Simulating train moving loads in physical model testing of railway infrastructure and its numerical calibration

Ballastless high-speed railways have dynamic performances that are quite different from those of conventional ballasted railways. The essential dynamic characteristics of high-speed railways due to passing train wheels, such as the cyclic effect, moving effect, and speed effect, were put forward and discussed. A full-scale accelerated railway testing platform for ballastless high-speed railways was proposed in this study. The feasibility of the sequential loading method in simulating train moving loads, and the boundary effect of the proposed physical model of ballastless railways, was investigated using three-dimensional finite element models. A full-scale physical model, 5 m long, 15 m wide, and 6 m high, was then established according to practical engineering design methods. Using a sequential loading system composed of eight high-performance hydraulic actuators, loads of a moving train with highest speed of 360 km/h were simulated. Preliminary experimental results of vibration velocities were presented and compared with field measurements of the Wuguang high-speed railway in China. Results showed that the experimental results coincided with the field measurements, demonstrating that the full-scale accelerated railway testing platform can simulate the process of a moving train and realistically reproduce the dynamic behaviors of ballastless high-speed railways.     

Monitoring and analysis of changes in a wetland landscape in Xingzi county

Wetlands play an important role in water conservation, environmental protection, and biodiversity conservation. Remote sensing is an economical and efficient technique for wetland monitoring which can limit disturbance in sensitive areas and support wetland conservation. In this paper, we used three phases of Thematic Mapper/Enhanced Thematic Mapper plus (TM/ETM+) remote sensing images from October 1989, October 1999, and October 2009 to study wetlands in Xingzi County. The images were segmented using the object-oriented remote sensing image interpretation software eCognition Developer 8.64, then segmented images were classified by slope, digital elevation model (DEM) data, Normalized Difference Vegetation Index (NDVI), Specific Leaf Area Vegetation Index (SLAVI), and Land and Water Masks (LWM) index to produce land type classification maps. Land use change information was obtained by analyzing the superposition of two classification maps of the wetland area from different years. The results showed that landscape patches in Xingzi County displayed fragmentation in their spatial distribution over time. Based on an index of changes in landscape patches, the fastest growing landscape type is grassland, while the fastest decreasing type is irrigated land. Dominant driving factors of changes in Xingzi County’s wetland landscape are population growth and policy changes.     

Reflectance spectroscopy and remote sensing data for finding sulfide-bearing alteration zones and mapping geology in Gilgit-Baltistan,Pakistan

Gilgit-Baltistan region is covering the northern most part of Pakistan where the rocks of the Kohistan-Ladakh island arc and Karakoram plate are exposed. The area has greater potential for precious and base metals deposits which are needed to be explored through spectroscopy and remote sensing techniques. Minerals and rocks can nowadays be identified through the measurement of their absorption and reflectance features by spectroscopic analysis. Spectral reflectance analysis is also very important in selecting the appropriate spectral bands for remote-sensing data analysis of unknown or inaccessible areas. In this study, reflectance spectra in the spectral range of 0.35–2.5 μm of different types of unaltered and altered rocks found in the Machulu and Astor areas of northern Pakistan were obtained using an ASD spectroradiometer. The fresh rock samples showed low spectral reflectance as compared to the altered rock samples. The minerals jarosite, goethite, and hematite showed depth of absorption minima in the range of 0.4–1.15 μm due to the presence of iron (Fe), while jarosite and limonite showed absorption depth at 2.2 μm due to the presence of hydroxyl ions (OH^¯). The clay minerals montmorillonite and illite showed absorption depth at 1.93 and 2.1 μm, respectively. Muscovite showed depth of absorption minima at 1.4 and 1.9 μm in some samples. Calcite showed deep absorption minima at 2.32 μm, while anorthite showed absorption features at 1.4, 1.9, 2.24, and 2.33 μm. Olivine showed a slight depressed absorption feature at 1.07 μm. The copper-bearing phases malachite, chrysocolla, and azurite showed, respectively, a broad absorption feature in the range of 0.6–0.9 μm, a small absorption at 1.4 μm, and a deep absorption at 1.93 μm. The unmineralized samples exhibited high reflectance in the wavelength ranges of 0.6–0.8, 1.6–1.9, 2.0–2.3, 2.1–2.25, and 2.4–2.5 μm, respectively, while the mineralized samples showed reflectance bands in the wavelength ranges of 0.4–0.6, 1.3–1.8, and 2.1–2.2 μm. On this basis, the band ratio combinations 7/5–4/3–6/3 and 7/5–6/3–4/3 of Landsat 8 and 4/7–4/3–2/1 for ASTER data were found to be very effective in the lithological differentiation of major rock units.     

A topical evaluation and discussion of data movement technologies for data-intensive scientific applications

Transferring large volumes of information from one location to potentially many others that are geographically distributed and across varying networks is still prevalent in modern scientific data systems. This is despite the movement to push computation to the data and to reduce data movement needed to compute answers to challenging scientific problems, to disseminate information to the scientific community, and to acquire data for curation and enrichment. Because of this, it is imperative that decisions made regarding data movement systems and architectures be backed by both analytical rigor, and also by empirical evidence and measurement. The purpose of this study is to expand on the work performed by our research team over the last decade and to take a fresh look at the evaluation of multiple topical data transfer technologies in use cases derived from data-intensive scientific systems and applications in the areas of Earth science. We report on the evaluation of a set of data movement technologies against a set of empirically derived comparison dimensions. Based on this evaluation, we make recommendations towards the selection of appropriate data movement technologies in scientific applications and scenarios.     

New variogram modeling method using MGGP and SVR

A critical step for kriging in geostatistics is estimation of the variogram. Traditional variogram modeling comprise of the experimental variogram calculation, appropriate variogram model selection and model parameter determination. Selecting of the variogram model and fitting of model parameters is the most controversial aspect of geostatistics. Shapes of valid variogram models are finite, and sometimes, the optimal shape of the model can not be fitted, leading to reduced estimation accuracy. In this paper, a new method is presented to automatically construct a model shape and fit model parameters to experimental variograms using Support Vector Regression (SVR) and Multi-Gene Genetic Programming (MGGP). The proposed method does not require the selection of a variogram model and can directly provide the model shape and parameters of the optimal variogram. The validity of the proposed method is demonstrated in a number of cases.     

Qanat is not a hazard

Comments are presented on the article by Abbasnejad et al. (Environ Earth Sci 75:1306, 2016) dealing with qanat and hazards in Iran. My rebuttal starts from the direct attribution of the described hazards to qanat and addresses the importance in the correct use of terminology for geological hazards. All of the problems pointed out in Iran (subsidence, sinkholes, pollution) have, to me, an anthropogenic origin and cannot be directly ascribed to qanat. Eventually, I present some additional elements to highlight the remarkable importance of qanat systems and their influence on the development of similar underground structures in many countries of the Mediterranean Basin. This remarks the relevance of qanat as cultural heritage sites and the need for their preservation and valorization.     

Characteristics of deuterium excess parameters for geothermal water in Beijing

This study investigates the characteristics of geothermal water in 10 geothermal fields in Beijing. The relationships between the deuterium excess parameter (d) and temperature, depth, age of geothermal groundwater, groundwater flow field, and Eh were investigated using geothermal groundwater samples. Results showed that (1) the average d value of geothermal water is 5.4, whereas that of the groundwater in normal temperature is 6.04. The differences are induced by the oxygen isotope exchange during the water–rock interaction, which may be more easily completed in geothermal water than in cold groundwater. (2) The d value increases remarkably with the age of the geothermal groundwater. The d value increases from 11.2 to 14.6 when the age of the geothermal water is 12,760 ± 130 a and 38,960 ± 630 a, respectively. Moreover, the isotope heat exchange for composition of the hydrogen and oxygen isotopes in the geothermal groundwater proceeds sufficiently with time. (3) The d value decreases from 5.72 to 3.03 when the depth increases from 125.13 to 3221 m. Generally, in the same area, the d value decreases with depth because the temperature is increasing. (4) The d value of the groundwater gradually reduces from the northern recharge area to the southern discharge area. The average d value is 7.31 in the northern recharge area and 5.68 in the middle Beijing Depression, whereas the d value in the southern area of Fengheying is ?9.20. The larger difference in d values between the recharge and discharge areas is due to the slower velocity of underwater flow, which induces longer time for oxygen exchange. (5) The relationship between the d and Eh is complex. When Eh is <200 mV, the d value of the geothermal water decreases with the decrease in Eh. When Eh is higher than 200 mV, the d value increases slightly with the decrease in Eh. The study of the characteristics of deuterium excess parameters for geothermal water could provide a scientific isotopic evidence for assessment and exploitation measures in geothermal groundwater systems.     

2D probabilistic prediction of sparsely measured earth properties constrained by geophysical imaging fully accounting for tomographic reconstruction ambiguity

Many hydrological, environmental, or engineering exploration tasks require predicting spatially continuous scenarios of sparsely measured borehole logging data. We present a methodology to probabilistically predict such scenarios constrained by ill-posed geophysical tomography. Our approach allows for transducing tomographic reconstruction ambiguity into the probabilistic prediction of spatially continuous target parameter scenarios. It is even applicable to data sets where petrophysical relations in the survey area are non-unique, i.e., different facies related petrophysical relations may be present. We employ static two-layer artificial neural networks (ANNs) for prediction and additionally evaluate, whether the training performance of the ANNs can be used to rank geophysical tomograms, which are mathematically equal reconstructions of physical parameter distributions in the ground. We illustrate our methodology using a realistic synthetic database for maximal control about the prediction performance and ranking potential of the approach. For doing so, we try to link geophysical radar and seismic tomography as input parameters to porosity of the ground as target parameter of ANN. However, the approach is flexible and can cope with any combination of geophysical tomograms and hydrologic, environmental or engineering target parameters. Ranking of equivalent geophysical tomograms based on additional borehole logging data is found to be generally possible, but risks remain that the ranking based on the ANN training performance does not fully coincide with the closeness of geophysical tomograms to ground truth. Since geophysical field data sets do usually not offer control options similar to those used in our synthetic database, we do not recommend the utilization of recurrent ANNs to learn weights for the individual geophysical tomograms used in the prediction procedure.