中国邻海海-气热量、水汽通量计算和分析

使用长年代、高质量海洋气象观测资料,选用适合研究海域的计算参数,按2°×2°网格计算了中国邻海（黄、渤、东、南海及140°E以西西北太平洋）辐射平衡和海-气热量、水汽通量,讨论了它们的年、月分布和时空变化,并以海面热量净收支为主要指标进行了海洋气候区划。     

THE STABILITY OF ROCK-VENEERED HILLSLOPES

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Evaluating a process‐based model for use in streambank stabilization: insights on the Bank Stability and Toe Erosion Model (BSTEM)

Streambank retreat is a complex cyclical process involving subaerial processes, fluvial erosion, seepage erosion, and geotechnical failures and is driven by several soil properties that themselves are temporally and spatially variable. Therefore, it can be extremely challenging to predict and model the erosion and consequent retreat of streambanks. However, modeling streambank retreat has many important applications, including the design and assessment of mitigation strategies for stream revitalization and stabilization. In order to highlight the current complexities of modeling streambank retreat and to suggest future research areas, this paper reviewed one of the most comprehensive streambank retreat models available, the Bank Stability and Toe Erosion Model (BSTEM), which has recently been integrated with several popular hydrodynamic and sediment transport models including the Hydrologic Engineering Center's River Analysis System (HEC‐RAS). The objectives of this paper were to: (i) comprehensively review studies that have utilized BSTEM and report their findings, (ii) address the limitations of the model so that it can be applied appropriately in its current form, and (iii) suggest directions of research that will help make the model a more useful tool in future applications. The paper includes an extensive overview of peer reviewed studies to guide future users of BSTEM. The review demonstrated that the model needs further testing and evaluation outside of the central United States. Also, further development is needed in terms of accounting for spatial and temporal variability in geotechnical and fluvial erodibility parameters, incorporating subaerial processes, and accounting for the influence of riparian vegetation on streambank pore‐water pressure dynamics, applied shear stress, and erodibility parameters. Copyright © 2016 John Wiley & Sons, Ltd.     

Intermediate to Long-term Estimation of Strong Earthquake Risk Areas in the Chinese Mainland Based on Geodesic Measurements

Based on previous research results, present-day crustal deformation and gravity fields in the Chinese mainland are analyzed using the GPS data, leveling, gravity and cross-fault deformations. We analyzed strain accumulation of the major faults, and identified locked or high strain accumulation segments. Combining the effects of large earthquakes in the study area, the long-term (decade) probability of large earthquakes in the Chinese mainland is estimated.     

基于大地形变测量的中国大陆中长期强震危险区研究

应用GPS、 水准、 重力和跨断层形变观测资料, 在以往研究成果的基础上, 分析中国大陆现今地壳形变场、 重力场变化形态, 分析主要活动断裂带应变积累状态, 识别强闭锁、 高应变积累段, 结合大震对区域应变积累的影响, 估计未来10年尺度中国大陆地区可能发生大地震的危险区域。     

RN-FD型固体氡气源的稳定性及应用分析

对测氡仪器进行精确校准是氡测量工作中的重要环节,固体氡气源的稳定性、可靠性在校准中则显得至关重要。本文通过分析RN-FD型固体氡气源对闪烁室K值的稳定性实验结果,认为:RN-FD型固体氡气源标称的浓度值与实际浓度值不一致,标称浓度值只是理论浓度值而不是实际浓度值,需重新刻度才能使用;RN-FD型固体氡气源抽气循环时间不同则浓度不同,但抽气循环时间固定,观测结果比较稳定;对于没有α检查源的台站,RN-FD型固体氡气源可用于氡观测仪器坪区检查。     

页岩储层叠前密度稳定反演方法

密度参数是页岩储层评价的重要敏感参数,但通常认为准确获得密度信息难度较大.本文探索提出了一种稳定的基于弹性阻抗的叠前密度反演方法,以提高优质页岩的识别精度.首先,基于不同的参数化弹性阻抗方程开展密度敏感性分析,优选了敏感性最高的Aki-Richards近似弹性阻抗方程作为反演方程.然后,在弹性阻抗方程线性化的基础上,假设反演方程系数矩阵的逆存在,将密度(取对数)表示为多个角度弹性阻抗(取对数)的加权和,通过井旁道弹性阻抗反演结果与测井数据的回归求取加权系数,避免了常规反演方法对大型系数矩阵的求逆计算,从而提高了密度反演的稳定性,同时由于利用测井资料作为匹配目标,密度反演的精度也得到了提高.模型试算和涪陵页岩气田的实际资料应用实践均表明了该方法能够有效地提高密度反演的精度与稳定性.     

Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat

Hyperspectral sensing can provide an effective means for fast and non-destructive estimation of leaf nitrogen (N) status in crop plants. The objectives of this study were to design a new method to extract hyperspectral spectrum information, to explore sensitive spectral bands, suitable bandwidth and best vegetation indices based on precise analysis of ground-based hyperspectral information, and to develop regression models for estimating leaf N accumulation per unit soil area (LNA, g N m⁻²) in winter wheat (Triticum aestivum L.). Three field experiments were conducted with different N rates and cultivar types in three consecutive growing seasons, and time-course measurements were taken on canopy hyperspectral reflectance and LNA under the various treatments. Then, normalized difference spectral indices (NDSI) and ratio spectral indices (RSI) based on the original spectrum and the first derivative spectrum were constructed within the range of 350–2500 nm, and their relationships with LNA were quantified. The results showed that both LNA and canopy hyperspectral reflectance in wheat changed with varied N rates, with consistent patterns across different cultivars and seasons. The sensitive spectral bands for LNA existed mainly within visible and near infrared regions. The best spectral indices for estimating LNA in wheat were found to be NDSI (R₈₆₀, R₇₂₀), RSI (R₉₉₀, R₇₂₀), NDSI (FD₇₃₆, FD₅₂₆) and RSI (FD₇₂₅, FD₅₁₆), and the regression models based on the above four spectral indices were formulated as Y = 26.34x^1.887, Y = 5.095x − 6.040, Y = 0.609 e^3.008x and Y = 0.388x^1.260, respectively, with R² greater than 0.81. Furthermore, expanding the bandwidth of NDSI (R₈₆₀, R₇₂₀) and RSI (R₉₉₀, R₇₂₀) from 1 nm to 100 nm at 1 nm interval produced the LNA monitoring models with similar performance within about 33 nm and 23 nm bandwidth, respectively, over which the statistical parameters of the models became less stable. From testing of the derived equations, the model for LNA estimation on NDSI (R₈₆₀, R₇₂₀), RSI (R₉₉₀, R₇₂₀), NDSI (FD₇₃₆, FD₅₂₆) and RSI (FD₇₂₅, FD₅₁₆) gave R² over 0.79 with more satisfactory performance than previously reported models and physical models in wheat. It can be concluded that the present hyperspectral parameters of NDSI (R₈₆₀, R₇₂₀), RSI (R₉₉₀, R₇₂₀), NDSI (FD₇₃₆, FD₅₂₆) and RSI (FD₇₂₅, FD₅₁₆) can be reliably used for estimating LNA in winter wheat.     

Assessing model of highway slope stability based on optimized SVM

Considering the geological hazards attributed to the highway slope, using a common simple model cannot accurately assess the stability of the slope. First, principal component analysis (PCA) was conducted to extract the principal components of six factors (namely, bulk density, cohesion, internal friction angle, slope angle, slope height, and pore water pressure ratio) affecting the slope stability. Second, four principal components were adopted as input variables of the support vector machine (SVM) model optimized by genetic algorithm (GA). The output variable was slope stability. Lastly, the assessing model of highway slope stability based on PCA-GA-SVM is established. The maximal absolute error of the model is 0.0921 and the maximal relative error is 9.21% by comparing the assessment value and the practical value of the test sample. The above studies are conducive to enrich the assessing model of highway slope stability and provide some reference for highway slope engineering treatment.