The timing of salt structure growth in the Southern Permian Basin (Central Europe) and implications for basin dynamics

In this paper, a literature‐based compilation of the timing and history of salt tectonics in the Southern Permian Basin (Central Europe) is presented. The tectono‐stratigraphic evolution of the Southern Permian Basin is influenced by salt movement and the structural development of various types of salt structures. The compilation presented here was used to characterize the following syndepositional growth stages of the salt structures: (a) “phase of initiation”; (b) phase of fastest growth (“main activity”); and (c) phase of burial’. We have also mapped the spatial pattern of potential mechanisms that triggered the initiation of salt structures over the area studied and summarized them for distinct regions (sub‐basins, platforms, etc.). The data base compiled and the set of maps produced from it provide a detailed overview of the spatial and temporal distribution of salt tectonic activity enabling the correlation of tectonic phases between specific regions of the entire Southern Permian Basin. Accordingly, salt movements were initiated in deeply subsided graben structures and fault zones during the Early and Middle Triassic. In these areas, salt structures reached their phase of main activity already during the Late Triassic or the Jurassic and were mostly buried during the Early Cretaceous. Salt structures in less subsided sub‐basins and platform regions of the Southern Permian Basin mostly started to grow during the Late Triassic. The subsequent phase of main activity of these salt structures took place from the Late Cretaceous to the Cenozoic. The analysis of the trigger mechanisms revealed that most salt structures were initiated by large‐offset normal faults in the sub‐salt basement in the large graben structures and minor normal faulting associated with thin‐skinned extension in the less subsided basin parts.     

Trace-element composition and zoning in clinopyroxene- and amphibole-group minerals: Implications for element partitioning and evolution of carbonatites

The present work is a first comprehensive study of the trace-element composition and zoning in clinopyroxene- and amphibole-group minerals from carbonatites, incorporating samples from 14 localities worldwide (Afrikanda, Aley, Alnö, Blue River, Eden Lake, Huayangchuan, Murun, Oka, Ozernaya Varaka, Ozernyi, Paint Lake, Pinghe, Prairie Lake, Turiy Mys). The new electron-microprobe data presented here significantly extend the known compositional range of clinopyroxenes and amphiboles from carbonatites. These data confirm that calcic and sodic clinopyroxenes from carbonatites are not separated by a compositional gap, instead forming an arcuate trend from nearly pure diopside through intermediate aegirine–augite compositions confined to a limited range of CaFeSi₂O₆ contents (15–45 mol%) to aegirine with < 25 mol% of CaMgSi₂O₆ and a negligible proportion of CaFeSi₂O₆. A large set of LA-ICPMS data shows that the clinopyroxenes of different composition are characterized by relatively low levels of Cr, Co and Ni (≤ 40 ppm) and manifold variations in the concentration of trivalent lithophile and some incompatible elements (1–150 ppm Sc, 26–6870 ppm V, 5–550 ppm Sr, 90–2360 ppm Zr, and nil to 150 ppm REE), recorded in some cases within a single crystal. The relative contribution of clinopyroxenes to the whole-rock Rb, Nb, Ta, Th and U budget is negligible. The major-element compositional range of amphiboles spans from alkali- and Al-poor members (tremolite) to Na–Al-rich Mg- or, less commonly, Fe-dominant members (magnesiohastingsite, hastingsite and pargasite), to calcic–sodic, sodic and potassic–sodic compositions intermediate between magnesio-ferrikatophorite, richterite, magnesioriebeckite, ferri-nyböite and (potassic-)magnesio-arfvedsonite. In comparison with the clinopyroxenes, the amphiboles contain similar levels of tetravalent high-field-strength elements (Ti, Zr and Hf) and compatible transition elements (Cr, Co and Ni), but are capable of incorporating much higher concentrations of Sc and incompatible elements (up to 500 ppm Sc, 43 ppm Rb, 1470 ppm Sr, 1230 ppm Ba, 80 ppm Pb, 1070 ppm REE, 140 ppm Y, and 180 ppm Nb). In some carbonatites, amphiboles contribute as much as 25% of the Zr + Hf, 15% of the Sr and 35% of the Rb + Ba whole-rock budget. Both clinopyroxenes and amphiboles may also host a significant share (~ 10%) of the bulk heavy-REE content. Our trace-element data show that the partitioning of REE between clinopyroxene (and, in some samples, amphibole) and the melt is clearly bimodal and requires a revision of the existing models assuming single-site REE partitioning. Clinopyroxenes and amphiboles from carbonatites exhibit a diversity of zoning patterns that cannot be explained exclusively on the basis of crystal chemistry and relative compatibility of different trace-element in these minerals. Paragenetic analysis indicates that in most cases, the observed zoning patterns develop in response to removal of selected trace elements by phases co-precipitating with clinopyroxene and amphibole (especially magnetite, fluorapatite, phlogopite and pyrochlore). With the exception of magnesiohastingsite–richterite sample from Afrikanda, the invariability of trace-element ratios in the majority of zoned clinopyroxene and amphibole crystals implies that fluids are not involved in the development of zoning in these minerals. The implications of the new trace-element data for mineral exploration targeting REE, Nb and other types of carbonatite-hosted rare-metal mineralization are discussed.     

A mass ratio limit for primordial black holes

We augment our scenario for the formation of astronomical objects from macroscopic superstrings by the assumption that the central matter keeps its identity in the fragmentation. From the condition that the angular momentum per mass squared of this matter should be less than the Kerr limit G/c, we obtain upper limits for the ratio of the mass of central black holes M(BH) to the mass M of the host object. This limit is M(BH)/M ≈ 0.001, and, expressed in observed quantities, approximately M(BH)/M ≈ σ²/(v · c) where σ is the r.m.s. velocity, v the rotational velocity and c the velocity of light. The valuesM(BH) agree with the observed behaviour both in order of magnitude and in the variation with velocity dispersion. (© 2003 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Multi‐Scale Qualitative Location: A Topology‐Based Model

Qualitative locations describe spatial objects by relating the spatial objects to a frame of reference (e.g. a regional partition in this study) with qualitative relations. Existing models only formalize spatial objects, frames of reference, and their relations at one scale, thus limiting their applicability in representing location changes of spatial objects across scales. A topology‐based, multi‐scale qualitative location model is proposed to represent the associations of multiple representations of the same objects with respect to the frames of reference at different levels. Multi‐scale regional partitions are first presented to be the frames of reference at multiple levels of scale. Multi‐scale locations are then formalized to relate multiple representations of the same objects to the multiple frames of reference by topological relations. Since spatial objects, frames of reference, and topological relations in qualitative locations are scale dependent, scale transformation approaches are presented to derive possible coarse locations from detailed locations by incorporating polygon merging, polygon‐to‐line and polygon‐to‐point operators.     

Context inference and prediction modeling in ubiquitous health GIS

The ever‐increasing population in cities intensifies environmental pollution that increases the number of asthmatic patients. Other factors that may influence the prevalence of asthma are atmospheric parameters, physiographic elements and personal characteristics. These parameters can be incorporated into a model to monitor and predict the health conditions of asthmatic patients in various contexts. Such a model is the base for any asthma early warning system. This article introduces a novel ubiquitous health system to monitor asthmatic patients. Ubiquitous systems can be effective in monitoring asthmatic patients through the use of intelligent frameworks. They can provide powerful reasoning and prediction engines for analyzing various situations. Our proposed model encapsulates several tools for preprocessing, reasoning and prediction of asthma conditions. In the preprocessing phase, outliers in the atmospheric datasets were detected and missing sensor data were estimated using a Kalman filter, while in the reasoning phase, the required information was inferred from the raw data using some rule‐based inference techniques. The asthmatic conditions of patients were predicted accurately by a Graph‐Based Support Vector Machine in a Context Space (GBSVMCS) which functions anywhere, anytime and with any status. GBSVMCS is an improved version of the common Support Vector Machine algorithm with the addition of unlabeled data and graph‐based rules in a context space. Based on the stored value for a patient's condition and his/her location/time, asthmatic patients can be monitored and appropriate alerts will be given. Our proposed model was assessed in Region 3 of Tehran, Iran for monitoring three different types of asthma: allergic, occupational and seasonal asthma. The input data to our system included air pollution data, the patients’ personal information, patients’ locations, weather data and geographical information for 270 different situations. Our results showed that 90% of the system's predictions were correct. The proposed model also improved the estimation accuracy by 15% in comparison to conventional methods.     

Geographic information science and the analysis of place and health

The representation of place is a key theoretical advancement that geographic information science can offer to improve the understanding of environmental determinants of health, but developing robust computational representations of place requires a substantial departure from conventional notions of geographic representation in GIS. Unlike conventional GIS representations based on either objects or locations, we suggest that place representation should incorporate dynamic subjective, experiential, and relational aspects of place, as the influence of place on health behavior concerns not only the features that can be objectively observed at a particular location but also the environmental perceptions of the individual, as molded by biological, social, and experiential characteristics. In addition, assessments of environmental exposures on health outcomes should focus on individuals' time–activity patterns and microenvironment profiles, which form a potentially unique personalized exposure environment for each individual. Addressing these representational challenges via collaborative research has the potential to advance both geographic information science and health research.     

Modeling Urban Land Use Changes Using Support Vector Machines

Support Vector Machines (SVM) is a machine learning (ML) algorithm commonly applied to the classification of remotely sensing data and more recently for modeling land use changes. However, in most geospatial applications the current literature does not elaborate on specifications of the SVM method with respect to data sampling, attribute selection and optimal parameters choices. Therefore the main objective of this study is to present and investigate the SVM technique for modeling urban land use change. The SVM model building procedure is presented together with the detailed evaluation of the output results with respect to the choice of datasets, attributes and the change of SVM parameters. Geospatial datasets containing nine land use classes and spatial attributes for the Municipality of Zemun, Republic of Serbia were used for years 2001, 2003, 2007 and 2011. The Correlation‐based Feature Subset method, kappa coefficient, Area Under Receiver Operating Characteristic Curve (AUC) and kappa simulation were used to perform the model evaluation and compare the model outputs with the real land use datasets. The obtained results indicate that the SVM‐based models perform better when implementing balanced data sampling, reduced data sets to informative subsets of attributes and properly identify the optimal learning parameters.