Spatially irregular sedimentation in a small, morphologically complex lake: implications for paleoenvironmental studies

A sub-bottom acoustic survey of Devil Lake on the Canadian Shield in southern Ontario reveals three acoustic facies: (I) a moderately acoustically transparent, laminated sequence interpreted as a glacilacustrine deposit in glacial Lake Iroquois or a subsequent phase in water depths up to 200 m greater than at present, (II) a transitional more transparent, less layered facies interpreted as being deposited in a more distal glacial lake from erosion of sediment in the watershed exposed by the failure of the ice dam and lowering of the glacial lake before stabilization by the development of forests, and (III) an acoustically transparent facies with similar transmissivity to the water column, interpreted as Holocene gyttja. Each is spatially variable in extent and thickness in response to those processes. There is only a very weak relation between sediment thickness and the water depth in which it was deposited. Wave processes prevent deposition in water depths less than about 6 m and evidence of erosion to the greatest depths of the lake (>40 m) is pervasive. The data demonstrate the value of acoustic survey in assessing lacustrine processes and the history of lakes, and the significance of such documentation in planning a coring program and in interpreting the results.     

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.     

Economic Filters Applied to Quantitative Mineral Resource Simulations

The U.S. Geological Survey has developed a technique that allows mineral resource experts to apply economic filters to estimates of undiscovered mineral resources. This technique builds on previous work that developed quantitative methods for mineral resource assessments. A Monte-Carlo calculation uses mineral deposit models to estimate commodity grades and tonnages of undiscovered deposits. The results then are analyzed using simple estimates of capital expenditures and daily operating costs for a mine and associated mill. The daily operating costs and the value of the ore are used to calculate the net present value of the deposit, which is compared to the capital expenditures to determine whether the deposit is economic. Repetition of these calculations for many deposits produces a table that can be interpreted in terms of the probability of there being deposits that have anet present value exceeding some specified amount. Sample calculations indicate that applying economic filters to simulated mineral resources might change the perception of the results compared to presenting the calculations in terms of the expected mean gross-in-place value of the minerals.     

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.     

Alpine medicinal plant trade and Himalayan mountain livelihood strategies

Commercial alpine medicinal plants are collected from the wild by local rural households throughout the Himalaya and sold in order to increase household incomes. Recent studies indicate that this annual trade amounts to thousands of tonnes of roots, rhizomes, tubers, leaves, etc., worth millions of US dollars. The main market is in India. Based on a national survey, including the most commonly traded species, and a village study this paper investigates the importance of the alpine medicinal plant trade at national and local levels in Nepal. The national survey included standardized open-ended interviews with 232 harvesters, 64 local traders, 66 central wholesalers, 47 regional wholesalers, and 16 production companies. The village survey is based on the daily records of household activities in 15 households in a one-year period. The annual Nepalese alpine and sub-alpine medicinal plant trade is conservatively estimated to vary from 480 to 2500 t with a total harvester value of US$0.8–3.3 million; the average harvester value is estimated at US$66.0 ± 99.0. The trade in 1997/98 amounted to 1600 t with a harvester value of US$2.3 million and an export value equivalent to 2.5% of total export from Nepal. Medicinal plant harvesting was found to constitute an integrated part of local livelihood strategies, contributing from 3 to 44% (average of 12%) of the annual household income. Importance at household level depended on land and animal holdings, and the availability of adult male labour. The validity and reliability of methods and analyses is evaluated, and issues of conservation and management of alpine medicinal plant species under the community forestry scheme are discussed.     

我国钴矿矿产资源及其成矿作用

钴广泛应用于军事、钢铁、电器、机械等方面，它具有亲铁亲硫的双重性，在各类岩浆岩中的分布不均匀，由超基性岩向酸性岩，钴的含量迅速降低。我国钻矿资源呈现富矿少、贫矿多的特点，并且铬矿主要分布在甘肃、青海、新疆、吉林、陕西、云南、四川等地。按照矿床形成的成矿环境、矿床岩石组合，矿床类型划分为：与镁铁质-超镁铁质岩有关的硫化物钻矿、与海相火山喷发有关的钻多金属矿、与斑岩有关的铜钻矿、与砂岩有关的钻矿、与中酸性侵入岩有关的矽卡岩铁钻矿、与风化作用有关的钴矿等六类。     

陷落柱的探查与综合治理实践

介绍了桃园煤矿井下所见陷落柱的出露特征。为指导巷道施工，消除水患威胁，采取了综合勘探方法查明陷落柱的范围、高度、导水性等情况，并阐述了注浆加固综合治理陷落柱的方法。     

洁居纳卓组:西藏措勤地区的上新统岩石地层单位

分布于措勤县城一带的一套地层在1：100万日喀则幅区调中校划属上新统乌郁群，由下部火山岩和上部沉积岩组成。在1：25万措勘县幅区调中，根据获得的同位素年龄值已将下部火山岩地层划归古新统的典中组，而上部的沉积地层中含圆笠虫等有孔虫化石应属下白垩统。在这一地区还新发现一套陆相碎屑地层，根据其岩性组合、ESR年龄值及区域地层对比，本文将其新命名为上新统洁居纳卓组。     

云南巍山—永平矿集区流体地质填图的理论与实践

流体地质填图是一种全新的地质调查及找矿方法。本次研究选择云南巍山－永平矿集区进行流体地质填图试验，研究了矿集区的控矿地质要素、各种主要的矿床（点）特征、成矿流体子系统及流域、流体的盐度与均一温度、流体的气液相成分、微量元素含量、稳定同位素特征及成矿流体的浓集中心。得到的流体地质图基本反映了本区成矿流体的性质与状态，反映了两个成矿流体子系统的基本特征，圈字了2个成矿流体子系统的流域范围及7个成矿流体浓集中心。这些浓集中心与已知矿化点分布、化探异常及有利的地质条件基本吻合，成为该区进一步找矿预测的重要依据之一。本项目的实践表明，流体地质填图不失为一种有效的找矿手段。