Relationships between texture and mechanical properties of hybrid rocks from the Jaala–Iitti complex, southeastern Finland

The relationships between petrographical and mechanical properties of rock aggregate raw materials from the hybridised, subvolcanic Jaala–Iitti complex, southeastern Finland, were investigated. Petrography was quantified from polished thin sections with a polarising microscope to determine the modal composition and grain size distribution, and resistance to fragmentation and abrasion were determined. Abundance of fine-grained minerals (especially of hornblende), fine grain size-dominated grain size distribution, uniform spatial dispersion of hornblende crystals, and intense micrographic intergrowth texture with interlocking grain boundaries were found to have the greatest positive influence on the mechanical properties. The results showed the potentiality of hybridised rocks as raw materials for high quality aggregates that can resist fragmentation and abrasion.     

Trace metal and As solid-phase speciation in sulphide mine tailings – Indicators of spatial distribution of sulphide oxidation in active tailings impoundments

Redistribution of potentially harmful metals and As was studied based on selective extractions in two active sulphide mine tailings impoundments in Finland. The Hitura tailings area contains residue from Ni ore processing, while the Luikonlahti site includes tailings from the processing of Cu–Co–Zn–Ni and talc ores. To characterize the element solid-phase speciation with respect to sulphide oxidation intensity and the water saturation level of the tailings, drill cores were collected from border zones and mid-impoundment locations. The mobility and solid-phase fractionation of Ni, Cu, Co, Zn, Cr, Fe, Ca, Al, As, and S were analysed using a 5-step non-sequential (parallel) selective extraction procedure. The results indicated that metal redistribution and sulphide oxidation intensity were largely controlled by the disposal history and strategy of the tailings (sorting, exposure of sulphides due to delayed burial), impoundment structure and water table, and reactivity of the tailings. Metal redistribution suggested sulphide weathering in the tailings surface, but also in unsaturated proximal areas beside the earthen dams, and in water-saturated bottom layers, where O₂-rich infiltration is possible. Sulphide oxidation released trace metals from sulphide minerals at both locations. In the Hitura tailings, with sufficient buffering capacity, pH remained neutral and the mobilized metals were retained by secondary Fe precipitates deeper in the oxidized zone. In contrast, sulphide oxidation-induced acidity and rise in the water table after oxidation apparently remobilized the previously retained metals in Luikonlahti. In general, continuous disposal of tailings decreased the sulphide oxidation intensity in active tailings, unless there was a delay in burial and the reactive tailings were unsaturated after deposition.     

Mineralogical and geochemical alteration of Hitura sulphide mine tailings with emphasis on nickel mobility and retention

Weathering of Hitura (W Finland) nickel sulphide mine tailings and release of heavy metals into pore water was studied with mineralogical (optical and electron microscopy, X-ray diffraction) and geochemical methods (selective extractions). Tailings were composed largely of serpentine, micas and amphiboles with only minor carbonates and sulphides. Sulphides, especially pyrrhotite, have oxidized intensively in the shallow tailings in 10–15 years, but a majority of the tailings have remained unchanged. Oxidation has resulted in depletion of carbonates, slightly decreased pH, and heavy metal (Ni, Zn) release in pore water as well as in the precipitation of secondary Fe precipitates. Nevertheless, in the middle of the tailings area, where the oxidation front moves primarily downward, released heavy metals have been adsorbed and immobilized with these precipitates deeper in the oxidation zone. In contrast to what was seen in pore water pH, but in accordance with static tests of the previous studies, the neutralisation potential ratio (NPR) calculated based on the mineralogical composition and the total sulphur content suggested that tailings are ‘not potentially acid mine drainage (AMD) generating’. However, the calculated buffering capacity of the tailings resulted largely from the abundant serpentine because of the low carbonate content. Despite its slow weathering rate, serpentine may buffer the acidity to some extent through ion exchange processes in fine ground tailings. Nevertheless, in practice, acid production capacity of the tailings depends primarily on the balance between Ca–Mg carbonates and iron sulphides. NPR calculation based on carbonate and sulphur contents suggested, that the Hitura tailings are ‘likely AMD generating’. The study shows that sulphide oxidation can be significant in mobilisation of heavy metals even in apparently non-acid producing, low sulphide tailings. Therefore, prevention of oxygen diffusion into tailings is also essential in this type of sulphide tailings.     

Using Statistical Downscaling to Quantify the GCM-Related Uncertainty in Regional Climate Change Scenarios： A Case Study of Swedish Precipitation

There are a number of sources of uncertainty in regional climate change scenarios. When statistical downscaling is used to obtain regional climate change scenarios, the uncertainty may originate from the uncertainties in the global climate models used, the skill of the statistical model, and the forcing scenarios applied to the global climate model. The uncertainty associated with global climate models can be evaluated by examining the differences in the predictors and in the downscaled climate change scenarios based on a set of different global climate models. When standardized global climate model simulations such as the second phase of the Coupled Model Intercomparison Project （CMIP2） are used, the difference in the downscaled variables mainly reflects differences in the climate models and the natural variability in the simulated climates. It is proposed that the spread of the estimates can be taken as a measure of the uncertainty associated with global climate models. The proposed method is applied to the estimation of global-climate-model-related uncertainty in regional precipitation change scenarios in Sweden. Results from statistical downscaling based on 17 global climate models show that there is an overall increase in annual precipitation all over Sweden although a considerable spread of the changes in the precipitation exists. The general increase can be attributed to the increased large-scale precipitation and the enhanced westerly wind. The estimated uncertainty is nearly independent of region. However, there is a seasonal dependence. The estimates for winter show the highest level of confidence, while the estimates for summer show the least.     

Boosting for Mineral Prospectivity Modeling: A New GIS Toolbox

With an increasing demand for raw materials, predictive models that support successful mineral exploration targeting are of great importance. We evaluated different machine learning techniques with an emphasis on boosting algorithms and implemented them in an ArcGIS toolbox. Performance was tested on an exploration dataset from the Iberian Pyrite Belt (IPB) with respect to accuracy, performance, stability, and robustness. Boosting algorithms are ensemble methods used in supervised learning for regression and classification. They combine weak classifiers, i.e., classifiers that perform slightly better than random guessing to obtain robust classifiers. Each time a weak learner is added; the learning set is reweighted to give more importance to misclassified samples. Our test area, the IPB, is one of the oldest mining districts in the world and hosts giant volcanic-hosted massive sulfide (VMS) deposits. The spatial density of ore deposits, as well as the size and tonnage, makes the area unique, and due to the high data availability and number of known deposits, well-suited for testing machine learning algorithms. We combined several geophysical datasets, as well as layers derived from geological maps as predictors of the presence or absence of VMS deposits. Boosting algorithms such as BrownBoost and Adaboost were tested and compared to Logistic Regression (LR), Random Forests (RF) and Support Vector machines (SVM) in several experiments. We found performance results relatively similar, especially to BrownBoost, which slightly outperformed LR and SVM with respective accuracies of 0.96 compared to 0.89 and 0.93. Data augmentation by perturbing deposit location led to a 7% improvement in results. Variations in the split ratio of training and test data led to a reduction in the accuracy of the prediction result with relative stability occurring at a critical point at around 26 training samples out of 130 total samples. When lower numbers of training data were introduced accuracy dropped significantly. In comparison with other machine learning methods, Adaboost is user-friendly due to relatively short training and prediction times, the low likelihood of overfitting and the reduced number of hyperparameters for optimization. Boosting algorithms gave high predictive accuracies, making them a potential data-driven alternative for regional scale and/or brownfields mineral exploration.

     

Spatial Analysis Techniques as Successful Mineral-Potential Mapping Tools for Orogenic Gold Deposits in the Northern Fennoscandian Shield,Finland

Geoscientific Information Systems (GIS) provide tools to quantitatively analyze and integrate spatially referenced information from geological, geophysical, and geochemical surveys for decision-making processes. Excellent coverage of well-documented, precise and good quality data enables testing of variable exploration models in an efficient and cost effective way with GIS tools. Digital geoscientific data from the Geological Survey of Finland (GTK) are being used widely as spatial evidence in exploration targeting, that is ranking areas based on their exploration importance. In the last few years, spatial analysis techniques including weights-of-evidence, logistic regression, and fuzzy logic, have been increasingly used in GTK’s mineral exploration and geological mapping projects. Special emphasis has been put into the exploration for gold because of the excellent data coverage within the prospective volcanic belts and because of the increased activity in gold exploration in Finland during recent years. In this paper, we describe some successful case histories of using the weights-of-evidence method for the Au-potential mapping. These projects have shown that, by using spatial modeling techniques, exploration targets can be generated by quantitatively analyzing extensive amounts of data from various sources and to rank these target areas based on their exploration potential.     

Mapping the mesospheric CO2 clouds on Mars: MEx/OMEGA and MEx/HRSC observations and challenges for atmospheric models

This study presents the latest results on the mesospheric CO₂ clouds in the martian atmosphere based on observations by OMEGA and HRSC onboard Mars Express. We have mapped the mesospheric CO₂ clouds during nearly three martian years of OMEGA data yielding a cloud dataset of ∼60 occurrences. The global mapping shows that the equatorial clouds are mainly observed in a distinct longitudinal corridor, at seasons L_s = 0-60° and again at and after L_s = 90°. A recent observation shows that the equatorial CO₂ cloud season may start as early as at L_s = 330°. Three cases of mesospheric midlatitude autumn clouds have been observed. Two cloud shadow observations enabled the mapping of the cloud optical depth (τ = 0.01-0.6 with median values of 0.13-0.2 at λ = 1 μm) and the effective radii (mainly 1-3 μm with median values of 2.0-2.3 μm) of the cloud crystals. The HRSC dataset of 28 high-altitude cloud observations shows that the observed clouds reside mainly in the altitude range ∼60-85 km and their east-west speeds range from 15 to 107 m/s. Two clouds at southern midlatitudes were observed at an altitude range of 53-62 km. The speed of one of these southern midlatitude clouds was measured, and it exhibited west-east oriented speeds between 5 and 42 m/s. The seasonal and geographical distribution as well as the observed altitudes are mostly in line with previous work. The LMD Mars Global Climate Model shows that at the cloud altitude range (65-85 km) the temperatures exhibit significant daily variability (caused by the thermal tides) with the coldest temperatures towards the end of the afternoon. The GCM predicts the coldest temperatures of this altitude range and the season L_s = 0-30° in the longitudinal corridor where most of the cloud observations have been made. However, the model does not predict supersaturation, but the GCM-predicted winds are in fair agreement with the HRSC-measured cloud speeds. The clouds exhibit variable morphologies, but mainly cirrus-type, filamented clouds are observed (nearly all HRSC observations and most of OMEGA observations). In ∼15% of OMEGA observations, clumpy, round cloud structures are observed, but very few clouds in the HRSC dataset show similar morphology. These observations of clumpy, cumuliform-type clouds raise questions on the possibility of mesospheric convection on Mars, and we discuss this hypothesis based on Convective Available Potential Energy calculations.     

Final analysis of ELAIS 15-μm observations: method, reduction and catalogue

We present the final analysis of the European Large Area Infrared Space Observatory ( ISO ) Survey (ELAIS) 15-μm observations, carried out with the ISO Camera (ISOCAM) instrument on board the ISO .
The data-reduction method, known as the Lari Method, is based on a mathematical model of the behaviour of the detector and was specifically designed for the detection of faint sources in ISOCAM/ISO Photopolarimeter (ISOPHOT) data. The method is fully interactive and leads to very reliable and complete source lists.
The resulting catalogue includes 1923 sources detected with signal-to-noise ratio of  > 5  in the 0.5–100 mJy flux range and over an area of 10.85 deg² split into four fields, making it the largest non-serendipitous extragalactic source catalogue obtained to date from the ISO data.
This paper presents the concepts underlying the data-reduction method together with its latest enhancements. The data-reduction process, the production and basic properties of the resulting catalogue are discussed. The catalogue quality is assessed by means of detailed simulations, optical identifications and comparison with previous analyses.