山西保德—静乐地区新近纪地层时代讨论

通过对山西保德-静乐地区新近纪地层的野外地质调查和实测剖面,对分布于该区的新近纪地层进行了详细的研究和划分,获得了新的ESR和古地磁测年数据,并结合生物地层序列确定了该区新近系地层的顺序为：新近系上新统静乐组（N2j）、新近系中新统上部保德组（N1^26）和中新统下部芦子沟组（N1^1l）。     

西藏札达盆地控盆断裂有限元数值模拟

札达盆地是喜马拉雅构造带中的一个山间断陷盆地,其演化过程与盆地两侧的控盆构造密切相关。对控盆断裂的构造应力场进行模拟计算,将有助于进一步深化对本区构造控盆的认识。因此,在对盆地构造地质进行详细调查的基础上,结合本区的深部地质与地球物理资料,对札达盆地控盆断裂的构造应力场进行了模拟。计算结果表明,札达盆地的演化明显受盆地两侧边界断裂的控制,札达盆地是在整体南北向挤压应力的作用下,不同块体差异隆升作用的结果。其南侧的控盆断裂为北倾的正断层,北侧的控盆断裂为南倾的逆断层,二者共同形成了南降北升的翘板式断陷盆地运动过程,是喜马拉雅地块在陆内汇聚挤压构造环境中构造应力场调整的一种方式。     

基于ICCP算法的重力辅助惯性导航

迭代最近等值线算法(ICCP)是一种重要的匹配导航算法,文中首先介绍ICCP算法的基本原理,随后在0.2′×0.2′重力异常数据库的基础上,利用ICCP算法进行仿真计算得到最佳匹配位置。最后为了验证匹配位置是否可用于修正惯导误差,提出将匹配位置误差作为观测量,用卡尔曼滤波对惯导系统误差进行最优估计。由最后的仿真结果可以看出,ICCP算法可有效抑制惯导纬度误差的增长,且最大纬度误差不超过2,′以匹配位置误差作为观测量可以用来估计惯导方位误差角。     

辽宁西部地区铁矿类型及勘查开发方向探讨

辽宁境内下辽河凹陷以西地区,是晋冀铁矿集中区的东延部分,铁矿资源丰富.根据铁矿成矿地质条件、成矿时代及演化特点,结合近几年勘查开发工作的新进展,认为该地区铁矿床的类型有沉积变质型、沉积型、岩浆型、矽卡岩型和热液型.在寒武纪页岩之下有可能寻找到沉积型铁矿床.在辽西与河北大庙之间的华北地台北缘隆起区,岩浆型铁矿具有较大的找矿潜力,重点靶区为北票松台沟地区,朝阳崔家店地区,建平深井-青风山地区.而沉积变质型铁矿在北票宝国-牛虎头沟,朝阳老西沟,建平大南沟-锅底山、卧龙岗,凌源黑沟等地区的建平岩群中应具有找矿潜力.     

基于NSWT模极大值的PSF估计

大气湍流扰动严重影响了空间目标的观测、成像和识别。在目标图像的重建过程中,对成像系统和大气条件先验知识的缺乏使PSF（Point Spread Function）的估计成为解决病态问题的重要内容。在NSWT（NonSubsampled Wavelet Transform）和Fried参数的理论基础上,根据不同尺度下小波变换模极大值和高斯点扩散函数方差的关系,结合Fried参数提出一种新的估计方法,即基于非抽取小波变换模极大值的PSF估计算法。实验结果证明,该估计算法是确实可行的,重建的PSF能够有效地改善因受大气湍流影响的图像解卷积的效果。     

未来气候变化对云南柠檬气候适宜性的影响

气候变化影响柠檬生长、产量和品质，本文利用云南省125个国家气象站1981—2018年,月平均气温、月平均降水量、月日照时数，进行基准年代下云南柠檬气候适宜性分区；采用RCP2.6、RCP4.5、RCP8.5气候情景模式，研究2021—2030年、2031—2040年、2041—2050年云南柠檬气候适宜区的变化，探讨未来气候变化背景下，云南柠檬气候适宜性变化，以期对柠檬产业布局、规划、引种提供科学的指导。结果表明：21世纪中叶，RCP2.6情景下柠檬最适宜种植区增加了3成左右，RCP4.5情景下柠檬最适宜种植区增加了5成左右，RCP8.5情景下柠檬最适宜种植区增加了9成左右。但由于受降水因子的限制，中度适宜区到2041—2050年开始缩减，总的可适宜种植区（高度适宜区+中度适宜区）基本趋于稳定，这将给柠檬在云南的种植发展提供广阔空间。     

基于短基线集InSAR技术监测北京地区地表形变

短基线集差分干涉测量(SBAS-InSAR)技术是在传统DInSAR技术基础上发展起来的一种更高精度的长时序变形监测方法,可有效地克服传统DInSAR在微小形变监测中受时空去相干以及大气效应等因素的影响,已成为当前InSAR技术的研究热点.本文利用ENVISAT ASAR传感器获取的22幅C波段影像数据,基于短基线集差分干涉测量技术对北京地区地表沉降进行监测,获取每个观测时刻的形变累积量,得到研究区的形变序列图,进而分析了该区域地表沉降特征,结合地质环境监测成果,初步讨论了2003至2010年间北京地区区域地表沉降成因.     

Oil-generation kinetics for organic facies with Type-II and -IIS kerogen in the Menilite Shales of the Polish Carpathians

The Menilite Shales (Oligocene) of the Polish Carpathians are the source of low-sulfur oils in the thrust belt and some high-sulfur oils in the Carpathian Foredeep. These oil occurrences indicate that the high-sulfur oils in the Foredeep were generated and expelled before major thrusting and the low-sulfur oils in the thrust belt were generated and expelled during or after major thrusting. Two distinct organic facies have been observed in the Menilite Shales. One organic facies has a high clastic sediment input and contains Type-II kerogen. The other organic facies has a lower clastic sediment input and contains Type-IIS kerogen. Representative samples of both organic facies were used to determine kinetic parameters for immiscible oil generation by isothermal hydrous pyrolysis and S₂ generation by non-isothermal open-system pyrolysis. The derived kinetic parameters showed that timing of S₂ generation was not as different between the Type-IIS and -II kerogen based on open-system pyrolysis as compared with immiscible oil generation based on hydrous pyrolysis. Applying these kinetic parameters to a burial history in the Skole unit showed that some expelled oil would have been generated from the organic facies with Type-IIS kerogen before major thrusting with the hydrous-pyrolysis kinetic parameters but not with the open-system pyrolysis kinetic parameters. The inability of open-system pyrolysis to determine earlier petroleum generation from Type-IIS kerogen is attributed to the large polar-rich bitumen component in S₂ generation, rapid loss of sulfur free-radical initiators in the open system, and diminished radical selectivity and rate constant differences at higher temperatures. Hydrous-pyrolysis kinetic parameters are determined in the presence of water at lower temperatures in a closed system, which allows differentiation of bitumen and oil generation, interaction of free-radical initiators, greater radical selectivity, and more distinguishable rate constants as would occur during natural maturation. Kinetic parameters derived from hydrous pyrolysis show good correlations with one another (compensation effect) and kerogen organic-sulfur contents. These correlations allow for indirect determination of hydrous-pyrolysis kinetic parameters on the basis of the organic-sulfur mole fraction of an immature Type-II or -IIS kerogen.     

双坡面临空岩质边坡滑动破坏模式下的稳定性分析

多数岩质边坡稳定性分析已基本解决了常走向单坡面临空的边坡平面滑动问题。然而,在路堑边坡工程和自然边坡中有很多边坡坡面并不是平面,它包含多个坡面。多坡面边坡包含两个或多个不同走向的坡面。多坡面边坡几何特征不同于单坡面边坡,所以滑动条件也不一样。在赤平投影图上,多坡面的滑动包络线是组成边坡的每个坡面单独投影包络线的组合。为了探讨方便,只讨论了双坡面临空岩质边坡的滑动破坏模式,并将其分为双坡面临空下的平面破坏和楔形体破坏。在赤平投影图上,双坡面滑动区域定义为双坡面边坡滑动包络图中两侧坡面的真倾线之间的面积。如果有一两个节理面真倾向线落在滑动区内,滑动破坏才可能发生。     

T -distributed Random Fields: A Parametric Model for Heavy-tailedWell-log Data1

Histograms of observations from spatial phenomena are often found to be more heavy-tailed than Gaussian distributions, which makes the Gaussian random field model unsuited. A T-distributed random field model with heavy-tailed marginal probability density functions is defined. The model is a generalization of the familiar Student-T distribution, and it may be given a Bayesian interpretation. The increased variability appears cross-realizations, contrary to in-realizations, since all realizations are Gaussian-like with varying variance between realizations. The T-distributed random field model is analytically tractable and the conditional model is developed, which provides algorithms for conditional simulation and prediction, so-called T-kriging. The model compares favourably with most previously defined random field models. The Gaussian random field model appears as a special, limiting case of the T-distributed random field model. The model is particularly useful whenever multiple, sparsely sampled realizations of the random field are available, and is clearly favourable to the Gaussian model in this case. The properties of the T-distributed random field model is demonstrated on well log observations from the Gullfaks field in the North Sea. The predictions correspond to traditional kriging predictions, while the associated prediction variances are more representative, as they are layer specific and include uncertainty caused by using variance estimates.