Application of <Emphasis Type="Italic">fuzzy logic and data mining</Emphasis> techniques as tools for qualitative interpretation of acid mine drainage processes

In this article, a set of clustering algorithms based on Fuzzy Logic and Data Mining are applied, allowing to obtain data in the form of linguistic rules and charts about the behaviour of the Tinto and Odiel river estuary (SW Spain) affected by Acid Mine Drainage (AMD). In order to provide researchers with no skills on data mining techniques an easy and intuitive interpretation, we have developed a computer tool based on fuzzy logic that allows immediate qualitative analysis of the data contained in a data from the estuary water chemical analyses, and serves as a contrast to functioning models previously proposed with classical statistics.     

基于数据仓库的数据挖掘技术

从数据挖掘的产生出发，到发展的各个分支，讨论了各种数据挖掘方法的特点和应用对象。介绍典型的算法、它们的优化改进和变形，并分析数据挖掘技术将来面临的问题及发展方向。     

Contrast of evolution models for agricultural contaminants in ground waters by means of fuzzy logic and data mining

This work aims at contrasting, by means of a set of fuzzy logic- and data mining-based algorithms, the functioning model of a detritic aquifer undergoing overexploitation and nitrate excess input coming from strawberry and citrus intensive crops in its recharge zone. To provide researchers unskilled in data mining techniques with an easy and intuitive interpretation, the authors have developed a computer tool based on fuzzy logic that allows immediate qualitative analysis of the data contained in a data mass from the water chemical analyses, and serves as a contrast to functioning models previously proposed with classical statistics. M.L. de la Torre and J.A. Grande belongs to Water Resources and Quality Research Group. J.M. Andujar and J. Aroba belongs to Control and Robotics Research Group     

基于变网格聚类的离群数据的研究

随着计算机技术的迅速发展,计算机在过程控制中起着越来越重要的作用,在集散控制系统中,上位机通过PCU(Process ControlUnit)传送来的数据进行分析处理而后对PCU实现参数整定、报警与管理。因此这一领域的数据挖掘方法研究具有十分重要的意义。文章将根据这一领域的特殊情况,提出一种基于变网格的聚类分析算法分析过程数据库中的离群数据,从而实现系统的预测报警功能。     

基于Weka的地震数据挖掘系统的设计与实现

基于开源的数据挖掘系统Weka,使用Java语言及面向对象的思想,设计并实现了地震数据挖掘系统。根据地震数据资料的特点,将数据挖掘的核心技术（聚类分析、关联规则分析等）引入到该系统中,其中聚类分析选用DBSCAN作为核心算法,关联规则分析选用Apriori作为核心算法。用户使用该系统只需在交互界面选择相关参数,即可实现调用数据挖掘算法来分析地震数据,发现探索其隐含规律。     

空间数据挖掘技术

近年来，伴随着空间信息获取技术的快速发展，不同类型的空间数据库以及数据库中的数据量正在不断增长，如何从这些海量数据中提取出隐含的信息和知识已成为人们研究的热点。空间数据挖掘就是为了解决这一科学问题而提出的。该文在回顾已有空间数据挖掘规则的基础上。指出了空间数据挖掘过程中存在的主要问题，进而探讨了未来空间数据挖掘的主要研究方向。     

The Theoretical Links Between Sequential Gaussian Simulation, Gaussian Truncated Simulation, and Probability Field Simulation

Sequential Gaussian simulation (sgsim), Gaussian truncated simulation (gtsim), and probability field simulation (pfsim) are three algorithms frequently used for conditional stochastic simulation. They were developed independently and are seen as different algorithms in applications. This paper establishes that gtsim and pfsim can be bridged by a simple quantile transform between Gaussian and uniform distributions. As for the sgsim algorithm, the normal score back-transform can be seen as a series of truncations of the simulated Gaussian field. All three algorithms are shown to be applicable to both continuous and categorical variables. In practice, gtsim can be most often replaced by the more CPU-efficient pfsim algorithm.     

地震反演与非线性随机优化方法

地震反演问题常常需要一些非线性全局最优化的方法。近年来，基于生物学、物理学、人工智能和一些非线性科学而发展了一些具有全局优化性能且通用性强的随机搜索算法，如：遗传算法、模拟退火、禁忌搜索和混沌搜索等。作者主要针对地震勘探反演问题的应用综述了这四种非线性随机优化方法的原理和特点，并对它们的性能作了一定的分析和比较。     

彬长矿区采陷裂缝影响因素分析

对彬长矿区地面裂缝进行了详细调查,指出该矿区地面裂缝的主要为开采塌陷引起的采陷裂缝,并对采陷裂缝的发育特征进行了归类。分析认为：采深增大,基岩厚度增大,覆岩的稳定性增强,对地面裂缝的规模控制也增强,60°～70°为塌陷裂缝边界范围圈定的指标值;工作面变宽,采厚变大,会加剧地面裂缝的产生和发展;“上硬下软”的地层结构,有利控制地表移动变形,关键层厚度增大,可以明显减缓地表移动;松散层厚度愈大,抗变形能力愈大的土层,其裂缝愈不发育;V型沟谷对裂缝的形成和发展影响较小,斜坡则对裂缝的发育有加剧作用;降雨对裂缝的发育有明显的加速作用。     

棘爪机构伸缩式钻孔水力开采装置的设计与试验

钻孔水力采矿由于安全系数高、工程效益和生态环境保护好等优点在国外一直被研究和使用。我国相关的科研院所也正在开展研究。本文在分析研究国外钻孔水力开采装置的基础上,研制了具有自主知识产权的棘爪机构伸缩式钻孔水力开采装置。该装置利用机械式伸缩水枪的方式扩大水力开采矿层的范围,创新性地采用棘爪机构固定水枪的伸出角度,从而提高单孔开采量,降低开采成本。初步野外实验结果表明,该装置结构设计合理、工作可靠性高,可有效进行开采,达到了预期的目标。     

Monte Carlo simulation of permeability fields and reservoir performance predictions with SVD parameterization in RML compared with EnKF

In a previous paper, we developed a theoretical basis for parameterization of reservoir model parameters based on truncated singular value decomposition (SVD) of the dimensionless sensitivity matrix. Two gradient-based algorithms based on truncated SVD were developed for history matching. In general, the best of these “SVD” algorithms requires on the order of 1/2 the number of equivalent reservoir simulation runs that are required by the limited memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) algorithm. In this work, we show that when combining SVD parameterization with the randomized maximum likelihood method, we can achieve significant additional computational savings by history matching all models simultaneously using a SVD parameterization based on a particular sensitivity matrix at each iteration. We present two new algorithms based on this idea, one which relies only on updating the SVD parameterization at each iteration and one which combines an inner iteration based on an adjoint gradient where during the inner iteration the truncated SVD parameterization does not vary. Results generated with our algorithms are compared with results obtained from the ensemble Kalman filter (EnKF). Finally, we show that by combining EnKF with the SVD-algorithm, we can improve the reliability of EnKF estimates.     

软岩矿区地面下沉及其对工业建筑物影响分析

根据鲁中软岩矿区开采引起地面下沉及地面井塔楼等建筑物倾斜变形的工程实际,采用非确定性研究方法,将深埋破碎金属矿体开采引起地面沉陷或岩体移动变形这一客观现象视为一随机事件,建立了开采引起地面沉陷或岩体移动变形分析的数学模型。利用该模型可对开采引起地面沉陷及其对地表工业建筑物影响进行具体分析评价。通过具体计算分析结果表明,在软岩地层条件下矿体开采地表移动变形的影响范围有随着采深的增加而逐渐增大的趋势。     

An evolutionary approach to modelling the thermomechanical behaviour of unsaturated soils

A new data‐mining approach is presented for modelling of the stress–strain and volume change behaviour of unsaturated soils considering temperature effects. The proposed approach is based on the evolutionary polynomial regression (EPR), which unlike some other data‐mining techniques, generates a transparent and structured representation of the behaviour of systems directly from raw experimental (or field) data. The proposed methodology can operate on large quantities of data in order to capture nonlinear and complex relationships between contributing variables. The developed models allow the user to gain a clear insight into the behaviour of the system. Unsaturated triaxial test data from the literature were used for development and verification of EPR models. The developed models were also used (in a coupled manner) to produce the entire stress path of triaxial tests. Comparison of the EPR model predictions with the experimental data revealed the robustness and capability of the proposed methodology in capturing and reproducing the constitutive thermomechanical behaviour of unsaturated soils. More importantly, the capability of the developed models in accurately generalizing the predictions to unseen data cases was illustrated. The results of a sensitivity analysis showed that the models developed from data are able to capture and represent the physical aspects of the unsaturated soil behaviour accurately. The merits and advantages of the proposed methodology are also discussed. Copyright © 2014 John Wiley & Sons, Ltd.     

A mathematical programming approach for scheduling equipment in a surface coal mining operation

Summary A mathematical programming model for scheduling open pit mining was developed and validated using data from a surface mining operation. A two-phase solution procedure was used involving repeated evaluations of an integer scheduling model and a simple transportation model.     

Drought Monitoring Using Data Mining Techniques: A Case Study for Nebraska, USA

Drought has an impact on many aspects of society. To help decision makers reduce the impacts of drought, it is important to improve our understanding of the characteristics and relationships of atmospheric and oceanic parameters that cause drought. In this study, the use of data mining techniques is introduced to find associations between drought and several oceanic and climatic indices that could help users in making knowledgeable decisions about drought responses before the drought actually occurs. Data mining techniques enable users to search for hidden patterns and find association rules for target data sets such as drought episodes. These techniques have been used for commercial applications, medical research, and telecommunications, but not for drought. In this study, two time-series data mining algorithms are used in Nebraska to illustrate the identification of the relationships between oceanic parameters and drought indices. The algorithms provide flexibility in time-series analyses and identify drought episodes separate from normal and wet conditions, and find relationships between drought and oceanic indices in a manner different from the traditional statistical associations. The drought episodes were determined based on the Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI). Associations were observed between drought episodes and oceanic and atmospheric indices that include the Southern Oscillation Index (SOI), the Multivariate ENSO Index (MEI), the Pacific/North American (PNA) index, the North Atlantic Oscillation (NAO) Index, and the Pacific Decadal Oscillation (PDO) Index. The experimental results showed that among these indices, the SOI, MEI, and PDO have relatively stronger relationships with drought episodes over selected stations in Nebraska. Moreover, the study suggests that data mining techniques can help us to monitor drought using oceanic indices as a precursor of drought.     

瞬变电磁法资料的快速反演成像技术与软件

研究开发了 2种非常实用的瞬变电磁资料的快速反演成像技术,即基于薄板模型的S—反演算法和基于波场变换的拟波动方程偏移成像方法,并在Win98环境下,用VC ++开发出了可视化的集成处理软件包。通过对实际资料的处理,证明了该方法技术及软件的有效性和实用性。     

Modification of subsidence parameters for sloping ground surfaces by the rays projection method

Summary This paper deals with the determination of subsidence parameters on a sloping ground surface above a completely mined underground panel in an inclined seam. A rays projection method is analytically developed to derive the subsidence components on the sloping ground surface from the corresponding ones on an assumed equivalent horizontal surface through the point of the mean elevation of the sloping surface above the extracted panel. Using the derived equations, the variation of subsidence with the inclination of the ground surface is analysed. An example is given to illustrate the application of the rays projection method in predicting subsidence effects on a sloping surface.     

遗传算法在反演三维地下水流模型参数中的应用

本文以非均质各向同性承压三维非稳定流为理想模型，结合有限元法讨论了用遗传算法反演水文地质参数问题，计算结果表明，本文在瘴简单遗传算法（SGA）的基础上提出的优体克隆＋子体优生遗传算法（BCC－YGCD－GA）具有收敛速度快，解的精度高和避免出现早期等优点，在水资源评价和矿床疏干计算中有广阔的应用前景。     

Quasi-Monte Carlo integration on the grid for sensitivity studies

In this paper we present error and performance analysis of quasi-Monte Carlo algorithms for solving multidimensional integrals (up to 100 dimensions) on the grid using MPI. We take into account the fact that the Grid is a potentially heterogeneous computing environment, where the user does not know the specifics of the target architecture. Therefore parallel algorithms should be able to adapt to this heterogeneity, providing automated load-balancing. Monte Carlo algorithms can be tailored to such environments, provided parallel pseudorandom number generators are available. The use of quasi-Monte Carlo algorithms poses more difficulties. In both cases the efficient implementation of the algorithms depends on the functionality of the corresponding packages for generating pseudorandom or quasirandom numbers. We propose efficient parallel implementation of the Sobol sequence for a grid environment and we demonstrate numerical experiments on a heterogeneous grid. To achieve high parallel efficiency we use a newly developed special grid service called Job Track Service which provides efficient management of available computing resources through reservations.     

Doppler weather radar based nowcasting of cyclone Ogni

In this paper, we describe offline analysis of Indian Doppler Weather Radar (DWR) data from cyclone Ogni using a suite of radar algorithms as implemented on NEXRAD and the advanced algorithms developed jointly by the National Severe Storms Laboratory (NSSL) and the University of Oklahoma. We demonstrate the applicability of the various algorithms to Indian radar data, the improvement in the quality control and evaluate the benefit of nowcasting capabilities in Indian conditions. New information about the tropical cyclone structure, as derived from application of the algorithms is also discussed in this study.