Geospatial analysis of land use driving force in coal mining area: case study in Ningdong,China

GeoJournal - Geospatial techniques such as remote sensing and Geographic Information System are very important for studying the dynamic process of land use. To analyze the land use change and...     

Remote Sensing Inversion for Simulation of Soil Salinization Based on Hyperspectral Data and Ground Analysis in Yinchuan,China

The Pingluo area, as an experimental study area in Yinchuan, has been subjected to major environmental degradation due to soil salinization problems. Soil salinization is one of the main problems of land degradation in arid and semiarid regions. In the present study, remote sensing was integrated with mathematical modeling to evaluate soil salinization adequately. To detect soil salinization, soil water content and electrical conductivity of soil samples were analyzed. The reflectance of soil samples was measured using a spectrometer (SR-3500) with 1024 bands. Indices of soil salinity, vegetation and drought were analyzed using Landsat images over the study area. Based on Landsat images, physicochemical analysis, reflectance of sensitive bands for soil salinization and environmental indices, canopy response salinity index (CRSI), perpendicular drought index (PDI) and enhanced normalized difference vegetation index (ENDVI), a new model was established for simulation and prediction of soil salinization in the study area. Correlation analyses and multiple regression methods were used to construct an accurate model. The results showed that green, blue and near-infrared light was significantly correlated with soil salinity and that the spectral parameters improved this correlation significantly. Therefore, the model was more effective when combining spectral parameters with sensitive bands with modeling. After mathematical transformation of soil reflectance, the correlations of bands sensitive to soil salinization were 0.739 and 0.7 for electrical conductivity and water content, respectively. After transformation of vegetation reflectance, the correlation coefficient of soil salinity became 0.577. After inversion of the model based on soil hyperspectral and water content, the significance became 0.871 and 0.726, respectively, which can be used to predict soil salinity and water content. The spectral soil salinity model had a coefficient of 0.739 for soil salinity prediction. Among the salinity indices, the CRSI was selected as the most significant, with R² of 0.571, whereas the R² for PDI reached only 0.484. Among the vegetation indices, the ENDVI had the highest response to soil salinity, with R² of 0.577. After scale conversion, the correlation percentages between CRSI and measured soil salinity and between ENDVI and measured soil salinity increased to 16.2% and 8.5%, respectively. Following the correlation between PDI and soil water content, the percentage of correlation increased to 11.6%. The integration of hyperspectral remote sensing, ground methods and an inversion method for salinity is a very important and effective technique for rapid and nondestructive monitoring of soil salinization.

     

Evolving a total-evaluation map of flash flood hazard for hydro-prioritization based on geohydromorphometric parameters and GIS–RS manner in Al-Hussain river basin,Tartous, Syria

Floods are one of the most severe hydrological hazards that cause an excessive threat to landscape stability, population welfare, infrastructure and spatial development in the Syrian coastal region. Al-Hussain river basin, like other Syrian coastal basins, is prone to a hazardous, frequent-flooding threat. However, investigating the probable areas of flooding risk is a difficult challenge that results from the almost complete absence of spatially distributed geo-hydrological measurements in Al-Hussain river basin. In this regard, geohydromorphometric parameters (Stream number N_u, Stream length L_u, Bifurcation ratio R_b, Form factor F, Elongation ratio E_b, Drainage density D_d, Stream frequency F_s, Drainage texture T_d, Lemniscate ratio K, Compactness index C, Circulatory ratio R_c, Ruggedness number R_n, Basin relief H_r, and Relief ratio R_r) derived from remote sensing data in the GIS environment can provide a comprehensive and objective approach that can be utilized to map the spatial distribution of flood hazards at the level of delineated sub-basins. This being the case, the present research focuses on exploring the spatial distribution of flood risk in ten sub-basins belonging to Al-Hussain river basin by using spatial techniques tools in order to overall determine the hydro-prioritization of conservation. The geo-visualization map of generated flash flood susceptibility evaluates five degrees of the flood risk: very low, low, moderate, high, and very high. The flood risk map indicates that Qalea, and Talaa sub-basins have massive hydrodynamic risk, which, in turn, indicates the urgent need of soil and water maintaining measures. This hydrological dynamic in these sub-basins is explained by high values of D_d, F, R_r, R_n, R_c, and K, respectively. Overall, the spatial outcomes of the current work successfully proved the efficiency of extracted geohydromorphometric layers from RS data in the context of the spatial assessment of flash flood hazard; they also ensure ecological sustainability and productivity of the study basin.

     

Evidence of fault-propagation folds in foreland basin: the case of Chemsi and Belkhir anticlines of southern Tunisian Atlas

Structural analysis of Jebel Chemsi and Belkhir located in southern Tunisian Atlas lead to propose the fault-propagation fold as a model for these anticlines. Geometric analogy is settled after dip surveys and observation of several anticline kinks. Several, independent geomorphologic observations support the hinge migration kinematics characterizing this numerical model. The geomorphological hallmarks used matches to (1) alluvial fan progradation, (2) knick points on longitudinal profiles of channel streams and (3) anomalies on the drainage net in the eastern limits of the fold. These anomalies proved a centrifugal hinge migration of, at least, last folding stages in the direction prospected by the model. Results of numerical modelling using Ramp EM software showed detachment layer at 5.5 km that matches to Triassic series. Shortening amplitude is about 2 km for Jebel Chemsi and 1.5 km for Jebel Belkhir. Locally, we highlighted the role of inherited faults in locating and controlling the compressive deformation. In active tectonic region, the use of geomorphological approach is suitable to highlight the folding kinematics and thus to prove the deformation model. In our case study, many special conditions, such as excellent outcropping resulting of arid climate, constant base level and good lithological contrast, allow objective interpretations     

Application of the exact constitutive relationship of modified Cam clay to the undrained expansion of a spherical cavity

The modified Cam clay (MCC) model is used to study the response of virgin‐compressed clay subjected to undrained triaxial compression. The MCC constitutive relationship is obtained in a closed form. Both elastic and plastic deviatoric strains are considered in the analysis. The solution allows to obtain total and effective stress paths followed by the clay in undrained spherical expansion. Pore water pressures are determined from the difference between total and effective mean stresses. For illustration purposes, the analysis is also applied to the well‐known reconstituted normally consolidated London clay and the results are compared with the recently published data obtained by a numerical approach. In addition, the Almansi large strains are used in the analysis, as these allow to obtain limit expansion and pore pressures, whereas both small‐strain and logarithmic‐strain approaches do not permit to determine them. Copyright © 2010 John Wiley & Sons, Ltd.     

The role of visual saliency in the automation of seismic interpretation

In this paper, we propose a workflow based on SalSi for the detection and delineation of geological structures such as salt domes. SalSi is a seismic attribute designed based on the modelling of human visual system that detects the salient features and captures the spatial correlation within seismic volumes for delineating seismic structures. Using this attribute we cannot only highlight the neighbouring regions of salt domes to assist a seismic interpreter but also delineate such structures using a region growing method and post‐processing. The proposed delineation workflow detects the salt‐dome boundary with very good precision and accuracy. Experimental results show the effectiveness of the proposed workflow on a real seismic dataset acquired from the North Sea, F3 block. For the subjective evaluation of the results of different salt‐dome delineation algorithms, we have used a reference salt‐dome boundary interpreted by a geophysicist. For the objective evaluation of results, we have used five different metrics based on pixels, shape, and curvedness to establish the effectiveness of the proposed workflow. The proposed workflow is not only fast but also yields better results as compared with other salt‐dome delineation algorithms and shows a promising potential in seismic interpretation.     

Thermo-mechanical behavior of energy piles in high plasticity clays

Energy piles make use of constant and moderate ground temperature for efficient thermal control of buildings. However, this use introduces new engineering challenges because the changes of temperature in the foundation pile and ground induce additional deformations and forces in the foundation element and coupled thermo-hydro-mechanical phenomena in the soil. Several published full-scale tests investigated this aspect of energy piles and showed thermally induced deformation and forces in the foundation element. In parallel, significant progress has been made in the understanding of thermal properties of soils and on the effect of cyclic thermal load on ground and foundation behavior. However, the effect of temperature on the creep rate of energy piles has received practically no attention in the past. This paper reports the experimental results of an in situ tension thermo-mechanical test on an energy pile performed in a very stiff high plasticity clay. During the in situ test, the pile was subjected to thermal loading by circulating hot water in fitted pipes, simulating a thermal load in a cooling-dominated climate, at different levels of mechanical loading. The axial strain and temperature in the pile, and the load–displacement of the pile were monitored during the tension test at different locations along the center of the pile and at the pile head, respectively. The data showed that as the temperature increases, the observed creep rate of the energy pile in this high plasticity clay also increases, which will lead to additional time-dependent displacement of the foundation over the life time of the structure. It was also found that the use of geothermal piles causes practically insignificant thermally induced deformation and loads in the pile itself.     

Shape factors of cylindrical piezometers in uniform soil

This article presents the analytical solution of Laplace equation for the steady flow around open-ended cylindrical piezometers located in an infinite, isotropic, and incompressible saturated soil. Shape factors are obtained for piezometers with varying length-to-diameter ratios. Comparisons are made with published factors obtained by means of approximate analytical solutions, numerical approaches, and measurements in electric analog models. It is shown that some expressions that are currently used in practice are inadequate and should be abandoned.     

Geochemical assessment of Paleocene limestones of Sinn El-Kaddab Plateau,South Western Desert of Egypt,for industrial uses

The present work assesses the potential industrial uses of Paleocene limestone deposits, Garra Formation in Sinn El-Kaddab Plateau, South Western Desert of Egypt, based on their mineralogy and geochemical characteristics. Eighty-six limestone samples collected from ten stratigraphic columnar sections were analyzed using X-ray diffraction (XRD) and X-ray fluorescence (XRF) techniques. Petrographically, Garra limestone deposits consist dominantly of benthic foraminiferal wackestone and packstone microfacies. The XRD data revealed dominance of low-Mg calcite as essential carbonate mineral in the studied limestone. The bulk rock XRF results revealed CaO (29.28–55.75%) with an average of 52.74%. The CaO exhibits a strong negative correlation with MgO, SiO₂, TiO₂, Al₂O₃, and moderate negative correlation with Fe₂O₃ which indicates that the contribution of these elements is mainly due to detrital input. On the other hand, Al₂O₃ and Na₂O contents exhibit significant negative correlation with loss of ignition (LOI) wt% indicating their incorporation within terrigenous matrix. The average Sr content attains 1297 ppm indicating deposition in temperate marine environment. The average molar CaCO₃% values of bulk samples have been computed for individual columnar sections to determine the purity status of Garra limestone deposits. They range from impure (CaCO₃%?=?79.92%) to high-pure (CaCO₃%?=?97.76%) limestone. Therefore, Garra limestone deposits have extremely valuable uses in a wide spectrum of industrial applications including Portland cement, steel, ceramics, whiting, chemical uses, paper, and feed stuff.     

A comparison of seismic saltbody interpretation via neural networks at sample and pattern levels

Saltbodies are important subsurface structures that have significant implications for hydrocarbon accumulation and sealing in petroleum reservoirs, and accurate saltbody imaging and delineation is now greatly facilitated with the availability of three-dimensional seismic surveying. However, with the growing demand for larger survey coverage and higher imaging resolution, the size of seismic data is increasing dramatically. Correspondingly, manual saltbody interpretation fails to offer an efficient solution, particularly in exploration areas of complicated salt intrusion history. Recently, artificial intelligence is attracting great attention from geoscientists who desire to utilize the popular machine learning technologies for evolving the interpretational tools capable of mimicking an experienced interpreter's intelligence. This study first implements two popular machine learning tools, the multi-layer perceptron and the convolutional neural network, for delineating seismic saltbodies at sample and pattern levels, respectively, then compares their performance through applications to the synthetic SEAM seismic volume, and moreover tentatively investigates what contributes to the better convolutional neural network delineation. Specifically, the multi-layer perceptron scheme is capable of efficiently utilizing an interpreter's knowledge by selecting, pre-conditioning and integrating a set of seismic attributes that best highlight the target saltbodies, whereas the convolutional neural network scheme makes it possible for saltbody delineation directly from seismic amplitude and thus significantly reduces the dependency on attribute selection from interpreters. It is concluded that the better performance from the convolutional neural network scheme results from two factors. First, the convolutional neural network builds the mapping relationship between the seismic signals and the saltbodies using the original seismic amplitude instead of manually selected seismic attributes, so that the negative impact of using less representative attributes is virtually eliminated. Second and more importantly, the convolutional neural network defines, learns and identifies the saltbodies by utilizing local seismic reflection patterns, so that the seismic noises and processing artefacts of distinct patterns are effectively identified and excluded.