Structural geology practice and learning,from the perspective of cognitive science

Spatial ability is required by practitioners and students of structural geology and so, considering spatial skills in the context of cognitive science has the potential to improve structural geology teaching and practice. Spatial thinking skills may be organized using three dichotomies, which can be linked to structural geology practice. First, a distinction is made between separating (attending to part of a whole) and combining (linking together aspects of the whole). While everyone has a basic ability to separate and combine, experts attend to differences guided by experiences of rock properties in context. Second, a distinction is made between seeing the relations among multiple objects as separate items or the relations within a single object with multiple parts. Experts can flexibly consider relations among or between objects to optimally reason about different types of spatial problems. Third, a distinction is made between reasoning about stationary and moving objects. Experts recognize static configurations that encode a movement history, and create mental models of the processes that led to the static state. The observations and inferences made by a geologist leading a field trip are compared with the corresponding observations and inferences made by a cognitive psychologist interested in spatial learning. The presented framework provides a vocabulary for discussing spatial skills both within and between the fields of structural geology and cognitive psychology.     

An SVM-based machine learning method for the separation of alteration zones in Sungun porphyry copper deposit

Sungun porphyry copper deposit is in East Azarbaijan province, NW of Iran. There exist four hypogene alteration types in Sungun: potassic, propylitic, potassic–phyllic, and phyllic. Copper mineralization is essentially associated more with the potassic and less with the phyllic alterations and their separation is, therefore, quite important. This research has tried to separate these two alteration zones in Sungun porphyry copper deposit using the Support Vector Machine (SVM) method based on the fluid inclusion data, and seven variables including homogenization temperatures, salinity, pressure, depth, density and the Cu grade have been measured and calculated for each separate sample. To apply this method, use is made of the radial basis function (RBF) as the kernel function. The best values for λ and C (the most important SVM parameters) that perform well in the training and test data are 0.0001 and 1, respectively. If these values for λ and C are applied, the phyllic and potassic alteration zones in the training and test data will be separated with an accuracy of about 95% and 100%, respectively. This method can help geochemists in separating the alteration zones because classifying and separating samples microscopically is not only very hard, but also quite time and money consuming.     

Predicted ultimate capacity of laterally loaded piles in clay using support vector machine

The determination of ultimate capacity of laterally loaded pile in clay is a key parameter for designing the laterally loaded pile. The available methods for determination of ultimate resistance of pile in clay are not reliable. This study investigates the potential of a support vector machine (SVM)-based approach to predict the ultimate capacity of laterally loaded pile in clay. The SVM, which is firmly based on statistical learning theory, uses a regression technique by introducing an ?-insensitive loss function. A sensitivity analysis has been carried out to determine the relative importance of the factors affecting ultimate capacity. The results show that SVM has the potential to be a practical tool for prediction of the ultimate capacity of pile in clay.     

关于集合Kalman滤波的理论和方法的发展

随着同化方法的不断的发展，一种新的资料同化方法——集合Kalman滤波正在兴起。简单地回顾了同化方法的发展，探讨了集合Kalman滤波的特点。同时，还介绍了集合Kalman滤波发展的过程以及指出目前所面临的问题和未来的发展趋势。     

ENSEMBLE PREDICTION EXPERIMENTS OF TRACKS OF TROPICAL CYCLONES BY USING MULTIPLE CUMULUS PARAMETERIZATION SCHEMES

Ensemble prediction experiments of the tracks of eight tropical cyclones occurring between 2004 -2006 over the western Pacific have been performed by using MM5 with five cumulus parameterizations chemes. The results show that the predictions of the tracks of the tropical cyclones are sensitive to the selection of cumulus parameterization schemes. Each scheme has its own advantage and disadvantage, and the predications without cumulus parameterization schemes are not the worst, sometimes even better than the others. And all of the three ensemble methods improve the predictions of the tracks significantly, among which the ensemble method without parameterization schemes, the Grell, Betts-Miller and Kain-Fritsch schemes are the best.     

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs¹³⁷ activity given the measurements taken in the region of Briansk following the Chernobyl accident.     

Simulation of torsional shear test results with neuro-fuzzy control system

In the first part of this study, a series of stress-controlled hollow cylinder cyclic torsional triaxial shear tests were conducted on loose to medium dense saturated samples of clean Toyoura sand to investigate its liquefaction behavior. A uniform cyclic sinusoidal loading at a 0.1 Hz frequency was applied to air-pluviated samples where confining pressure and relative density was varied. Cyclic shear stress–strain changes, the number of cycles to reach liquefaction and pore pressure variations were recorded. Results indicate that the liquefaction resistances of uniform sands are significantly affected by the method of sample preparation and initial conditions.     

The influence of tropical Indian Ocean warming on the Southern Hemispheric stratospheric polar vortex

During the past decades, concurrent with global warming, most of global oceans, particularly the tropical Indian Ocean, have become warmer. Meanwhile, the Southern Hemispheric stratospheric polar vortex (SPV) exhibits a deepening trend. Although previous modeling studies reveal that radiative cooling effect of ozone depletion plays a dominant role in causing the deepening of SPV, the simulated ozone-depletion-induced SPV deepening is stronger than the observed. This suggests that there must be other factors canceling a fraction of the influence of the ozone depletion. Whether the tropical Indian Ocean warming (IOW) is such a factor is unclear. This issue is addressed by conducting ensemble atmospheric general circulation model (AGCM) experiments. And one idealized IOW with the amplitude as the observed is prescribed to force four AGCMs. The results show that the IOW tends to warm the southern polar stratosphere, and thus weakens SPV in austral spring to summer. Hence, it offsets a fraction of the effect of the ozone depletion. This implies that global warming will favor ozone recovery, since a warmer southern polar stratosphere is un-beneficial for the formation of polar stratospheric clouds (PSCs), which is a key factor to ozone depletion chemical reactions. Supported by National Natural Science Foundation of China (Grant Nos. 40775053 and 90711004), National Basic Research Program of China (Grant No. 2009CB421401), and Innovation Key Program of Chinese Academy of Sciences (Grant Nos. KZCXZ-YW-Q11-03, KZCZ2-YW-Q03-08)     

集合预报开创了业务数值天气预报的新纪元

近年来世界上随着几个气象中心的集合预报投入业务,标志着业务数值天气预报进行了一个新的纪元,在任何进修大气的起初状态是不知道的,只有近似地描述大气,所以确切地说,天气预报问题应该提为大气的相空间中合适的概率密度函数随时间的演变。在误差的增长超出线性动力学范畴之外,集合预报是预报大气概率密度函数演变唯一可行的方法。可用扰动的产生是集合预报的主要问题之一。沿着预报系统的相空间中最不稳定的方向来扰动初始条     

积云团对大气层结影响的模拟研究

文中设计了一个二维完全弹性的积云团数值模式 .模式中考虑了水平均一和时间定常的大尺度抬升速度的影响 .大度抬升速度只对热力学方程和水汽守恒方程有影响 .积云团的启动方式采用冷池和随机扰动相结合的方法 .文中对一个给定温湿层结和大尺度抬升速度情况下 ,就不同扰动结构、水平环境切变、模式区域大小和冷池强度等进行了 1 1个模拟试验 .结果表明 ,在不同扰动结构、是否有水平环境风切变、模式区域不同以及不同云型谱时 ,给定温湿层结和大尺度抬升速度下的积云团活动使得温湿层结向着同一温湿层结调整 .这说明大气由于积云团活动达到准平衡状态 ,这个准平衡状态主要依赖于初始温湿层结和大尺度抬升速度 .这与 Betts积云参数化方案的假设相一致 .