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.     

生态交界带特征与动态变化机理分析

文章论述了生态交界带是两个以上生态系统交接的过渡带，它具有特有的生态系统结构，生态交界带生态系统的结构，功能和物质，能是以及信息的交流与相邻生态系统有密切的联系，但又表现出明显的异质性，其动态变化机理显示生态交界带内物质，能量和信息的均衡状况。     

北京西部山地景观生态整治与景观规划--以北京房山区史家营乡的典型研究为例

北京市西部山区是因矿区开发使山地景观生态环境破坏的重点地区,在北京市生态环境整治与建设中具有十分重要的典型意义。不合理的煤炭资源开采导致了矿点密度大,采空塌陷区分布广,水资源急剧减少,水质污染严重,植被大规模遭破坏,水土流失严重,泥石流等灾害加重以及煤尘污染问题突出等一系列环境问题,呈现出山地生态系统遭受严重破坏的景观生态特征。本文以史家营为例,在典型调查的基础上,规划了景观生态环境恢复与重建区、种植养殖和观光生态农业示范区、果粮养殖生态农业区、林草放牧生态畜牧业示范区、生态旅游区的五个景观规划区域,探讨了生态型矿业、中低山特色生态农业和生态旅游发展的产业发展模式,并初步提出了景观生态整治的措施。     

湘北红壤坡地雨水过程的水土流失及其影响

模拟主要利用方式构建的垫面为主导因子,采用径流场实测方法,通过4a(1998～2001年)实地观测试验,剖析不同下垫面对雨水地表径流及其过程的影响。研究表明:降雨(时间分布及其强度)是影响地表径流特征的主导因素;年降雨量与地表径流量和系统水土流失量呈正相关。不同垫面地表径流产量有显著性差异;雨水径流过程的土壤和养分流失有相似的表现。在假定雨水年份的基础上,应用不同雨水年份出现概率指标,推算出:湘北红壤坡地现今利用状况下,雨水产地表径流过程的年均水土流失量及其养分(有机质、N、P、K)损失量;和可最大开发利用时,可能带来的水土流失与养分损失。提出"利用坡地集雨利用优势,构建与单元生态系统水循环平衡相适应的坡地农林复合生态系统,通过水平衡生态建设来维系生态系统的水分平衡"观点。     

台湾西南部泥岩土地利用型态与环境劣化趋势之分析

台湾泥岩恶地因其特殊之土壤理化性质及区域微气候的相互恶性循环，衍生生态环境等问题。在以往之35年，裸化面积从1967年的2532hm^2，至2001年11月已扩大为15539hm^2。其表土大量流失、崩塌、河川含砂量激增、植被退化与微气候变异等破坏，更显示泥岩之环境劣化问题。以SPOT卫星影像判识泥岩裸露面积，探讨不同时期之地景变异，并应用地景生态理论量化泥岩地表不同土地利用之空间结构变化，分析地景结构变迁之生态内涵与生态指标意义。因子分析结果其主要变异为多样性因子与形状破碎型因子所影响，而应用马可夫链模式计算出不同时期之土地利用变迁内涵，此结果搭配指标分析可解释泥岩裸露面积与竹林面积皆持续扩大，乃由人为土地利用之活动及植被变迁所引起。     

森林沼泽区矿产资源地球化学勘查

通过景观调查和中大比例尺化探方法试验,对我国东北森林沼泽区景观地球化学特征、元素存在形式、迁移富集机制、影响因素、采样介质、采样粒度、采样方法等理论和方法技术问题进行研究,提出该类地区元素表生分散富集的某些地球化学规律和中大比例尺化探工作方法.     

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.     

The design of post‐mining landscapes using geomorphic principles

Nature can provide analogues for post‐mining landscapes in terms of landscape stability and also in terms of the rehabilitated structure ‘blending in’ with the surrounding undisturbed landscape. In soil‐mantled landscapes, hillslopes typically have a characteristic pro?le that has a convex upper hillslope pro?le with a concave pro?le lower down the slope. In this paper hillslope characteristic form is derived using the area–slope relationship from pre‐mining topography at two sites in Western Australia. Using this relationship, concave hillslope pro?les are constructed and compared to linear hillslopes in terms of sediment loss using the SIBERIA erosion model. It is found that concave hillslopes can reduce sediment loss by up to ?ve times that of linear slopes. Concave slopes can therefore provide an alternative method for the construction of post‐mining landscapes. An understanding of landscape geomorphological properties and the use of erosion models can greatly assist in the design of post‐mining landscapes. Copyright © 2003 John Wiley & Sons, Ltd.