2004年江苏火箭人工增雨降温分析

为缓解夏季高温,减轻用电负荷,2004年江苏省实施了火箭人工增雨降温作业.本文利用多普勒天气雷达和地面各项观测资料,对2004年11 d 27次火箭增雨降温作业前后降水与降温情况进行统计分析,在此基础上重点分析了8月5日常州市的火箭人工增雨降温作业过程,分析了这次作业的效果,讨论了强对流云催化降温的机理和火箭增雨降温作业的着眼点,为以后此项工作的开展提供依据.     

Prediction of ground subsidence in Samcheok City,Korea using artificial neural networks and GIS

This study shows the construction of a hazard map for presumptive ground subsidence around abandoned underground coal mines (AUCMs) at Samcheok City in Korea using an artificial neural network, with a geographic information system (GIS). To evaluate the factors governing ground subsidence, an image database was constructed from a topographical map, geological map, mining tunnel map, global positioning system (GPS) data, land use map, digital elevation model (DEM) data, and borehole data. An attribute database was also constructed by employing field investigations and reinforcement working reports for the existing ground subsidence areas at the study site. Seven major factors controlling ground subsidence were determined from the probability analysis of the existing ground subsidence area. Depth of drift from the mining tunnel map, DEM and slope gradient obtained from the topographical map, groundwater level and permeability from borehole data, geology and land use. These factors were employed by with artificial neural networks to analyze ground subsidence hazard. Each factor’s weight was determined by the back-propagation training method. Then the ground subsidence hazard indices were calculated using the trained back-propagation weights, and the ground subsidence hazard map was created by GIS. Ground subsidence locations were used to verify results of the ground subsidence hazard map and the verification results showed 96.06% accuracy. The verification results exhibited sufficient agreement between the presumptive hazard map and the existing data on ground subsidence area. An erratum to this article can be found at     

Applicability of artificial neural networks for obtaining velocity models from synthetic seismic data

Seismic velocity analysis is a crucial part of seismic data processing and interpretation which has been practiced using different methods. In contrast to time consuming and complicated numerical methods, artificial neural networks (ANNs) are found to be of potential applicability. ANN ability to establish a relationship between an input and output space is considered to be appropriate for mapping seismic velocity corresponding to travel times picked from seismograms. Accordingly a preliminary attempt is made to evaluate the applicability of ANNs to determine velocity and dips of dipping layered earth models corresponding to travel time data. The study is based on synthetic data generated using inverse modeling approach for three earth models. The models include a three-layer structure with same dips and same directions, a three-layer model with different dips and same directions, as well as a two-layer model with different dips and directions. An ANN structure is designed in three layers, namely, input, output, and hidden ones. The training and testing process of the ANN is successfully accomplished using the synthetic data. The evaluation of the applicability of the trained ANN to unknown data sets indicates that the ANN can satisfactorily compute velocity and dips corresponding to travel times. The error intervals between the desired and calculated velocity and dips are shown to be acceptably small in all cases. The applicability of the trained ANN in extrapolating is also evaluated using a number of data outside of the range already known to ANN. The results indicate that the trained ANN acceptably approximates the velocity and dips. Furthermore, the trained ANN is also evaluated in terms of capability of handling deficiency in input data where acceptable results were also achieved in velocity and dip calculations. Generally, this study shows that velocity analysis using ANNs can promisingly tackle the challenge of retrieving an initial velocity model from the travel time hyperbolas of seismic data.     

Predicting standard penetration test N-value from cone penetration test data using artificial neural networks

Standard Penetration Test(SPT) and Cone Penetration Test(CPT) are the most frequently used field tests to estimate soil parameters for geotechnical analysis and design.Numerous soil parameters are related to the SPT N-value.In contrast,CPT is becoming more popular for site investigation and geotechnical design.Correlation of CPT data with SPT N-value is very beneficial since most of the field parameters are related to SPT N-values.A back-propagation artificial neural network(ANN) model was developed to predict the N6o-value from CPT data.Data used in this study consisted of 109 CPT-SPT pairs for sand,sandy silt,and silty sand soils.The ANN model input variables are:CPT tip resistance(q_c),effective vertical stress(σ'_v),and CPT sleeve friction(f_s).A different set of SPT-CPT data was used to check the reliability of the developed ANN model.It was shown that ANN model either under-predicted the N_(60)-value by 7-16%or over-predicted it by 7-20%.It is concluded that back-propagation neural networks is a good tool to predict N_(60)-value from CPT data with acceptable accuracy.     

用于成像光谱数据特征矿物识别的人工神经网络结构研究

当某一问题很难甚至无法用数学方法建立精确模型时,人工神经网络的方法则显示了优势。对于一个具体问题,采用何种网络结构是至关重要的。本文以美国内华达州Cuprite矿区成像光谱数据特征矿物识别为例,采用6种不同结构的多层前馈网络模型,从其训练难度、运算效率及识别效果等方面进行了综合对比分析。     

人工增雨研究和作业的进展以及提高科学化的可行途径——“人工增雨研究专辑”前言

我国人工增雨研究有很好的成果,有大规模作业的丰富经验,近年来又有很大进步。"人工增雨研究专辑"选登其中一些具有普遍意义的成果和经验,以及未来近期内可以再进一步提高的一些思考和建议。作者试图先对这些经验和问题进行归纳概述和分析讨论。大体上说,这些成功的研究成果和作业经验有:选择有利于增雨的历史天气—气候背景条件、临近作业时对作业云的判断、机理的数值模拟、凝结核和雨滴及它们的谱分布规律和新催化剂的研制等。未来近期内应该做、而且可以有结果的使作业进一步科学化的工作,除结合经验介绍而指出的一些外,关键是:1)尽量做到观测—预报—作业—效果检验一体化;直接面对增雨作业效果本身进行检验,这样才能使作业和效果检验科学化。为此应用控制论的观点和方法(尤其是工程控制论和自然控制论)是可行的。2)要开展暖性对流云催化作业的研究。3)一定要对已有的工作经验进行系统总结,并一定要将研究工作和作业实践密切结合起来。4)一定要气象和水利部门协调起来,才能充分有效地解决四度空间的水资源利用问题。此外,还涉及其他一些人工影响天气的问题,如防雹、消雾等。     

Separating wheat from chaff: Diatom taxon selection usingan artificial neural network pruning algorithm

This study addresses the question of what diatom taxa to includein a modern calibration set based on their relative contribution in apalaeolimnological calibration model. Using a pruning algorithm for ArtificialNeural Networks (ANNs) which determines the functionality of individual taxa interms of model performance, we pruned the Surface Water Acidification Project(SWAP) pH-diatom data-set until the predictive performance of thepruned set (as assessed by a jackknifing procedure) was statistically differentfrom the initial full-set. Our results, based on the validation at each5% data-set reduction, show that (i) 85% of the taxa canbe removed without any effect on the pH model calibration performance, and (ii)that the complexity and the dimensionality reduction of the model by theremoval of these non-essential or redundant taxa greatly improve therobustness of the calibration. A comparison between the commonly usedmarginal criteria for inclusion (species tolerance andHill's N2) and our functionality criterion shows that the importance ofeach taxon in an ANN palaeolimnological model calibration does not appear todepend on these marginal characteristics.     

Knowledge Recovery for Continental-Scale Mineral Exploration by Neural Networks

This study is concerned with understanding of the formation of ore deposits (precious and base metals) and contributes to the exploration and discovery of new occurrences using artificial neural networks. From the different digital data sets available in BRGM's GIS Andes (a comprehensive metallogenic continental-scale Geographic Information System) 25 attributes are identified as known factors or potential factors controlling the formation of gold deposits in the Andes Cordillera. Various multilayer perceptrons were applied to discriminate possible ore deposits from barren sites. Subsequently, because artificial neural networks can be used to construct a revised model for knowledge extraction, the optimal brain damage algorithm by LeCun was applied to order the 25 attributes by their relevance to the classification. The approach demonstrates how neural networks can be used efficiently in a practical problem of mineral exploration, where general domain knowledge alone is insufficient to satisfactorily model the potential controls on deposit formation using the available information in continent-scale information systems.     

山西晋祠泉复流时间的人工神经网络预测

由于岩溶地下水的长期过量，晋祠泉自1994年出现断流至今，目前正在兴建中的万家寨引黄工程将使晋祠泉域的地下水得到一定程度的涵养，因此晋祠泉能与与何时复流已经成为许多人关心的问题。地对上述问题，本文应用一种改进的人工神经网络的方法来分析预测晋祠泉的流量变化态势，并大致给出了复流时间。     

塔克拉玛干沙漠腹地地下水质的应用评价

长期利用高矿化度地下水灌溉造成沙质土壤的次生盐渍化是塔克拉玛干沙漠腹地建立人工生态系统的主导限制因素之一。据１９９２年与１９９４年在塔克拉玛干沙漠腹地满参与中三点２年的试验研究结果认为：在灌溉水的适宜应用条件下，利用矿化度为４ｇ／Ｌ～５ｇ／Ｌ的地下水灌溉种植瓜果蔬菜和固沙植物是能够成功的；生长季内土壤含盐量＜０．１％。