相律的应用:蒸发结晶过程自由度

在连续原理和浓度限制条件的基础上,分析了卤水蒸发过程的自由度,提出了“蒸发结晶过程自由度”这一概念。发现蒸发结晶过程自由度在整个过程中不是1便是0,在1和0之间交替变化,与选择的组分数和析出固相数目无关。蒸发结晶过程自由度为1,物理意义是在不引起新相产生旧相不消失的前提下过程连续地变化(水分的连续蒸失),几何意义是指在适当的坐标系中的一条线,一条直线或曲线;0的物理意义是指新相的产生和旧相的即将消失或者前一段的连续变化过渡到后一段的连续变化,几何意义是指一点,两蒸发阶段的交点或者过程的终点。简单地讨论了过程的单向性和连续件。     

Effects of laser beam alignment tolerance on lidar accuracy

One of the major lidar error sources not yet analyzed in the literature is the tolerance of the laser beam alignment with respect to the scanning mirror. In this paper, the problem of quantifying these errors is solved for rotating polygon mirror type lidar systems. An arbitrary deviation of the beam from its design direction–the vector of beam misalignment–can be described by two independent parameters. We choose these as horizontal and vertical components of the misalignment vector in the body frame. Either component affects both, horizontal and vertical lidar accuracy. Horizontal lidar errors appear as scan line distortions—along and across track shifts, rotations and scaling. It is shown that the horizontal component of misalignment results in a scan line first being shifted across the track and then rotated around the vertical at the new center of the scan line. Resulting vertical lidar error, being a linear function of the scan angle, is similar to that produced by a roll bias. The vertical component of the beam misalignment causes scan line scaling and an along track shift. The corresponding vertical error is quadratic with respect to the scan angle. The magnitude of these effects is significant even at tight alignment tolerances and cannot be realistically accounted for in the conventional calibration model, which includes only range, attitude and GPS biases. Therefore, in order to attain better accuracy, this model must be expanded to include the beam misalignment parameters as well. Addition of new parameters into the model raises a question of whether they can be reliably solved for. To give a positive answer to this question, a calibration method must utilize not only ground control information, which is typically very limited, but also the relative accuracy information from the overlapping flight lines.     

Aerosol climatology for the planetary boundary layer derived from regular lidar measurements

Regular aerosol extinction and backscatter measurements using a UV Raman Lidar have been performed for almost 3 years in Hamburg in the frame of the German Lidar Network. A set of 92 aerosol extinction and 164 aerosol backscatter profiles has been used for statistical investigations. Mean values and variances of the aerosol extinction and backscatter in the boundary layer have been calculated. Large fluctuations during the whole year have been found. The measured aerosol extinction over Hamburg shows a seasonal cycle with highest values in early fall and a second less prominent peak in spring.An analysis of the data using back trajectories showed a dependence of the aerosol extinction on the origin of the air mass. The residence time of the air mass over industrialized areas was found to be an important parameter for the measured aerosol extinction at Hamburg. However, only a small part of the total variability could be explained by the air mass origin.For 75 cases of aerosol extinction measurements under cloud-free conditions, the aerosol backscatter profile and therefore, the lidar ratio as a function of altitude could be determined. Winter measurements of the lidar ratio are often close to model results for maritime aerosol, the summer measurements are close to the model results for urban or continental aerosols.The high quality of the data has been proven by intercomparisons with other lidar systems and with star photometer measurements of the aerosol optical depth during the Lindenberg Aerosol Characterization Experiment (LACE'98) field campaign.     

可可西里——东昆仑活动构造带强震活动研究

青海昆仑山口西 8.1级地震发生在具有新生性特征的可可西里—东昆仑活动断裂带上。该断裂带在 190 0年以来的 10 0多年中经历了一个强震活动过程。在该强震活动过程中 ,地震沿整个可可西里—东昆仑活动构造带分段破裂 ,强震的破裂长度和震级之间大致满足对数线性的统计关系 ,强震活动呈现指数型时间分布的加速特征。这种强震加速活动特征可以用含多个震源体的孕震系统的强震成组活动模型给予解释。     

辽宁义县—北票地区义县组地层层序——义县阶标准地层剖面建立和研究之一

义县地区广义的义县组可解体为王家屯组 (暂命名 )和义县组 ;分属王家屯和义县火山旋回 ,前者为偏酸、偏碱性 ,后者主体为中基性、末期为中酸性火山岩系 ,并广泛发育潜火山岩相的玄武玢岩、安山玢岩和火山集块角砾熔岩筒。该区有七个主要沉积层 ,自下而上分别为王家屯组马神庙层、义县组老公沟层、业南沟层、砖城子层、大康堡层、朱家沟层和金刚山层 ;产有较丰富的无脊椎动物、脊椎动物和植物化石。以砖城子层、大康堡层和金刚山层为界 ,义县火山旋回可划分为 4个亚旋回 ,分别代表火山活动的初始期、主期、晚期和末期。北票四合屯地区的义县组相当义县地区义县组的第一和第二亚旋回 ,含鸟类化石的主沉积层 (尖山沟层和上园层 )可与砖城子层对比     

根据激光雷达观测结果研究中国武汉地区钠层的分布

通过大量钠层荧光激光雷达观测数据的分析研究，给出我国武汉地区钠层平均分布形态的基本特征. 结合钠层模型分析，指出大气微量元素分布的纬度变化可能是导致不同纬度地区钠层平均分布形态差异的主要原因,同时给出武汉地区随季节变化的钠层平均分布形态. 对武汉地区钠层分布形态时间变化的分析，发现其短期变化与重力波活动及夜间变化与潮汐波活动相关.     

Geospatial analysis of a coastal sand dune field evolution: Jockey's Ridge, North Carolina

Preservation and effective management of highly dynamic coastal features located in areas under development pressures requires in-depth understanding of their evolution. Modern geospatial technologies such as lidar, real time kinematic GPS, and three-dimensional GIS provide tools for efficient acquisition of high resolution data, geospatial analysis, feature extraction, and quantification of change. These techniques were applied to the Jockey's Ridge, North Carolina, the largest active dune field on the east coast of the United States, with the goal to quantify its deflation and rapid horizontal migration. Digitized contours, photogrammetric, lidar and GPS point data were used to compute a multitemporal elevation model of the dune field capturing its evolution for the period of 1974– 2004. In addition, peak elevation data were available for 1915 and 1953. Analysis revealed possible rapid growth of the dune complex between 1915–1953, followed by a slower rate of deflation that continues today. The main dune peak grew from 20.1 m in 1915 to 41.8 m in 1953 and has since eroded to 21.9 m in 2004. Two of the smaller peaks within the dune complex have recently gained elevation, approaching the current height of the main dune. Steady annual rate of main peak elevation loss since 1953 suggests that increase in the number of visitors after the park was established in 1974 had little effect on the rate of dune deflation. Horizontal dune migration of 3–6 m/yr in southerly direction has carried the sand out of the park boundaries and threatened several houses. As a result, the south dune section was removed and the sand was placed at the northern end of the park to serve as a potential source. Sand fencing has been an effective management strategy for both slowing the dune migration and forcing growth in dune elevation. Understanding the causes of the current movements can point to potential solutions and suggest new perspectives on management of the dune as a tourist attraction and as a recreation site, while preserving its unique geomorphic character and dynamic behavior.     

Research on Parallel Algorithms for uv-faceting Imaging

The uv-faceting imaging is one of the widely used large field of view imaging technologies, and will be adopted for the data processing of the low-frequency array in the first stage of the Square Kilometre Array (SKA1). Due to the scale of the raw data of SKA1 is unprecedentedly large, the efficiency of data processing directly using the original uv-faceting imaging will be very low. Therefore, a uv-faceting imaging algorithm based on the MPI (Message Passing Interface)+OpenMP (Open Multi-Processing) and a uv-faceting imaging algorithm based on the MPI+CUDA (Compute Unified Device Architecture) are proposed. The most time-consuming data reading and gridding in the algorithm are optimized in parallel. The verification results show that the results of the proposed two algorithms are basically consistent with that obtained by the current mainstream data processing software CASA (Common Astronomy Software Applications), which indicates that the proposed two algorithms are basically correct. Further analysis of the accuracy and total running time shows that the MPI+CUDA method is better than the MPI+OpenMP method in both the correctness rate and running speed. The performance test results show that the proposed algorithms are effective and have certain extensibility.     

Study of Star/Galaxy Classification Based on the XGBoost Algorithm

Machine learning has achieved great success in many areas today. The lifting algorithm has a strong ability to adapt to various scenarios with a high accuracy, and has played a great role in many fields. But in astronomy, the application of lifting algorithms is still rare. In response to the low classification accuracy of the dark star/galaxy source set in the Sloan Digital Sky Survey (SDSS), a new research result of machine learning, eXtreme Gradient Boosting (XGBoost), has been introduced. The complete photometric data set is obtained from the SDSS-DR7, and divided into a bright source set and a dark source set according to the star magnitude. Firstly, the ten-fold cross-validation method is used for the bright source set and the dark source set respectively, and the XGBoost algorithm is used to establish the star/galaxy classification model. Then, the grid search and other methods are used to adjust the XGBoost parameters. Finally, based on the galaxy classification accuracy and other indicators, the classification results are analyzed, by comparing with the models of function tree (FT), Adaptive boosting (Adaboost), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Stacked Denoising AutoEncoders (SDAE), and Deep Belief Nets (DBN). The experimental results show that, the XGBoost improves the classification accuracy of galaxies in the dark source classification by nearly 10% as compared to the function tree algorithm, and improves the classification accuracy of sources with the darkest magnitudes in the dark source set by nearly 5% as compared to the function tree algorithm. Compared with other traditional machine learning algorithms and deep neural networks, the XGBoost also has different degrees of improvement.     

An Automatic Method for Detecting Transients and Variable Sources in AST3 Survey Based on Image Subtraction and Random Forest

AST3-2 (the second Antarctic Survey Telescope) is located in Antarctic Dome A, the loftiest ice dome on the Antarctic Plateau. It produces a huge amount of observational data which require a more efficient data reduction program to be developed. Also the data transmission in Antarctica is much difficult, thus it is necessary to perform data reduction and detect variable and transient sources remotely and automatically in Antarctica, but this attempt is restricted by the unsatisfactory performance of the low power consumption computer in Antarctica. For realizing this purpose, to develop a new method based on the existing image subtraction method and random forest algorithm, taking the AST3-2 2016 dataset as the test sample, becomes an alternative choice. This method performs image subtraction on the dataset, then applies the principle component analysis to extract the features of residual images. Random forest is used as a machine learning classifier, and in the test a recall rate of 97% is resulted for the positive sample. Our work has verified the feasibility and accuracy of this method, and finally found out a batch of candidates for variable stars in the AST3-2 2016 dataset.