Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data

Most studies have the achieved rapid and accurate determination of soil organic carbon (SOC) using laboratory spectroscopy; however, it remains difficult to map the spatial distribution of SOC. To predict and map SOC at a regional scale, we obtained fourteen hyperspectral images from the Gaofen-5 (GF-5) satellite and decomposed and reconstructed the original reflectance (OR) and the first derivative reflectance (FDR) using discrete wavelet transform (DWT) at different scales. At these different scales, as inputs, we selected the 3 optimal bands with the highest weight coefficient using principal component analysis and chose the normalized difference index (NDI), ratio index (RI) and difference index (DI) with the strongest correlation with the SOC content using a contour map method. These inputs were then used to build regional-scale SOC prediction models using random forest (RF), support vector machine (SVM) and back-propagation neural network (BPNN) algorithms. The results indicated that: 1) at a low decomposition scale, DWT can effectively eliminate the noise in satellite hyperspectral data, and the FDR combined with DWT can improve the SOC prediction accuracy significantly; 2) the method of selecting inputs using principal component analysis and a contour map can eliminate the redundancy of hyperspectral data while retaining the physical meaning of the inputs. For the model with the highest prediction accuracy, the inputs were all derived from the wavelength range of SOC variations; 3) the differences in prediction accuracy among the different prediction models are small; and 4) the SOC prediction accuracy using hyperspectral satellite data is greatly improved compared with that of previous SOC prediction studies using multispectral satellite data. This study provides a highly robust and accurate method for predicting and mapping regional SOC contents.     

锚固站对BDS自主导航精度的影响分析

介绍了自主导航的轨道确定及时间同步观测方程。以北斗仿真全星座为对象，通过采用仿真星间及卫星与地面锚固站间观测值，进行了60 d自主导航解算，分别探讨了锚固站数量及锚固站观测连续性对北斗卫星导航系统（BDS）3类卫星自主导航精度的影响。结果表明：锚固站数量及观测连续性对RERR及CERR无影响；加入1个锚固站即可显著改进URE结果精度，继续增加锚固站数量虽然可进一步提高URE精度但其改进效果有限；锚固站观测中断时间越长，其对应自主导航精度越低。因此，在BDS自主导航运行模式下应保持较高的锚固站观测频次以保证自主导航精度；另外，锚固站数量及观测连续性对北斗系统3类卫星自主导航精度的影响无显著差异。     

Stochastic bivariate time series models of waves in the North Sea and their application in simulation-based design

In this paper, we present and evaluate three long-term wave models for application in simulation-based design of ships and marine structures. Designers and researchers often rely on historical weather data as a source for ocean area characteristics based on hindcast datasets or in-situ measurements. The limited access and size of historical datasets reduces repeatability of simulations and analyses, making it difficult to assess the sampling variability of performance and loads on marine vessels and structures. Markov, VAR and VARMA wave models, producing independent long-term time series of significant wave height (H_s) and spectral peak period (T_p), is presented as possible solutions to this problem. The models are tested and compared by addressing how the models affect interpretation of design concepts and the ability to replicate statistical and physical characteristics of the wave process. Our results show that the VAR and VARMA models perform sufficiently in describing design performance, but does not capture the physical process fully. The Markov model is found to perform worst of the tested models in the applied tests, especially for measures covering several consecutive sea states.     

An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing

New Earth observation missions and technologies are delivering large amounts of data. Processing this data requires developing and evaluating novel dimensionality reduction approaches to identify the most informative features for classification and regression tasks. Here we present an exhaustive evaluation of Guided Regularized Random Forest (GRRF), a feature selection method based on Random Forest. GRRF does not require fixing a priori the number of features to be selected or setting a threshold of the feature importance. Moreover, the use of regularization ensures that features selected by GRRF are non-redundant and representative. Our experiments based on various kinds of remote sensing images, show that GRRF selected features provides similar results to those obtained when using all the available features. However, the comparison between GRRF and standard random forest features shows substantial differences: in classification, the mean overall accuracy increases by almost 6% and, in regression, the decrease in RMSE almost reaches 2%. These results demonstrate the potential of GRRF for remote sensing image classification and regression. Especially in the context of increasingly large geodatabases that challenge the application of traditional methods.     

多层构造油藏纵向细分计算单元对储量参数及结果的影响

为满足油田中后期精细开发及后续调整挖潜的需要，亟需进行更精细的储量研究，其中最重要的一环就是纵向细分计算单元.对于多层构造油藏，纵向细分主要根据油层组内隔夹层分布特征、小层物性特征，细分到以小层或者分布特征和物性相近且纵向上连续分布的小层组合为计算单元.根据纵向细分计算单元前后储量参数选值的理论推导，结合储层的沉积展布特征，认为含油面积范围内无储层尖灭时，仅平面油层厚度差异较大的油藏平均有效厚度变小.含油面积范围内储层尖灭时，正常三角洲沉积储层的油藏各参数一般都变小；而辫状河三角洲沉积储层的油藏一般平均有效厚度变小，平均有效孔隙度和平均含油饱和度变大.这一结论可以有效指导储量评估过程中纵向细分计算单元方案划分，为同类油田的精细储量研究提供技术支撑.     

Enhancement of backtracking search algorithm for identifying soil parameters

In this paper, an enhanced backtracking search algorithm (so-called MBSA-LS) for parameter identification is proposed with two modifications: (a) modifying the mutation of original backtracking search algorithm (BSA) considering the contribution of current best individual for accelerating convergence speed and (b) novelly incorporating an efficient differential evolution (DE) as local search for improving the quality of population. The proposed MBSA-LS is first validated with better performance than the original BSA and some other typical state-of-the-art optimization algorithms on a benchmark of soil parameter identification in terms of effectiveness, efficiency, and robustness. Then, the efficiency of the MBSA-LS is further illustrated by two representative cases: identifying soil parameters from both laboratory tests and field measurements. All comparisons demonstrate that the proposed MBSA-LS algorithm can give accurate results in a short time. Finally, to conveniently solve the problems of parameter identification, a practical tool ErosOpt for parameter identification is developed by integrating the proposed MBSA-LS and some other efficient algorithms for readers to conduct the parameter identification using optimisation algorithms.     

利用InSAR资料反演宁洱Ms6.4地震震源参数及库仑应力变化

首先利用ALOS PALSAR数据，通过D-InSAR技术获取2007-06-03云南宁洱MS6.4地震的同震形变场，然后基于Okada弹性半空间位错模型反演该地震的断层几何以及精细滑动分布，最后计算宁洱地震后周边断层的静态库仑应力变化。结果表明，形变主要集中在西盘，最大视线向形变量为51.6 cm；反演得到的震源位置为23.05°N、101.02°E，深度3 km，断层走向145°，倾向49.5°，平均滑动角153°，发震断层为NNW向普洱断裂，断层活动以右旋走滑为主，兼具逆冲分量；断层面最大滑动量为1.2 m，反演得到的震级为MW619。基于库仑应力场发现，磨黑断裂处于库仑应力增加区域，而2014年景谷地震位于负值区域。结合实地考察资料和反演结果表明，宁洱地震为浅源地震，但断层并未出露地表。     

Estimating the starting time and identifying the type of urbanization based on dense time series of landsat-derived Vegetation-Impervious-Soil (V-I-S) maps – A case study of North Taiwan from 1990 to 2015

Land cover and land use change (LCLUC) is a global phenomenon, and LCLUC in urbanizing regions has substantial impacts on humans and their environments. In this paper, a semi-automatic approach to identifying the type and starting time of urbanization was developed and tested based on dense time series of Vegetation-Impervious-Soil (V-I-S) maps derived from Landsat surface reflectance imagery. The accuracy of modeled V-I-S fractions and the estimated time of initial change in impervious cover were assessed. North Taiwan, one of the regions of the island of Taiwan that experienced the greatest urban LCLUC, was chosen as a test area, and the study period is 1990 to 2015, a period of substantial urbanization. In total, 295 dates of Landsat imagery were used to create 295 V-I-S fraction maps that were used to construct fractional cover time series for each pixel. Root Mean Square Error (RMSE)s for the modeled Vegetation, Impervious, and Soil were 25 %, 22 %, 24 % respectively. The time of Urban Expansion is estimated by logistic regression applied to Impervious cover time series, while the time of change for Urban Renewal is determined by the period of brief Soil exposure. The identified location and estimated time for newly urbanized lands were generally accurate, with 80% of Urban Expansion estimated within ±2.4 years. However, the accuracy of identified Urban Renewal was relatively low. Our approach to identifying Urban Expansion with dense time series of Landsat imagery is shown to be reliable, while Urban Renewal identification is not.     

杭州湾中部实测波浪特性分析

利用杭州湾中部一年实测波浪资料,分析该地区的波参数统计变化特性,采用最小二乘法拟合得出相关参数之间的关系式,并统计分析了频谱特性。结果表明:杭州湾中部以小浪和轻浪为主,在各月分布较为均匀,年平均周期为2.97 s;最大一次波浪过程为冷空气影响所致,影响强度大于台风莫拉克;常浪向分布在东北至东南向,其中又以小浪出现较多,对应的波周期以2~4 s为主;强浪向主要分布在西北和东北方向,对应的波周期主要分布在3~5 s;多数特征波参数之间相关关系较好;由谱分析得知风浪占多数,且以单峰为主。研究结果可为相关工程和理论研究提供基础资料。     

一种横观各向同性岩体蠕变模型

层状岩体由于层理面或者结构面的存在，在力学上具有横观各向同性的特点，现有的各向同性蠕变模型难以全面反映横观各向同性岩体的蠕变力学特性。为构建能够反映横观各向同性岩体的三维蠕变模型，以能反映瞬时应变、减速蠕变和稳态蠕变特征的黏弹性Burgers模型为基础，基于常泊松比假定，在三维各向同性蠕变本构方程的基础上，按照算子替换的方法，将横观各向同性柔度矩阵代替各向同性柔度矩阵，并考虑了平行和垂直层理方向岩体蠕变力学行为的差异性，推导了横观各向同性岩体的三维蠕变本构方程。根据本构方程的特点，提出了根据平行和垂直方向岩体蠕变试验结果进行三维蠕变本构模型中蠕变参数的辨识方法。将提出的模型应用于三轴蠕变试验参数辨识，从而获得了一套完整的三维蠕变参数，并与试验结果进行对比分析，从而验证了所提模型与方法的合理性和有效性。进一步，指出了传统蠕变试验设计方案的局限性，给出了横观各向同性材料蠕变试验设计建议。研究成果为研究岩体三维蠕变机制提供了新思路，可对岩体蠕变试验设计提供相应的科研支撑。