Geostatistical estimation of chemical contamination in stream sediments: The case study of Vale das Gatas mine (northern Portugal)

Based on an environmental geochemistry case study carried out in the neighbourhood of a W–Sn abandoned mine, the pollution in stream sediments was modelled through a Global Contamination Index. Such an index permits one to summarize the combination of deleterious elements in a single variable, obtained by the projection of samples onto the first axis of a PCASD (Principal Components Analysis of Standardized Data) applied to the entire n × p matrix containing the available concentrations of p = 16 elements in the set of n = 220 collected samples.In order to provide a sound basis for a coherent planning of the remediation process which will be put in operation in the affected area, it is necessary to balance the costs of reclaiming with the probabilities of exceeding the upper limits accepted for concentrations of environmentally harmful elements in sediments. Given these limits, they are back-transformed in the index values, providing a practical threshold between ‘clean’ and ‘contaminated’ samples. On the other hand, the minimum dimension of the cell to be reclaimed is restrained by the selected remediation process to be applied in the affected area. Hence, to meet the constraints of such a remediation process, it is required to estimate the probabilities of exceeding the index threshold in technologically meaningful sub-areas. For this end, the Indicator Block Kriging technique was applied, producing a series of maps where sub-areas to be reclaimed can be spotted for different probability levels. These maps, on which the decision making remediation agency can rely for its cost-benefit analysis, take into account both the spatial structure of ‘clean’ vs. ‘contaminated’ samples and the constraints of the reclaiming process.     

Spatial Variability of Rock Depth in Bangalore Using Geostatistical, Neural Network and Support Vector Machine Models

Geospatial technology is increasing in demand for many applications in geosciences. Spatial variability of the bed/hard rock is vital for many applications in geotechnical and earthquake engineering problems such as design of deep foundations, site amplification, ground response studies, liquefaction, microzonation etc. In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 km². In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, Geostatistical model based on Ordinary Kriging technique, Artificial Neural Network (ANN) and Support Vector Machine (SVM) models have been developed. In Ordinary Kriging, the knowledge of the semi-variogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of the Bangalore, where field measurements are not available. A new type of cross-validation analysis developed proves the robustness of the Ordinary Kriging model. ANN model based on multi layer perceptrons (MLPs) that are trained with Levenberg–Marquardt backpropagation algorithm has been adopted to train the model with 90% of the data available. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing loss function has been used to predict the reduced level of rock from a large set of data. In this study, a comparative study of three numerical models to predict reduced level of rock has been presented and discussed.     

Triple diagram method for the prediction of wave height and period

Many formulations have been developed so far to predict the wave height and period from fetch length and wind blowing duration for a constant wind speed. This study aimed to predict wave parameters from fetch length and meteorological factors by using triple diagram methodology based on Kriging principles. Proposed model results were compared with Joint North Sea Wave Project (JONSWAP) model which is used so commonly in the ocean and coastal engineering studies. For the implementation of the methodology hourly wave and wind data were obtained from a buoy located in Lake Ontario. Numerical and graphical comparisons demonstrated that the proposed method outperforms the classical formulation.     

实验变差函数计算方法的研究与运用

本文系统、详尽地分析讨论了不同样品数据条件下实验变差函数的计算方法。在实验变差函数计算流程的设计和有效样品搜索域的确定方面提出了自己独特的见解。最后用一个矿山的控矿工程教据对文中介绍的方法进行了测试。结果表明,利用本文的方法,容易得到稳健、准确、信息量高的实验变差函数。     

Optimization of the Sampling Technique for the Determination of Dissolved Hydrogen in Groundwater

In this study a field‐sampling technique for dissolved hydrogen (H₂) in groundwater will be presented which allows the transport of gaseous samples into the laboratory for further analysis. The method consists of transferring the headspace trapped in a gas‐sampling bulb which is continuously purged by groundwater into previously evacuated vials using a gas‐tight syringe. Three transfer steps with preceding evacuation of the vial led to a H₂‐recovery of 100 % in laboratory experiments. The method has been applied to determine H₂ concentrations in an aquifer contaminated with chlorinated solvents. Tests concerning the effect of different pumping techniques on H₂ concentrations revealed that most reliable values were obtained with a bladder pump, while an electrically driven submersible pump generated considerable amounts of hydrogen due to electrochemical interactions with the sampled water. Concentrations of dissolved hydrogen in field and laboratory samples were about two orders of magnitude higher when sampling was performed with the electrically driven submersible pump compared to sampling with the bladder pump and a peristaltic pump. Lab experiments with a Plexiglas reservoir to produce H₂‐enriched water were used to study the effect of two tubing materials (PVC, polyamide) on H₂ losses. PVC tubing turned out to allow transfer of H₂‐enriched water over 25 m without significant losses, while PA‐tubing was not suitable for sampling of H₂.     

CT技术用于激光等离子体成象研究

本文研究适用于ＩＣＦ实验的ＣＴ技术，并已用于“星光Ⅱ”装置的激光等离子体的实验Ｘ光测量，重建出Ｘ光的三维分布，获得了有意义的结果，表明该技术在ＩＣＦ实验中的应用的可能性。     

线性地质统计学在地质工作计算机化中的作用及意义

线性地质统计学的发展与地质工作逐渐由定性向定量、由人工劳动逐渐被计算机取代的过程密切相关的。对样品品位赋予一定的权值进行滑动加权平均，来估计中心块段平均品位的方法，使地质问题的计算能在计算机上实现开辟了新的途径。     

正态化克立格法在路腊汞矿储量计算中的应用

正态化克立格法是对传统储量计算方法的改革和创新。其计算过程的科学性和计算结果的正确性在路腊汞矿作了探采对比试算，取得了良好的结果。     

四川盆地碳酸盐岩油气勘探实践与认识

四川盆地烃源十分丰富。但油气演化程度高,大多已进入高成熟和过成熟阶段,至今只在侏罗系发现有油藏,三叠系及其更老地层皆为气藏。储层主要为碳酸盐岩,其特点是低孔低渗,平均孔隙度仅1.7%,平均渗透率小于1×10~(-3)μm~2,但其中的“孔洞发育层段”平均孔隙度可达3%～6%。根据四十年的勘探经验,加强预探并争取新发现是四川盆地油气勘探发展的关键。预探中,按构造带规划大中型气田勘探目标,将成藏条件相似、地域上相邻的局部圈闭群作为一个整体勘探对象,实行稀井广探,少井高效。“八五”以来,在高陡构造带勘探技术、储层横向预探技术等方面有长足的进步,勘探成效不断提高,实现了资源序列的良性循环。     

Space-time mapping of soil salinity using probabilistic bayesian maximum entropy

The mapping of saline soils is the first task before any reclamation effort. Reclamation is based on the knowledge of soil salinity in space and how it evolves with time. Soil salinity is traditionally determined by soil sampling and laboratory analysis. Recently, it became possible to complement these hard data with soft secondary data made available using field sensors like electrode probes. In this study, we had two data sets. The first includes measurements of field salinity (EC_a) at 413 locations and 19 time instants. The second, which is a subset of the first (13 to 20 locations), contains, in addition to EC_a, salinity determined in the laboratory (EC_2.5). Based on a procedure of cross-validation, we compared the prediction performance in the space-time domain of 3 methods: kriging using either only hard data (HK) or hard and mid interval soft data (HMIK), and Bayesian maximum entropy (BME) using probabilistic soft data. We found that BME was less biased, more accurate and giving estimates, which were better correlated with the observed values than the two kriging techniques. In addition, BME allowed one to delineate with better detail saline from non-saline areas.