用多层变密度直接反演法研究下扬子地区的岩石圈构造

本文用多层变密度直接反演方法研究下扬子地区的岩石圈结构,并讨论其大地构造涵义及演化问题。文中给出了三次样条密度-深度函数的重力正演公式,采用非线性最小二乘优化算法,得到岩石圈内G_5、G_6、G_7三个主要密度界面,它们分别相应于中地壳低密度层底面、Moho面和上地幔顶部低密度层上界面。通过岩石圈结构分析,得到以下结果:杭州湾—海盐—嘉兴—长兴—高淳—芜湖隐伏深断裂是一条北西向的一级构造边界,它对下扬子区的大地构造发展和地壳演化有重要影响。     

基于现行抗震规范的胡聿贤模型参数研究

回顾了地震地面加速度随机过程模型,根据随机振动理论采用迭代方法求解出与我国现行《建筑抗震设计规范》(GB50011-2001)设计反应谱相对应的功率谱密度函数,选用胡聿贤功率谱密度函数拟合上述功率谱密度函数,运用非线性拟合技术给出了与规范对应的胡聿贤功率谱参数,可供这种模型作为地震地面运动输入时选用。     

超高密度电阻率法在岩溶及破碎带探测中的应用

结合工程实例,介绍了超高密度电阻率法在勘探岩溶及破碎带中的应用,总结了数据采集过程中的注意事项,并将成果与钻孔资料作了对比分析.结果表明,该方法在岩溶及破碎带的分布位置、大小、埋藏深度及发育情况等方面的勘察上都取得了较好的效果,是一种能有效提高工程勘察精度的物探方法.     

盆地基底岩性的综合地球物理预测方法——以松辽盆地滨北地区基底岩性预测为例

预测盆地基岩岩性不仅对于研究盆地的深部地质结构及盆地的形成演化具有重要的意义,而且也对基岩风化壳油气藏的勘探具有一定的指导作用.本文通过对盆地重、磁异常成因的综合分析,提出了一系列盆地基底岩性综合预测研究的综合地球物理资料处理解释方法技术.指出在地震构造界面的约束下采用重力剥皮技术可以较为可靠地获取基底岩性重力异常并分析了界面密度差对剥皮后基底岩性重力异常的影响,给出了等效密度差的求取方法.分析了基底起伏对基岩岩性磁异常的影响,指出采用"平化曲"将磁异常归化到与基底同一高度,可以有效地提高对基底岩性体的刻画能力.通过综合分析认为:应用基底的相对视密度、相对视磁化率及两者的相关系数可以有效地刻画基底岩性的特征.神经网络是基底岩性判别与分类的有效方法技术.通过对松辽盆地北部滨北地区的基底岩性的综合预测显示了本文系列预测基底岩性方法的有效性,预测结果反映了松辽盆地基底岩性的分布特征.该系列方法技术可为其他盆地的基底地质填图提供了可借鉴的综合预测方法技术.     

碰撞非相干散射理论谱宏观法和微观法的比较研究

非相干散射理论谱是理解非相干散射雷达探测的理论基础,其求解过程分为微观法和宏观法.在考虑碰撞不考虑磁场的条件下,本文使用这两种方法对理论谱分别进行求解,给出了关键求解过程,并对等离子体的归一化导纳和极化率以及非相干散射的微分散射截面和谱密度函数进行分析,得到了它们各自之间的数学关系,概括性分析了两种方法在理论选择、最终结论及多种因素下求解的异同点.

     

空-地-井重力异常正则化协同密度反演方法

围绕综合利用空-地-井多源重力异常联合反演提升精度的目标,提出正则化协同密度反演方法,从而有效利用多维数据的横向和纵向变化特征提高反演精度,且无需先验信息的约束.此外,还提出利用奇异值谱和深度分辨率工具评价航空数据观测高度和层数对反演分辨率的影响.通过理论模型试验本文方法在空-地、地-井等不同情况下的应用效果,结果表明空-地-井重力异常正则化协同密度反演方法能获得更高分辨率、高精度的反演结果,且证明不同的钻孔位置对反演结果有较大的影响,从而可指导实际的空-地-井联合勘探.最后,利用文顿盐丘实际勘探数据进行空地和空地井重力数据协同反演,反演结果垂向分辨率明显优于地面观测数据反演结果,盐盖的顶面及中心埋深与前人研究和地质资料解释相吻合,验证了方法正确性及实用性,为推进我国空-地-井立体重磁勘探提供了重要的技术手段.

     

CO_2 Density-Raman Shift Relation Derived from Synthetic Inclusions in Fused Silica Capillaries and Its Application

Abstract: The densities of CO2 inclusions in minerals are commonly used to determine the crystallizing conditions of the host minerals. However, conventional microthermometry is difficult to apply for inclusions of small size (< 5–10 μm) or low density. Raman analysis is an alternative method for determining CO2 density, provided that the CO2 density–Raman shift relation is known. This study aims to establish this CO2 density–Raman shift relation by using CO2 inclusions synthesized in fused silica capillaries. By using this newly-developed synthetic technique, we formed pure CO2 inclusions, and their densities were determined by microthermometry. The Raman analysis showed that the relation between CO2 density (D in g/cm3) and the separations (Δ in cm?1) between the two main bands (i.e. Fermi diad bands) in CO2 Raman spectra can be represented by a cubic equation: D (g/cm3)=0.74203(?0.019Δ3+5.90332Δ2?610.79472Δ+21050.30165)?3.54278 (r2=0.99920). Our calculated D value for a given Δ is between those obtained from two previously-reported equations, which were derived from different experimental methods. An example was given in this study to demonstrate that the densities of natural CO2 inclusions that could not be derived from microthermometry could be determined by using our method.     

Diagnosing snow accumulation errors in a rain‐snow transitional environment with snow board observations

Diagnosing the source of errors in snow models requires intensive observations, a flexible model framework to test competing hypotheses, and a methodology to systematically test the dominant snow processes. We present a novel process‐based approach to diagnose model errors through an example that focuses on snow accumulation processes (precipitation partitioning, new snow density, and snow compaction). Twelve years of meteorological and snow board measurements were used to identify the main source of model error on each snow accumulation day. Results show that modeled values of new snow density were outside observational uncertainties in 52% of days available for evaluation, while precipitation partitioning and compaction were in error 45% and 16% of the time, respectively. Precipitation partitioning errors mattered more for total winter accumulation during the anomalously warm winter of 2014–2015, when a higher fraction of precipitation fell within the temperature range where partition methods had the largest error. These results demonstrate how isolating individual model processes can identify the primary source(s) of model error, which helps prioritize future research.     

From soil aggregates to riverine flocs: a laboratory experiment assessing the respective effects of soil type and flow shear stress on particles characteristics

Particles eroded from hillslopes and exported to rivers are recognized to be composite particles of high internal complexity. Their architecture and composition are known to influence their transport behaviour within the water column relative to discrete particles. To‐date, hillslope erosion studies consider aggregates to be stable once they are detached from the soil matrix. However, lowland rivers and estuaries studies often suggest that particle structure and dynamics are controlled by flocculation within the water column. In order to improve the understanding of particle dynamics along the continuum from hillslopes to the lowland river environment, soil particle behaviour was tested under controlled laboratory conditions. Seven flume erosion and deposition experiments, designed to simulate a natural erosive event, and five shear cell experiments were performed using three contrasting materials: two of them were poorly developed and as such can not be considered as soils, whilst the third one was a calcareous brown soil. These experiments revealed that soil aggregates were prone to disaggregation within the water column and that flocculation may affect their size distribution during transport. Large differences in effective particle size were found between soil types during the rising limb of the bed shear stress sequence. Indeed, at the maximum applied bed shear stress, the aggregated particles median diameter was found to be three times larger for the well‐developed soil than for the two others. Differences were smaller in the falling limb, suggesting that soil aggregates underwent structural changes. However, characterization of particles strength parameters showed that these changes did not fully turn soil aggregates into flocs, but rather into hybrid soil aggregate–floc particles. Copyright © 2013 John Wiley & Sons, Ltd.     

Retrieval of effective leaf area index (LAIe) and leaf area density (LAD) profile at individual tree level using high density multi-return airborne LiDAR

As an important canopy structure indicator, leaf area index (LAI) proved to be of considerable implications for forest ecosystem and ecological studies, and efficient techniques for accurate LAI acquisitions have long been highlighted. Airborne light detection and ranging (LiDAR), often termed as airborne laser scanning (ALS), once was extensively investigated for this task but showed limited performance due to its low sampling density. Now, ALS systems exhibit more competing capacities such as high density and multi-return sampling, and hence, people began to ask the questions like—“can ALS now work better on the task of LAI prediction?” As a re-examination, this study investigated the feasibility of LAI retrievals at the individual tree level based on high density and multi-return ALS, by directly considering the vertical distributions of laser points lying within each tree crown instead of by proposing feature variables such as quantiles involving laser point distribution modes at the plot level. The examination was operated in the case of four tree species (i.e. Picea abies, Pinus sylvestris, Populus tremula and Quercus robur) in a mixed forest, with their LAI-related reference data collected by using static terrestrial laser scanning (TLS). In light of the differences between ALS- and TLS-based LAI characterizations, the methods of voxelization of 3D scattered laser points, effective LAI (LAIe) that does not distinguish branches from canopies and unified cumulative LAI (ucLAI) that is often used to characterize the vertical profiles of crown leaf area densities (LADs) was used; then, the relationships between the ALS- and TLS-derived LAIes were determined, and so did ucLAIs. Tests indicated that the tree-level LAIes for the four tree species can be estimated based on the used airborne LiDAR (R² = 0.07, 0.26, 0.43 and 0.21, respectively) and their ucLAIs can also be derived. Overall, this study has validated the usage of the contemporary high density multi-return airborne LiDARs for LAIe and LAD profile retrievals at the individual tree level, and the contribution are of high potential for advancing forest ecosystem modeling and ecological understanding.