The critical scale of fluctuation for active lateral forces in spatially variable undrained clays

This study addresses the phenomenon of the critical scale of fluctuation (SOF) for active lateral force (P_a) in undrained clay when there is a spatial variability in the clay. The phenomenon is significant under shear strength (τ_f) random fields but is insignificant under unit weight (γ) random fields. It is found that the phenomenon of the critical SOF is connected to the nature of the spatial averaging, which is “line averaging” under τ_f random fields and is “area averaging” under γ random fields. The former averaging effect (line) is significantly weaker than the latter (area), so the tendency for the critical slip plane to seek for a favorable location is stronger for the τ_f random field than for the γ random field. Hence, the phenomenon of the critical SOF is more pronounced under τ_f random fields than under γ random fields. The underlying mechanisms for the phenomenon of the critical SOF will be explored in this paper.     

Generating soil thickness maps by means of geomorphological-empirical approach and random forest algorithm in Wanzhou County,Three Gorges Reservoir

Soil thickness, intended as depth to bedrock, is a key input parameter for many environmental models. Nevertheless, it is often difficult to obtain a reliable spatially exhaustive soil thickness map in wide-area applications, and existing prediction models have been extensively applied only to test sites with shallow soil depths. This study addresses this limitation by showing the results of an application to a section of Wanzhou County (Three Gorges Reservoir Area, China), where soil thickness varies from 0 to ～40 m. Two different approaches were used to derive soil thickness maps: a modified version of the geomorphologically indexed soil thickness (GIST) model, purposely customized to better account for the peculiar setting of the test site, and a regression performed with a machine learning algorithm, i.e., the random forest, combined with the geomorphological parameters of GIST (GIST-RF). Additionally, the errors of the two models were quantified, and validation with geophysical data was carried out. The results showed that the GIST model could not fully contend with the high spatial variability of soil thickness in the study area: the mean absolute error was 10.68 m with the root-mean-square error (RMSE) of 12.61 m, and the frequency distribution residuals showed a tendency toward underestimation. In contrast, GIST-RF returned a better performance with the mean absolute error of 3.52 m and RMSE of 4.56 m. The derived soil thickness map could be considered a critical fundamental input parameter for further analyses.     

Seismic random noise attenuation and signal-preserving by multiple directional time-frequency peak filtering

Time-frequency peak filtering (TFPF) is an effective method for seismic random noise attenuation. The linearity of the signal has a significant influence on the accuracy of the TFPF method. The higher the linearity of the signal to be filtered is, the better the denoising result is. With this in mind, and taking the lateral coherence of reflected events into account, we do TFPF along the reflected events to improve the degree of linearity and enhance the continuity of these events. The key factor to realize this idea is to find the traces of the reflected events. However, the traces of the events are too hard to obtain in the complicated field seismic data. In this paper, we propose a Multiple Directional TFPF (MD–TFPF), in which the filtering is performed in certain direction components of the seismic data. These components are obtained by a directional filter bank. In each direction component, we do TFPF along these decomposed reflected events (the local direction of the events) instead of the channel direction. The final result is achieved by adding up the filtering results of all decomposition directions of seismic data. In this way, filtering along the reflected events is implemented without accurately finding the directions. The effectiveness of the proposed method is tested on synthetic and field seismic data. The experimental results demonstrate that MD–TFPF can more effectively eliminate random noise and enhance the continuity of the reflected events with better preservation than the conventional TFPF, curvelet denoising method and F–X deconvolution method.     

随机森林机器算法在江苏省小麦赤霉病病穗率预测中的应用

基于2002—2018年江苏省13个市的小麦赤霉病病穗率资料与生育期观测资料、相应时段内的逐日气象数据,应用随机森林机器学习算法,分生育期、分区域定量评估影响病穗率的主要气象因子特征变量和贡献率,按不同起报时间建立预测模型并进行验证。结果表明,各生育期重要特征变量贡献率的排序为:抽穗扬花期＞拔节期＞越冬期。抽穗扬花期湿度、连续≥3 d的雨日和日照对赤霉病起主导作用,拔节期日照、降雨量、湿度和雨日与越冬期气温和降雪对赤霉病均具有前期影响,甄别出的重要特征变量排序结果符合赤霉病菌发育、释放、侵染和流行规律;基于随机森林算法建立的病穗率预测模型的精度与重要特征变量个数、赤霉病发生区域、M_try参数设定、生育期有关;最早可在3月初进行预测,预测时效近3个月,起报时间越接近乳熟期,输入的重要特征变量越多,则病穗率预测准确率越高,病穗率模拟值与实测值的波动趋势完全一致,对赤霉病“中等”和“偏重”等级模拟效果好,表明随机森林算法在赤霉病预测中有较高的可靠性和业务应用潜力。      

RAPD分析中最适样本量和位点数的研究

用25个引物,对40个样本扩增,研究了勒氏笛鲷随机扩增多态性DNA(RAPD)分析中的最适样本量和位点数。结果显示:在样本量达到20且位点数达到70个以上时,遗传距离趋于一个稳定的数值,即在RAPD分析中,样本数应达到20且位点数应达到70才能保证结果的可靠性。     

MIXTURE ANGLYSIS USING FACTOR ANGLYSIS I: CALIBRATION AND QUANTITATION

One of the major application areas of factor analysis, multivariate calibration and quantitation, is coveredin this review. The algorithms, methodologies and applications covered include principal componentregression, target transformation factor analysis, singular value decomposition and rank annihilationfactor analysis. Many important areas of research having relevance to multivariate calibration andquantitation problems are also covered in this review, including background correction, measurementerror, rank determination, cross-validation, figures of merit, detection of invalid samples, experimentaldesign, sample selection, statistical inference and wavelength selection.     

Spatial statistics of clustered data

Modern spatial statistics techniques are widely used to make predictions for natural processes that are continuously distributed over some convex domain. Implementation of these techniques often relies on the adequate estimation of certain spatial correlation functions such as the covariance and the variogram from the data sets available. This work studies the practical estimation of such spatial correlation functions in the case of clustered data. The coefficient of variation of the dimensionless spatial density of the point pattern of sample locations is suggested as a useful metric for degree of clusteredness of the clustered data set. We show that the common variogram estimator becomes increasingly unreliable with increasing coefficient of variation of the dimensionless spatial density of the point pattern of sample locations. Moreover, we present a modified form of the variogram estimator that incorporates declustering weights, and propose a scheme for estimating the declustering weights based on zones of proximity. Finally, insight is gained in terms of a numerical application of the common and modified methods on piezometric head data collected over an irregular network.Acknowledgments. This work has been supported by grants from the National Institute of Environmental Health Sciences (P42 ES05948-02), the Army Research Office (DAAG55-98-1-0289), and the National Aeronautics and Space Administration (60-00RFQ041). Some of the calculations conducted in support of this work were done on the SGI Origin 2400 at the North Carolina Supercomputing Center, RTP, NC.     

The Moon 35 years after Apollo: What's left to learn?

With the cancellation of the Apollo program after Apollo 17 returned from the Moon in 1972, the focus of NASA switched to other areas of the Solar System. Study of the Moon did continue through analysis of the returned samples and remotely sensed data sets (both orbital and surface), as well as through Earth-based telescopic studies. In the 1990s, new orbital data were obtained from several missions (fly-by and orbital), the first being Galileo that allowed the lunar farside to be mapped, followed by global mapping by the Clementine and Lunar Prospector missions.Interest in the Moon started to increase at the beginning of the 21st century as other nations focused their space exploration programs on the Moon. The speech by President Bush in January 2004 put the Moon back into the critical exploration path for NASA, paving the way for humans to return to the lunar surface by 2020. This return will be critical for developing technologies and protocols for the eventual human exploration of other parts of the solar system. At the time of writing (June 2008), the SELENE/Kaguya mission (Japan and Chang’e-1 (China) are orbiting the Moon, with Chandrayaan-1 (India) and Lunar Reconnaissance Orbiter (USA) being scheduled to launch later in 2008.The past (and present) exploration of the Moon begs the question “what's left to be done?” With the renewed focus on the Moon, now that it is on the pathway for the exploration of Mars (and beyond) a similar question has been raised - what should the astronauts do on the Moon? The publication of the New Views of the Moon book [Jolliff et al., 2006. New Views of the Moon, Reviews in Mineralogy, vol. 60. American Mineralogical Society, 721pp] highlighted a number of important scientific questions that remain unanswered as well as posing many more on the basis of the currently available data. These questions resonated in three Lunar Exploration Analysis Group (LEAG) reports pertinent to this discussion, which were also published (on line) during 2006 (http://www.lpi.usra.edu/leag), and in the National Research Council of the National Academies [2007. The Scientific Context for Exploration of the Moon. National Academies Press, Washington, DC, 112pp] report entitled “The Scientific Context for Exploration of the Moon”. This paper synthesizes these recent studies, along with those from the 1980s and 1990s, to emphasize the lunar science questions that remain unanswered. In addition, it summarizes the missions already flown to the Moon along with those that are planned in order to give the reader an idea of exactly what lunar science has been and will be conducted in the hope that it will inspire proposals for missions to address the outstanding science questions.     

Investigation of fractal distribution law for the trace number of random and grouped fractures in a geological mass

A fractal study method of the number of geological mass fractures is introduced in detail in this paper. Three main aspects of the problem were studied: (1) The random distribution of fractures in a geological mass was in good agreement with the fractal law. The size scale of the studied geological mass ranged from 2400 m to 1 mm for the length of each side, and the geological mass samples were taken from 13 coal areas in China. (2) The geological mass fractures were evidently directional and anisotropic, having originated from tectonic movement. Observation and statistics for the data from the Xuangang, Fenxi and Dongshan coal areas in Shanxi, China, demonstrated that the fracture distribution of each group, classified by the strike of the strata, still follow the fractal law, even though the fractal dimension varies to a certain extent with different strikes. (3) The sedimentary strata containing the coal seams, as a geological mass, underwent almost similar tectonic movements in their geological history. The mechanical experiments on geological mass samples from Fenxi and Jiexiu in Shanxi demonstrated that the fractal dimension of the number of fractures in the same strata is in good power function with the product of strength and elastic modulus. The larger the product of the strength of the elastic modulus is, the larger is the fractal dimension, and vice versa.     

面向对象方法的时间序列MODIS数据湿地信息提取——以洞庭湖流域为例

以洞庭湖流域为研究区,对大范围湿地信息遥感提取方法进行了研究。先基于时间序列MODIS EVI及物候特征参数,通过J-M(Jeffries-Matusita distance)距离分析,构建了MODIS(250 m)最佳时序组合分类数据;其次,通过Johnson指数确定了最佳分割尺度,采用面向对象的遥感分类方法(Random tree分类器)提取了洞庭湖流域的湿地信息,并验证该方法的适用性。研究结果表明,基于时序数据与面向对象的Random tree分类的总体精度和Kappa系数分别为78.84%和0.71,较之基于像元的相同算法的总体分类精度和Kappa系数分别提高了5.79%和0.04。同时,基于面向对象方法的湿地整体的用户精度与生产者精度较基于像元方法分别提高了4.56%和6.21%,可有效提高大区域湿地信息提取的精度。