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.     

塔里木盆地西部含盐系地层成盐元素的地球化学初步研究

钾矿是我国目前紧缺的矿种之一 ,钾矿的寻找对我国有着十分重要的意义。塔里木盆地是我国第三系地层的广泛发育区域 ,有一定的找钾远景 ,尤其是库车、莎车等地区。通过对塔里木盆地 (坳陷 )西部的区域地质概况、成矿条件等的介绍 ,运用地球化学的分析测试手段 ,对塔里木盆地西部采集到的样品作了地球化学分析。论述了该区成盐沉积的地球化学特征 ,并讨论了该区形成钾矿的可能性。     

从江县翁浪金矿床容矿岩石与围岩蚀变及其找矿标志

文章简单介绍了构造蚀变岩型金矿翁浪金矿床的容矿岩石和围岩蚀变特征，指出该类矿床的找矿标志，对进一步寻找此类矿床有一定的意义。     

宁芜地区大平山铜矿、天台山黄铁矿与古火山机构关系的探讨

以大平山铜矿、天台山黄铁矿矿为例，阐述了该区的矿区、矿床地质及火山机构特征，并从成矿时间、空间及成因三个方面论述了矿床成矿与古火山机构的关系。     

土屋-延东斑岩型铜(钼)矿床多源信息找矿模型

为提高东天山铜镍成矿带内的矿产勘查效益，文章综合了区域成矿环境、矿床特征、地球物理场、地球化学场等成矿信息，尝试建立了土屋—延东斑岩型铜钼矿床的多源信息找矿模型。认为成矿带内铜矿资源远景良好，必将成为我国又一个铜矿资源潜力区。     

锡铁山铅锌矿地质特征、矿床成因及找矿标志

通过找矿工作的实践，认为锡铁山铅锌矿床是由火山喷流沉积—后期热液叠加改造富集的块状硫化物多金属矿床。区域上NW—SE向早古生代形成的裂谷带，三级盆地内沉积的晚奥陶世滩间山群的大理岩与绿片岩系是表区找矿的最佳区段。而绢云绿泥斜长片岩、含碳质绢云绿泥片岩、白色大理岩、条带状大理岩是铅锌矿的最重要的找矿标志。     

叶蜡石传压介质资源初步研究

分析国内叶蜡石传压介质材料供需状况，指出北京赵家台叶蜡石传压介质材料资源枯竭，虽然国内叶蜡石矿石资源丰富，但适宜作叶蜡石传压介质材料资源却极少，急需从资源和材料特性着手，加大研究力度，寻找新的接替资源。     

阿尔泰可可塔勒铅锌矿床围岩蚀变及成因

可可塔勒铅锌矿床受火山喷发中心和沉积洼地控制，铅锌沉淀于海进阶段的局限还原卤水池中；矿下存在大型蚀变带，构成成矿流体对流循环过程中的水-岩作用带；后期造山挤压过程使地层和矿体倒转，矿床最终定位于麦兹倒转向斜之北东倒转翼的东南近转折部位。指出该矿床属海底火山喷流沉积改造型块状硫化物铅锌矿床。     

油房西矿区地球物理特征及找矿标志

油房西矿区位于华北地台北缘，苇塘河断裂西部，具有良好的银多金属找矿前景。通过对油房西矿区物性参数及局部异常特征的分析研究，探讨激电异常与矿体的关系，总结该区找矿标志，指出进一步找矿方向，以期提高地质找矿效果。