A multi-tracer approach to delineate groundwater dynamics in the Rio Actopan Basin,Veracruz State,Mexico

Geochemistry and environmental tracers were used to understand groundwater resources, recharge processes, and potential sources of contamination in the Rio Actopan Basin, Veracruz State, Mexico. Total dissolved solids are lower in wells and springs located in the basin uplands compared with those closer to the coast, likely associated with rock/water interaction. Geochemical results also indicate some saltwater intrusion near the coast and increased nitrate near urban centers. Stable isotopes show that precipitation is the source of recharge to the groundwater system. Interestingly, some high-elevation springs are more isotopically enriched than average annual precipitation at higher elevations, indicating preferential recharge during the drier but cooler winter months when evapotranspiration is reduced. In contrast, groundwater below 1,200 m elevation is more isotopically depleted than average precipitation, indicating recharge occurring at much higher elevation than the sampling site. Relatively cool recharge temperatures, derived from noble gas measurements at four sites (11–20 °C), also suggest higher elevation recharge. Environmental tracers indicate that groundwater residence time in the basin ranges from 12,000 years to modern. While this large range shows varying groundwater flowpaths and travel times, ages using different tracer methods (¹⁴C, ³H/³He, CFCs) were generally consistent. Comparing multiple tracers such as CFC-12 with CFC-113 indicates piston-flow to some discharge points, yet binary mixing of young and older groundwater at other points. In summary, groundwater within the Rio Actopan Basin watershed is relatively young (Holocene) and the majority of recharge occurs in the basin uplands and moves towards the coast.     

Influence of Mussel Culture on the Vertical Export of Phytoplankton Carbon in a Coastal Upwelling Embayment (Ría de Vigo,NW Iberia)

The goal of this paper is to find out whether suspended mussel culture affects the vertical fluxes of biogenic particles in the Ría de Vigo on a seasonal scale. With this aim, vertical fluxes of particulate organic carbon (POC) and the magnitude and composition of vertical export of phytoplankton carbon (Cphyto) collected in sediment traps were examined by comparing data obtained inside a mussel farming area (RaS) with those found at a reference station (ReS) not affected by mussels. Our results indicate that mussel farming has a strong impact on sedimentation fluxes under the rafts, not only increasing POC flux but also altering the magnitude and composition of Cphyto fluxes. Average POC flux at RaS (2564?±?1936 mg m^?2 day^?1) was four times higher than at ReS (731?±?276 mg m^?2 day^?1), and much of this increase was due to biodeposit fluxes (Cbiodep) which accounted for large proportion of POC flux (35–60 %). Indeed, because of this high Cbiodep flux, only a small proportion of the POC flux was due to Cphyto flux (3–12 %). At the same time, we observed an increased sedimentation of phytoplankton cells at RaS that could be explained by a combination of mechanisms: less energetic hydrodynamic conditions under mussel rafts, ballast effect by sinking mussel feces, and diatom aggregates. Moreover, mussel farming also altered the quality of the Cphyto flux by removing part of the predatory pressure of zooplankton and thus matching diatom composition in water column and sediment traps.     

Geochemistry and geochronology of Late Jurassic and Early Cretaceous intrusions related to some Au (Sb) deposits in southern Anhui: a case study and review

Some Au deposits in southern Anhui Province have recently been found to be closely associated with Late Mesozoic intrusions. Typical examples include the Huashan Au (Sb) deposit and Au deposits at Zhaojialing, Wuxi, and Liaojia. In order to understand the mechanisms that led the formation of these Au deposits, we make detailed reviews on the geological characteristics of these Au deposits. Specifically, we present new LA-ICP-MS zircon U–Pb dating, along with elemental and Hf isotopic data from the Huashan Au (Sb) deposit. Our data suggests that the Huashan ore-related intrusions were emplaced during the Late Jurassic and Early Cretaceous periods (144–148 Ma). They are characterized by arc-magma features and high oxygen fugacity and are rich in inherited zircons. Zircon U–Pb ages and Lu–Hf isotopes from intrusions suggest that Proterozoic juvenile lithosphere is the main source of these intrusions. The regional geological history implies that lithosphere beneath southern Anhui was produced during a Proterozoic subduction and was fertilized with Au (Cu) in the process. Integrated with the results of previous studies, we inferred that Late Mesozoic intrusions formed by the remelting of the lithosphere could provide the metal endowment for the Au-rich deposits in southern Anhui.     

Identifying influence areas with connectivity analysis – application to the local perturbation of heterogeneity distribution for history matching

Numerical representations of a target reservoir can help to assess the potential of different development plans. To be as predictive as possible, these representations or models must reproduce the data (static, dynamic) collected on the field. However, constraining reservoir models to dynamic data – the history-matching process – can be very time consuming. Many uncertain parameters need to be taken into account, such as the spatial distribution of petrophysical properties. This distribution is mostly unknown and usually represented by millions of values populating the reservoir grid. Dedicated parameterization techniques make it possible to investigate many spatial distributions from a small number of parameters. The efficiency of the matching process can be improved from the perturbation of specific regions of the reservoir. Distinct approaches can be considered to define such regions. For instance, one can refer to streamlines. The leading idea is to identify areas that influence the production behavior where the data are poorly reproduced. Here, we propose alternative methods based on connectivity analysis to easily provide approximate influence areas for any fluid-flow simulation. The reservoir is viewed as a set of nodes connected by weighted links that characterize the distance between two nodes. The path between nodes (or grid blocks) with the lowest cumulative weight yields an approximate flow path used to define influence areas. The potential of the approach is demonstrated on the basis of 2D synthetic cases for the joint integration of production and 4D saturation data, considering several formulations for the weights attributed to the links.     

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...     

Rainfall thresholds for the triggering of landslides considering previous soil moisture conditions (Asturias,NW Spain)

Given its geological and climatic conditions and its rugged orography, Asturias is one of the most landslide prone areas in the North of Spain. Most of the landslides occur during intense rainfall episodes. Thus, precipitation is considered the main triggering factor in the study area, reaching average annual values of 960 mm. Two main precipitation patterns are frequent: (i) long-lasting periods of moderate rainfall during autumn and winter and (ii) heavy short rainfall episodes during spring and early summer. In the present work, soil moisture conditions in the locations of 84 landslides are analysed during two rainfall episodes, which represent the most common precipitation patterns: October–November 2008 and June 2010. Empirical data allowed the definition of available water capacity percentages of 99–100% as critical soil moisture conditions for the landslide triggering. Intensity-duration rainfall thresholds were calculated for each episode, considering the periods with sustained high soil moisture levels before the occurrence of each analysed landslide event. For this purpose, data from daily water balance models and weather stations were used. An inverse relationship between the duration of the precipitation and its intensity, consistent with published intensity-duration thresholds, was observed, showing relevant seasonal differences.     

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.