these models have long simulation run times and therefore are of limited use for exploratory or real-time flood predictions. We investigated the possibility of improving flood modelling speed using Machine Learning (ML). We propose a new method that replaces the computationally expensive parts of the hydrodynamic models with simple and efficient data-driven approximations. Our hypothesis is that by integrating ML with physics-based numerical methods, we can achieve improved generalization performance: that is, the trained model for one case study can be used in other studies without the need for new training. We tested two ML approaches: for the first, we integrated curve fitting, and, for the second, artificial neural networks (ANN) with a finite volume scheme to solve the local inertial approximation of the SWEs. The data-driven models approximated the Momentum Equation, which explicitly solved the time derivative of flow rates. Water depths were then updated by applying a water balance equation. We also tested two different training datasets: the simulated dataset, generated from the results of hydrodynamic model, and the random dataset, generated by directly solving the momentum equation on randomly sampled input data. Various combinations of input features, for example, water slope and depth, were explored. The proposed models were trained in a small hypothetical case and tested in a different hypothetical and in two real case studies. Results showed that the curve-fitting method can be implemented successfully, given sufficient training and input data. The ANN model trained with a random dataset was substantially more accurate than that of the model trained with the simulated dataset. However, it was not successful in the real case studies. The curve-fitting method resulted in better generalization performance and increased the simulation speed of the local inertial model by 23%. Future research should test the performance of ML in terms of an increase in stable time step size and approximation of the full SWEs.