Crustal melting and recycling: geochronology and sources of Variscan syn-kinematic anatectic granitoids of the Tormes Dome (Central Iberian Zone). A U–Pb LA-ICP-MS study

International Journal of Earth Sciences - In this study, we report U–Pb Laser Ablation ICP-MS zircon and ID-TIMS monazite ages for peraluminous granitoid plutons...     

Exploratory Factor Analysis of Wireline Logs Using a Float-Encoded Genetic Algorithm

In the paper, a novel inversion approach is used for the solution of the problem of factor analysis. The float-encoded genetic algorithm as a global optimization method is implemented to extract factor variables using open-hole logging data. The suggested statistical workflow is used to give a reliable estimate for not only the factors but also the related petrophysical properties in hydrocarbon formations. In the first step, the factor loadings and scores are estimated by Jöreskog’s fast approximate method, which are gradually improved by the genetic algorithm. The forward problem is solved to calculate wireline logs directly from the factor scores. In each generation, the observed and calculated well logs are compared to update the factor population. During the genetic algorithm run, the average fitness of factor populations is maximized to give the best fit between the observed and theoretical data. By using the empirical relation between the first factor and formation shaliness, the shale volume is estimated along the borehole. Permeability as a derived quantity also correlates with the first factor, which allows its determination from an independent source. The estimation results agree well with those of independent deterministic modeling and core measurements. Case studies from Hungary and the USA demonstrate the feasibility of the global optimization based factor analysis, which provides a useful tool for improved reservoir characterization.     

Mapping fractional landscape soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow

An unsupervised machine-learning workflow is proposed for estimating fractional landscape soils and vegetation components from remotely sensed hyperspectral imagery. The workflow is applied to EO-1 Hyperion satellite imagery collected near Ibirací, Minas Gerais, Brazil. The proposed workflow includes subset feature selection, learning, and estimation algorithms. Network training with landscape feature class realizations provide a hypersurface from which to estimate mixtures of soil (e.g. 0.5 exceedance for pixels: 75% clay-rich Nitisols, 15% iron-rich Latosols, and 1% quartz-rich Arenosols) and vegetation (e.g. 0.5 exceedance for pixels: 4% Aspen-like trees, 7% Blackberry-like trees, 0% live grass, and 2% dead grass). The process correctly maps forests and iron-rich Latosols as being coincident with existing drainages, and correctly classifies the clay-rich Nitisols and grasses on the intervening hills. These classifications are independently corroborated visually (Google Earth) and quantitatively (random soil samples and crossplots of field spectra). Some mapping challenges are the underestimation of forest fractions and overestimation of soil fractions where steep valley shadows exist, and the under representation of classified grass in some dry areas of the Hyperion image. These preliminary results provide impetus for future hyperspectral studies involving airborne and satellite sensors with higher signal-to-noise and smaller footprints.     

Image-aided platform orientation determination with a GNSS/low-cost IMU system using robust-adaptive Kalman filter

The availability of miniaturized sensors with enhanced capabilities, new methods for image processing, and easy access to small and low-weight airborne platforms for data acquisition, including unmanned vehicles, opens new possibilities for geodetic navigation applications and developing new developments in sensor fusion. In this context, the development of efficient methods, based on low-cost sensors, to extract precise georeferenced information from digital cameras is of utmost interest. We present a method to improve the performance of the integration of GNSS/low-cost IMU by exploiting the orientation changes retrieved from digital images. In this work, a robust-adaptive Kalman filter is also introduced to further improve the performance of the method deployed. The adaptive factor and the robust factor accomplished are determined by innovation information and the threshold value of orientation changes between consecutive images. Results from airborne tests used to assess the performance of the method are presented. The results show that using a non-metric camera, the Euler angle estimation accuracy of the GNSS/low-cost IMU integration can be improved to be close to 0.5 degree and an additional improvement, which can reach 59%, can be achieved after using the robust-adaptive Kalman filter.     

3D-Monitoring Big Geo Data on a seaport infrastructure based on FIWARE

Many organizations of all kinds are using new technologies to assist the acquisition and analysis of data. Seaports are a good example of this trend. Seaports generate data regarding the management of marine traffic and other elements, as well as environmental conditions given by meteorological sensors and buoys. However, this enormous amount of data, also known as Big Data, is useless without a proper system to organize, analyze and visualize it. SmartPort is an online platform for the visualization and management of a seaport data that has been built as a GIS application. This work offers a Rich Internet Application that allows the user to visualize and manage the different sources of information produced in a port environment. The Big Data management is based on the FIWARE platform, as well as “The Internet of Things” solutions for the data acquisition. At the same time, Glob3 Mobile (G3M) framework has been used for the development of map requirements. In this way, SmartPort supports 3D visualization of the ports scenery and its data sources.     

‘Filament’ marker beds in the Karababa-A Member (Turonian) of the Arabian platform,SE Turkey: Palaeo-environmental significance,stratigraphy and correlation potential

A ‘filament’ event bed/level corresponds to the occurrence of thin elongate calcitic, shells, or plates of pelagic, bivalve or crinoidal, origin, within pelagic, laminated and organic-matter rich limestones. These organic-rich limestones had been generally deposited under hypoxic/anoxic conditions in an outer-ramp environment. These thin elongate, bivalve shells, or crinoidal plates, are found broken in small pieces, disintegrated, or simply preserved parallel to bedding as almost complete specimens. Such ‘filament’ beds were spotted in SE Turkey in deposits, namely the Karababa-A Member, around the Cenomanian-Turonian boundary (C/TB) and above. On a global scale, these filament event beds illustrate sea-level rise related to eustacy and/or climatic change. The abundance of filaments close to the C/TB is a biological marker of high organic productivity resulting from a climatic change to warmer conditions. The prevailing higher sea-level conditions were then favourable to the accumulation and preservation of organic-rich facies, characteristic of the Oceanic Anoxic Event 2 (OAE2). Using both biostratigraphic and carbon isotopic data, those deposits were dated from the latest Cenomanian-Turonian age. ‘Filament’ event beds were recorded in the same order, except for some small differences. A good number of these bio-events are global. Consequently, on a global scale, they are susceptible to constrain the C/TB, especially the base of the Turonian stage, with a significant degree of confidence.     

Use of Recycled Polyethylene Terephthalate Strips in Soil Improvement

Geotechnical and Geological Engineering - Many studies have investigated polyethylene terephthalate (PET) and polypropylene fibers in soil improvement. However, there is a lack of research using...     

Predicting seiche hazard for coastal harbours along the northern and western coasts of France

Natural Hazards - Long-period waves propagating inside harbours can lead to the generation of seiche that can affect and significantly disrupt port operations. This study is based on the analysis...