维护和加强地质情报工作

以历史演进和当前形势发展对情报工作的新要求为基础,从“情报与信息”、“情报与刊物”、“国内与国外”、“专职与兼职”4个方面讨论了地质情报工作的性质与定位。从情报的搜集、分析和服务3个方面讨论了情报意识的重要性。情报工作可分为战略情报与战术情报两大类,前者面对的是全局性任务,后者则是关键性任务。最后,对情报机构的建设和情报人才的培养进行了探讨,指出中国地质调查局作为中国地质工作的领衔机构,要十分重视维护和加强地质情报工作。     

南北地震带地震活动强度变化特征及其神经网络模型

南北地震带是中国大陆的一条主要活动地震带, 20世纪以来有很多大地震集中发生在这条地震带上。为了进一步探讨南北地震带地震活动强度变化的规律并对其进行中期预测,在研究了南北地震带逐年最大地震强度演化特征及其机理的基础上,建立了南北地震带地震强度序列变化的一种模式,建模中采用了人工神经网络技术,并提出了一种简易实用的能够获得较隹预测效果的确定神经网络输入窗口大小的方法。结果表明: 南北地震带的地震活动具有强弱分期轮回的特征; 用人工神经网络建模的预测结果与实际资料的对比检验中误差较小,因而该模型可作为南北地震带地震活动强度变化的预测模型。     

Mapping of hard rock aquifer system and artificial recharge zonation through remote sensing and GIS approach in parts of Perambalur District of Tamil Nadu,India

Mapping of hard rock aquifer system and artificial recharge zonation were carried out in an area of 325 km^2 in parts of the Perambalur District,Tamil Nadu,India.This district has been declared as one of the over-exploited regions in Tamil Nadu by the Central Groundwater Board.To raise the groundwater level,suitable recharge zones were identified and artificial recharge structures are suggested using geomatics technology in the present study.To this end,various thematic maps concerning lithology,soil,geomorphology,land use,land cover,slope,lineament,lineament density,drainage,drainage density and groundwater depth level were prepared.Fissile hornblende gneiss(244 km^2)covered most of the study area followed by charnockites(68 km^2).Structural hills and rocky pediments characterize the major geomorphological features in the targeted area,and are followed by deep moderated pediments.The area is mostly used as crop and fallow land,followed by scrub land and deciduous forest.In the study area,the slopes are predominantly very gentle(142 km^2)and nearly level(66 km^2)ones.Besides,Groundwater level data of 58 wells have been generated,in which the minimum and maximum depth were 3 and 28 m respectively.Integration under the GIS environment has been carried out using all the thematic layers to identify the groundwater prospect zone through the introduction of weight and rank methods.Integrated output performances were classified into very poor,poor,moderate,good and excellent categories.All these classes were further divided into two groups as suitable and non-suitable area for the selection of recharge sites.Hard rock fractures were mapped as lineaments from satellite images,and besides that,rose diagram was also generated to find out the trend of the fracture.Furthermore,fracture data of 146 numbers have been collected using Brunton compass to generate rose diagram and were correlated with the rose diagram derived from lineaments.The present study significantly brought up a few areas such as Ammapalayam,Melapuliyur,Senjeri and around Siruvachur for artificial recharge.     

Artificial neural networks and cluster analysis in landslide susceptibility zonation

A landslide susceptibility analysis is performed by means of Artificial Neural Network (ANN) and Cluster Analysis (CA). This kind of analysis is aimed at using ANNs to model the complex non linear relationships between mass movements and conditioning factors for susceptibility zonation, in order to identify unstable areas. The proposed method adopts CA to improve the selection of training, validation, and test records from data, managed within a Geographic Information System (GIS). In particular, we introduce a domain-specific distance measure in cluster formation. Clustering is used in data pre-processing to select non landslide records and is performed on the whole dataset, excluding the test set landslides. Susceptibility analysis is carried out by means of ANNs on the so-generated data and compared with the common strategy to select random non-landslide samples from pixels without landslides. The proposed method has been applied in the Brembilla Municipality, a landslide-prone area in the Southern Alps, Italy. The results show significant differences between the two sampling methods: the classification of the test set, previously separated and excluded from the training data, is always better when the non-landslide patterns are obtained using the proposed cluster sampling. The case study validates that, by means of a domain-specific distance measure in cluster formation, it is possible to introduce expert knowledge into the black-box modelling method, implemented by ANNs, to improve the predictive capability and the robustness of the models obtained.     

Amelioration of Carbon Removal Prediction for an Activated Sludge Process using an Artificial Neural Network (ANN)

A dynamic simulation model of the Ankara central wastewater treatment plant (ACWTP) was evaluated for the prediction of effluent COD concentrations. Firstly, a mechanistic model of the municipal wastewater treatment process was developed based on Activated Sludge Model No. 1 (ASM1) by using a GPS‐X computer program. Then, the mechanistic model was combined with a feed‐forward back‐propagation neural network in parallel configuration. The appropriate architecture of the neural network models was determined through several iterative steps of training and testing of the models. Both models were run with the data obtained from the plant operation and laboratory analysis to predict the dynamic behavior of the process. Using these two models, effluent COD concentrations were predicted and the results were compared for the purpose of evaluation of treatment performance. It was observed that the ASM1 ANN model approach gave better results and better described the operational conditions of the plant than ASM1.     

土地利用变化预测的案例推理方法

当前,基于案例的推理(Case-Based Reasoning,CBR)在解决复杂的地学问题时.对地学案例的表达和历史案例的相似性计算与推理存在明显缺陷,需要在CBR的表达模型和空间相似性计算与推理算法进行拓展.本文针对土地利用变化问题,首先在分析土地利用变化各种定量方法基础上,提出利用CBR进行土地利用变化分析的研究思路:其次,针对土地利用变化的空间特性及隐含的空间关系特性.给出土地利用变化案例的表达模型,案例间内蕴空间关系抽取算法,以及考虑案例间空间关系的CBR相似性推理模型:最后,进行珠江口区域土地利用变化的CBR方法试验.预测精度达到80%.为了进一步评价CBR方法对土地利用变化预测的有效性.在实例部分采用同样的实验数据进行贝叶斯网络的预测方法实验.由两种方法对比可知.CBR是从复杂到简单进行地学问题求解的一种有效方法.     

基于耦合的地理模拟优化系统

尽管GIS在涉及空间信息的许多学科和行业有广泛的应用,但其在对过程进行模拟和优化方面存在严重的功能不足。本文提出地理模拟优化系统GeoSOS的概念与实现方法。进一步建立了GeoSOS1.0的模拟优化平台,作为GIS的重要补充工具。包含了三个重要部分:地理元胞自动机(CA)、多智能体系统(MAS)、生物智能(SI)。其核心内容就是根据微观个体的相互作用,达到模拟和优化的目的。根据Tobler地理学的第一定律,提出了GeoSOS的统一的相互作用规则。GeoSOS具备将模拟和优化耦合起来的功能。将动态模拟模型与空间优化模型耦合起来,使得优化的方案具有一定的前瞻性。对比实验结果发现,耦合模型产生的效用值比非耦合模型分别高出4.3%(点状优化)和4.1%(线状优化),表明GeoSOS能够改善优化的结果。     

河南省数字地震台网自动量算的震级与人工量算的震级的偏差分析

对2007年12月-2008年9月河南省数字地震台网资料自动量算的震级与人工量算的震级进行了比较和分析。结果表明：由MSDP软件自动量算的震级与人工量算的震级偏差较小,且自动量算的震级可以满足地震定位精度的要求,在地震速报时可采用自动量算的震级。     

平台地形对地震地面运动特征周期值的影响

本文人工合成了36条代表不同频谱特性的地震动,构造了简单的平台地形,并利用人工合成地震动作为平台地形计算输入地震波,获得了地表观测点的时程和反应谱.在此基础上,分析了具有不同高度、侧向坡降和介质阻尼等的平台地形对地震动特征周期值的影响.研究的结果表明:当平台高度与入射地震波优势波长相比较小时,平台的高度、侧向坡降、阻尼比等对地震动特征周期值的影响不大,此时单个平台地形地表地震动的特征周期主要依赖于入射地震动的特征周期,而且一般比入射地震动的特征周期略有增大;当平台高度与入射地震波优势波长相比较大时,平台高度对地表地震动特征周期影响较大.     

Evaluation of total load sediment transport formulas using ANN

The calculated results from various sediment transport formulas often differ from each other and from measured data. Some parameters in the sediment transport formulas are more effective than others to estimate total sediment load. In this study, an Artificial Neural Network （ANN） model is trained using four dominant parameters of sediment transport formulas. ANN models are able to reveal hidden laws of natural phenomena such as sediment transport process. The results of ANN and some total bed material load sediment transport formulas have been compared to indicate the importance of variables which can be used in developing sediment transport formulas. To train ANN, average flow velocity, water surface slopes, average flow depth, and median particle diameter are used as dominant parameters to estimate total bed material load. Two hundreds and fifty samples are used to train the ANN model. Twenty-four sets of field data not used in the training nor calibration of ANN are used to compare or verify the accuracy of ANN and some well-known total bed material load formulas. The test results show that the ANN model developed in this study using minimum number of dominant factors is a reliable and uncomplicated method to predict total sediment transport rate or total bed material load transport rate. Results show that the accuracy of formulas in descending order are those by Yang （1973）, Laursen （1958）, Engelund and Hansen （1972）, Ackers and White （1973）, and Toffaleti （1969）. These results are similar to those made by ASCE （1982） based on laboratory and field data not used in this paper. Study results also show that the formulas based on physical laws of sediment transport, like those formulas that were developed based on power concept, are more accurate than other formulas for estimating total bed material sediment load in rivers.