Use of Noise to Augment Training Data: A Neural Network Method of Mineral–Potential Mapping in Regions of Limited Known Deposit Examples

One of the main factors that affects the performance of MLP neural networks trained using the backpropagation algorithm in mineral-potential mapping isthe paucity of deposit relative to barren training patterns. To overcome this problem, random noise is added to the original training patterns in order to create additional synthetic deposit training data. Experiments on the effect of the number of deposits available for training in the Kalgoorlie Terrane orogenic gold province show that both the classification performance of a trained network and the quality of the resultant prospectivity map increasesignificantly with increased numbers of deposit patterns. Experiments are conducted to determine the optimum amount of noise using both uniform and normally distributed random noise. Through the addition of noise to the original deposit training data, the number of deposit training patterns is increased from approximately 50 to 1000. The percentage of correct classifications significantly improves for the independent test set as well as for deposit patterns in the test set. For example, using ±40% uniform random noise, the test-set classification performance increases from 67.9% and 68.0% to 72.8% and 77.1% (for test-set overall and test-set deposit patterns, respectively). Indices for the quality of the resultant prospectivity map, (i.e. D/A, D × (D/A), where D is the percentage of deposits and A is the percentage of the total area for the highest prospectivity map-class, and area under an ROC curve) also increase from 8.2, 105, 0.79 to 17.9, 226, 0.87, respectively. Increasing the size of the training-stop data set results in a further increase in classification performance to 73.5%, 77.4%, 14.7, 296, 0.87 for test-set overall and test-set deposit patterns, D/A, D × (D/A), and area under the ROC curve, respectively.     

Use of Fuzzy Membership Input Layers to Combine Subjective Geological Knowledge and Empirical Data in a Neural Network Method for Mineral-Potential Mapping

Use of GIS layers, in which the cell values represent fuzzy membership variables, is an effective method of combining subjective geological knowledge with empirical data in a neural network approach to mineral-prospectivity mapping. In this study, multilayer perceptron (MLP), neural networks are used to combine up to 17 regional exploration variables to predict the potential for orogenic gold deposits in the form of prospectivity maps in the Archean Kalgoorlie Terrane of Western Australia. Two types of fuzzy membership layers are used. In the first type of layer, the statistical relationships between known gold deposits and variables in the GIS thematic layer are used to determine fuzzy membership values. For example, GIS layers depicting solid geology and rock-type combinations of categorical data at the nearest lithological boundary for each cell are converted to fuzzy membership layers representing favorable lithologies and favorable lithological boundaries, respectively. This type of fuzzy-membership input is a useful alternative to the 1-of-N coding used for categorical inputs, particularly if there are a large number of classes. Rheological contrast at lithological boundaries is modeled using a second type of fuzzy membership layer, in which the assignment of fuzzy membership value, although based on geological field data, is subjective. The methods used here could be applied to a large range of subjective data (e.g., favorability of tectonic environment, host stratigraphy, or reactivation along major faults) currently used in regional exploration programs, but which normally would not be included as inputs in an empirical neural network approach.     

金风口地面塌陷及水源干枯成因及防治

通过对金风口片区地表的调查，分析了地面降沉、塌陷的原因；研究了塌陷对水源的影响；总结了水源干枯的规律及影响因素；并提出了防治的办法及合理化建议。     

山东省集雨增补地下水与水资源可持续利用探讨

分析了山东省水资源特点、开发利用状况以及水资源持续利用所面临的形势，在阐述山东省降雨、地理等自然特点的基础上，认为集雨增补地下水是实现水资源可持续利用的有效途径，提出了集雨增补地下水的技术措施。     

南京市玄武湖水下交通隧道环境地质评价

玄武湖地下交通隧道既在城市,又在水下,环境地质条件极为复杂,是一种脆弱环境下的典型的地质工程。本文对玄武湖地下交通隧道的环境地质条件特别是水文地质条件进行了评价,预测了可能出现的如水污染、地面沉降、地面塌陷、巷道突水等环境地质问题,并提出了相应的防治对策。     

探地雷达检测道路厚度结构的应用现状及进展

对探地雷达检测道路厚度结构的应用现状进行介绍,分析雷达检测道路厚度结构的物理前提.将探地雷达的道路厚度检测技术与常规的钻孔取芯方法进行对比,并根据笔者在工作中应用的实际情况,说明探地雷达在公路厚度结构检测方面有广阔应用前景.     

现代抗震设计理论的发展过程

介绍了现代抗震设计理论的发展过程，基于性态的抗震设计理论的提出背景、发展情况及研究内容；详细介绍了基于性态的抗震设计理论的抗震设防方法；指出了基于性态的抗震设计理论与传统抗震设计理论的主要区别及特点。     

医院住院部大楼抗震性能评价

以某医院住院部大楼为例进行结构抗震性能评价，首先分析了场址的地震环境，开展了地震危险性分析工作，合成了不同地震危险水平下的地震动加速度时程。进而，通过对结构的模型简化，应用时程分析方法分析结构的抗震性能，得出了一些对其改造加固有意义的结论。     

最不利地震动在网壳结构抗震设计中的应用

利用文献提供的最不利地震动，对网壳结构等大跨空间结构进行了分析，分别考察了仅水平输入和三向地震波输入的两种情况，对最不利地震动在网壳结构抗震设计中的适用性进行了探讨，并在一实际工程设计中进行了应用。大跨度空间结构在水平地震动输入时，宜按照的最不利地震动进行设计；在三向地震动同时输入时，应同时分别按所推荐最不利设计地震动和按常规选用的地震动，对大跨度空间结构进行抗震分析。     

Flow and fracture maps for basaltic rock deformation at high temperatures

Understanding how the strength of basaltic rock varies with the extrinsic conditions of stress state, pressure and temperature, and the intrinsic rock physical properties is fundamental to understanding the dynamics of volcanic systems. In particular it is essential to understand how rock strength at high temperatures is limited by fracture. We have collated and analysed laboratory data for basaltic rocks from over 500 rock deformation experiments and plotted these on principal stress failure maps. We have fitted an empirical flow law (Norton’s law) and a theoretical fracture criterion to these data. The principal stress failure map is a graphical representation of ductile and brittle experimental data together with flow and fracture envelopes under varying strain rate, temperature and pressure. We have used these maps to re-interpret the ductile–brittle transition in basaltic rocks at high temperatures and show, conceptually, how these failure maps can be applied to volcanic systems, using lava flows as an example.