Excellence in Research and Innovation for Humanity

International Science Index

Commenced in January 1999 Frequency: Monthly Edition: International Abstract Count: 43397

Geological and Environmental Engineering

Study of the Mineralogical and Paragenetic of Gold Mineralization at the Zwartkoppie Ore Body in Barberton Greenstone Belt Mines, Mpumalanga Province, south Africa
The study of mineralogical and paragenetic of gold mineralization at the Zwartkoppie ore-body was conducted at Barberton greenstone belt mines which include Sheba, Fairview, New Consort and Albion mine. The Zwartkoppie ore-body is one of the major gold producers at Barberton Greenstone belt mines in South Africa. The metallogenesis of Zwartkoppie orebody is not clearly understood and this poses significant challenge on further exploration of the ore. To achieve the research problem, drilled cores from the study area were logged in detail, to confirm the mineralization of sulfide minerals along quartz veins, intersection of various fracture zones within the brecciated and banded chert unit. The major objective of the study was to establish a paragenetic model for drill cores from the Zwartkoppie ore-body at Barberton greenstone belt mines. Fifteen drill core samples were collected from the Zwartkoppie ore-body of the Onverwacht Group. All the cores were analyzed petrographically using standard optical microscope accompanied by XRD analyses. The petrological and mineralogical studies indicated evidence of a pervasive hydrothermal alterations throughout the Zwartkoppie ore-body due to interaction of hydrothermal fluids with host rocks which then introduced new minerals into the host rocks. The paragenetic model indicated that the sequence of the Zwartkoppie ore-body mineralization is two stages, whereby the pre-metamorphic stage was characterized by early formed silicates (quartz, chlorite, plagioclase and muscovite) and carbonates (ankerite, dolomite and calcite), which reflect the mineralogy of the greywacke and greenschist host rocks. The mineralization stage was represented by the minerals associated with the hydrothermal fluid minerals such as pyrite, pyrrhotite, chalcopyrite, galena and secondary silicate and carbonate minerals that included but not limited to ankerite, quartz, chlorite and muscovite.
An Investigation on the Geotechnical Properties of Coal Combustion by Products from Matimba Power Station in Lephalale, South Africa
The production of electricity by burning coal produces huge amount of fly ash and bottom ash, hence with the increase of the quantum of the thermal power generation the production of the fly ash also rises to the top. The major problems faced at the Matimba power station is the disposal and the handling of the byproduct of coal. The objectives of this investigation were to investigate the geotechnical properties of fly and bottom ash to establish their possible utilisation as a construction material, to determine the grain size distribution thereof ,to analyze the consistency of coal fly ash and bottom ash and perform comparative analysis of the geotechnical properties for suitability of the material as a building material, as well as to identify geotechnical constraints that may have an adverse effect on the project. The methodology used includes preliminary studies, reconnaissance survey, soil sampling and laboratory tests. This involves determination of Atterberg limits, sieve analysis, moisture content and soil classification. Results of the laboratory tests indicate that the material of coal fly ash is mainly clayey, while bottom ash is mainly silty. The coal fly ash was found to have low plasticity, low liquid limit as well as low plasticity index with low moisture content. The bottom ash on the other hand has considerable plastic limit, liquid limit with high moisture content. The plastering sand has been determined to have low plastic limit, considerable liquid limit with low plasticity index. In comparison with the mixture of ashes with plastering sand the material can be used as plastering material for building purposes.
The Performance of Flexi-Bolts Reinforcement System at Deep Level Gold Mine in South Africa
Most of South African mechanized and semi-mechanized gold mines in Witwatersrand basin are operating at depths ranging from 2.7 to 4km below the surface. The stress levels tend to increase with the increase in mining depth. In order to apply massive mining techniques, destressing is imperative. This destressing is accomplished by cutting a series of overlapping, 2.2 m high horizontal slots across the reef. Although the minor span of each slot is about 100m, stresses are extremely high due to presence of abutments. Seismicity of mines within Carletonville district are frequent and can be compared to each other. However, at current mining rates the magnitudes of Deep level gold mine are generally less than the neighboring mines. Safe and optimal performance of roof support remains one of the biggest challenges in underground mining today. In order to protect workers from the effects of smaller seismic events, a yielding system consisting of weld-mesh (100 mm apertures and 5 mm strands) and 2.4 m long, yielding anchors has been developed. The possible factors influencing the effectiveness of the flexi-bolts were investigated at two named Shaft A and Shaft B sections for this study. The investigation involved the analysis of whether the effect of corrosion, grouting, tensioning and angle of installation were the main causes of poor performance of flexi-bolts or whether their design did not meet their set specifications. Underground pull test were also conducted and compared with mass drop tests in order to understand the behavior of flexi-bolts under seismic events.
Hedgerow Detection and Characterization Using Very High Spatial Resolution SAR DATA
Hedgerow has an important role for a wide range of ecological habitats, landscape, agriculture management, carbon sequestration, wood production. Hedgerow detection accurately using satellite imagery is a challenging problem in remote sensing techniques, because in the special approach it is very similar to line object like a road, from a spectral viewpoint, a hedge is very similar to a forest. Remote sensors with very high spatial resolution (VHR) recently enable the automatic detection of hedges by the acquisition of images with enough spectral and spatial resolution. Indeed, recently VHR remote sensing data provided the opportunity to detect the hedgerow as line feature but still remain difficulties in monitoring the characterization in landscape scale. In this research is used the TerraSAR-x Spotlight and Staring mode with 3-5 m resolution in wet and dry season in the test site of Fermoy County, Ireland to detect the hedgerow by acquisition time of 2014-2015. Both dual polarization of Spotlight data in HH/VV is using for detection of hedgerow. The varied method of SAR image technique with try and error way by integration of classification algorithm like texture analysis, support vector machine, k-means and random forest are using to detect hedgerow and its characterization. We are applying the Shannon entropy (ShE) and backscattering analysis in single and double bounce in polarimetric analysis for processing the object-oriented classification and finally extracting the hedgerow network. The result still is in progress and need to apply the other method as well to find the best method in study area. Finally, this research is under way to ahead to get the best result and here just present the preliminary work that polarimetric image of TSX potentially can detect the hedgerow.
Rapid Monitoring of Earthquake Damage by Optical and SAR Data
Earthquake is an inevitable catastrophic natural disaster. The damages of buildings and man-made structures, where most of the human activities occur are the major cause of casualties from earthquakes. A comparison of optical and SAR data is presented in the case of Kathmandu valley which was hardly shaken by 2015-Nepal Earthquake. Though many existing researchers have conducted optical data based estimates or suggested combined use of optical and SAR data for improved accuracy, finding cloud-free optical images when urgently needed are not assured. Therefore, this research is specialized in developing SAR data based technique with the target of rapid and accurate geospatial reporting. Should consider that limited time available in post-disaster situation offering quick computation solely based on two pairs of pre-seismic and co-seismic single look complex (SLC) images. The InSAR coherence pre-seismic, cCo-seismic and post-seismic was used to detect the change in damaged area. In addition, the ground truth data from field applied to optical data by random forest classification for detection of damaged area. The ground truth data collected in the field were used to assess the accuracy of supervised classification approach. Though higher accuracy was obtained from the optical data, then integration of optical-SAR data finding cloud-free images when urgently needed are not assured; therefore, further research on improving the SAR based damage detection is suggested. Availability of very accurate damage information is expected for channeling the rescue and emergency operations. It is expected that the quick reporting of the post-disaster damage situation quantified by the rapid earthquake assessment should assist in channeling the rescue and emergency operations, and in informing the public about the scale of damage.
Construction of Submerged Aquatic Vegetation Index through Global Sensitivity Analysis of Radiative Transfer Model
Submerged aquatic vegetation (SAV) in wetlands can absorb nitrogen and phosphorus effectively to prevent the eutrophication of water. It is feasible to monitor the distribution of SAV through remote sensing, but for the reason of weak vegetation signals affected by water body, traditional terrestrial vegetation indices are not applicable. This paper aims at constructing SAV index to enhance the vegetation signals and distinguish SAV from water body. The methodology is as follows: (1) select the bands sensitive to the vegetation parameters based on global sensitivity analysis of SAV canopy radiative transfer model; (2) take the soil line concept as reference, analyze the distribution of SAV and water reflectance simulated by SAV canopy model and semi-analytical water model in the two-dimensional space built by different sensitive bands; (3)select the band combinations which have better separation performance between SAV and water, and use them to build the SAVI indices in the form of normalized difference vegetation index(NDVI); (4)analyze the sensitivity of indices to the water and vegetation parameters, choose the one more sensitive to vegetation parameters. It is proved that index formed of the bands with central wavelengths in 705nm and 842nm has high sensitivity to chlorophyll content in leaves while it is less affected by water constituents. The model simulation shows a general negative, little correlation of SAV index with increasing water depth. Moreover, the index enhances capabilities in separating SAV from water compared to NDVI. The SAV index is expected to have potential in parameter inversion of wetland remote sensing.
Calibration of the Radical Installation Limit Error of the Accelerometer in the Gravity Gradient Instrument
Gravity gradient instrument (GGI) is the core of the gravity gradiometer, so the structural error of the sensor has a great impact on the measurement results. In order not to affect the aimed measurement accuracy, limit error is required in the installation of the accelerometer. In this paper, based on the established measuring principle model, the radial installation limit error is calibrated, which is taken as an example to provide a method to calculate the other limit error of the installation under the premise of ensuring the accuracy of the measurement result. This method provides the idea for deriving the limit error of the geometry structure of the sensor, laying the foundation for the mechanical precision design and physical design.
Determination of Inflow Performance Relationship for Naturally Fractured Reservoirs: Numerical Simulation Study
The Inflow Performance Relationship (IPR) of a well is a relation between the oil production rate and flowing bottom-hole pressure. This relationship is an important tool for petroleum engineers to understand and predict the well performance. In the petroleum industry, IPR correlations are used to design and evaluate well completion, optimizing well production, and designing artificial lift. The most commonly used IPR correlations models are Vogel and Wiggins, these models are applicable to homogeneous and isotropic reservoir data. In this work, a new IPR model is developed to determine inflow performance relationship of oil wells in a naturally fracture reservoir. A 3D black-oil reservoir simulator is used to develop the oil mobility function for the studied reservoir. Based on simulation runs, four flow rates are run to record the oil saturation and calculate the relative permeability for a naturally fractured reservoir. The new method uses the result of a well test analysis along with permeability and pressure-volume-temperature data in the fluid flow equations to obtain the oil mobility function. Comparisons between the new method and two popular correlations for non-fractured reservoirs indicate the necessity for developing and using an IPR correlation specifically developed for a fractured reservoir.
An Approach for Determination of Shotcrete Thickness in Underground Structures
An intrinsic property of rock mass known as rock bolt supporting factor (RSF) or rock bolting capability of rock mass was developed and used for explanation of the mechanism of rock bolting practice. Based on the theory of RSF, numeral values can be assigned to each given rock mass to show the capability of that rock mass to be reinforced by rock bolting. For determination of shotcrete thickness, both safety and cost must be taken into account. The present paper introduces a scientific approach for determination of the necessary shotcrete thickness in underground structures for support purposes using the concept of rock bolt supporting factor (RSF). The proposed approach makes the outcome of shotcrete design one step more accurate than before. The actual dataset of 500 meters of Alborz Tunnel length is used as an example of the application of the approach.
Assessing the Utility of Unmanned Aerial Vehicle-Borne Hyperspectral Image and Photogrammetry Derived 3D Data for Wetland Species Distribution Quick Mapping
Lightweight unmanned aerial vehicle (UAV) loading with novel sensors offers a low cost approach for data acquisition in complex environment. This study established a framework for applying UAV system in complex environment quick mapping and assessed the performance of UAV-based hyperspectral image and digital surface model (DSM) derived from photogrammetric point clouds for 13 species classification in wetland area Mai Po Inner Deep Bay Ramsar Site, Hong Kong. The study area was part of shallow bay with flat terrain and the major species including reedbed and four mangroves: Kandelia obovata, Aegiceras corniculatum, Acrostichum auerum and Acanthus ilicifolius. Other species involved in various graminaceous plants, tarbor, shrub and invasive species Mikania micrantha. In particular, invasive species climbed up to the mangrove canopy caused damage and morphology change which might increase species distinguishing difficulty. Hyperspectral images were acquired by Headwall Nano sensor with spectral range from 400nm to 1000nm and 0.06m spatial resolution image. A sequence of multi-view RGB images was captured with 0.02m spatial resolution and 75% overlap. Hyperspectral image was corrected for radiative and geometric distortion while high resolution RGB images were matched to generate maximum dense point clouds. Furtherly, a 5 cm grid digital surface model (DSM) was derived from dense point clouds. Multiple feature reduction methods were compared to identify the efficient method and to explore the significant spectral bands in distinguishing different species. Examined methods including stepwise discriminant analysis (DA), support vector machine (SVM) and minimum noise fraction (MNF) transformation. Subsequently, spectral subsets composed of the first 20 most importance bands extracted by SVM, DA and MNF, and multi-source subsets adding extra DSM to 20 spectrum bands were served as input in maximum likelihood classifier (MLC) and SVM classifier to compare the classification result. Classification results showed that feature reduction methods from best to worst are MNF transformation, DA and SVM. MNF transformation accuracy was even higher than all bands input result. Selected bands frequently laid along the green peak, red edge and near infrared. Additionally, DA found that chlorophyll absorption red band and yellow band were also important for species classification. In terms of 3D data, DSM enhanced the discriminant capacity among low plants, arbor and mangrove. Meanwhile, DSM largely reduced misclassification due to the shadow effect and morphological variation of inter-species. In respect to classifier, nonparametric SVM outperformed than MLC for high dimension and multi-source data in this study. SVM classifier tended to produce higher overall accuracy and reduce scattered patches although it costs more time than MLC. The best result was obtained by combining MNF components and DSM in SVM classifier. This study offered a precision species distribution survey solution for inaccessible wetland area with low cost of time and labour. In addition, findings relevant to the positive effect of DSM as well as spectral feature identification indicated that the utility of UAV-borne hyperspectral and photogrammetry deriving 3D data is promising in further research on wetland species such as bio-parameters modelling and biological invasion monitoring.
Extraction of Urban Building Damage Using Spectral, Height and Corner Information
Timely and accurate information on urban building damage caused by earthquake is important basis for disaster assessment and emergency relief. Very high resolution (VHR) remotely sensed imagery containing abundant fine-scale information offers a large quantity of data for detecting and assessing urban building damage in the aftermath of earthquake disasters. However, the accuracy obtained using spectral features alone is comparatively low, since building damage, intact buildings and pavements are spectrally similar. Therefore, it is of great significance to detect urban building damage effectively using multi-source data. Considering that in general height or geometric structure of buildings change dramatically in the devastated areas, a novel multi-stage urban building damage detection method, using bi-temporal spectral, height and corner information, was proposed in this study. The pre-event height information was generated using stereo VHR images acquired from two different satellites, while the post-event height information was produced from airborne LiDAR data. The corner information was extracted from pre- and post-event panchromatic images. The proposed method can be summarized as follows. To reduce the classification errors caused by spectral similarity and errors in extracting height information, ground surface, shadows, and vegetation were first extracted using the post-event VHR image and height data and were masked out. Two different types of building damage were then extracted from the remaining areas: the height difference between pre- and post-event was used for detecting building damage showing significant height change; the difference in the density of corners between pre- and post-event was used for extracting building damage showing drastic change in geometric structure. The initial building damage result was generated by combining above two building damage results. Finally, a post-processing procedure was adopted to refine the obtained initial result. The proposed method was quantitatively evaluated and compared to two existing methods in Port au Prince, Haiti, which was heavily hit by an earthquake in January 2010, using pre-event GeoEye-1 image, pre-event WorldView-2 image, post-event QuickBird image and post-event LiDAR data. The results showed that the method proposed in this study significantly outperformed the two comparative methods in terms of urban building damage extraction accuracy. The proposed method provides a fast and reliable method to detect urban building collapse, which is also applicable to relevant applications.
Sparse Representation Based Spatiotemporal Fusion Employing Additional Image Pairs to Improve Dictionary Training
Remotely sensed imagery with the high spatial and temporal characteristics, which it is hard to acquire under the current land observation satellites, has been considered as a key factor for monitoring environmental changes over both global and local scales. On a basis of the limited high spatial-resolution observations, challenged studies called spatiotemporal fusion have been developed for generating high spatiotemporal images through employing other auxiliary low spatial-resolution data while with high-frequency observations. However, a majority of spatiotemporal fusion approaches yield to satisfactory assumption, empirical but unstable parameters, low accuracy or inefficient performance. Although the spatiotemporal fusion methodology via sparse representation theory has advantage in capturing reflectance changes, stability and execution efficiency (even more efficient when overcomplete dictionaries have been pre-trained), the retrieval of high-accuracy dictionary and its response to fusion results are still pending issues. In this paper, we employ additional image pairs (here each image-pair includes a Landsat Operational Land Imager and a Moderate Resolution Imaging Spectroradiometer acquisitions covering the partial area of Baotou, China) only into the coupled dictionary training process based on K-SVD (K-means Singular Value Decomposition) algorithm, and attempt to improve the fusion results of two existing sparse representation based fusion models (respectively utilizing one and two available image-pair). The results show that more eligible image pairs are probably related to a more accurate overcomplete dictionary, which generally indicates a better image representation, and is then contribute to an effective fusion performance in case that the added image-pair has similar seasonal aspects and image spatial structure features to the original image-pair. It is, therefore, reasonable to construct multi-dictionary training pattern for generating a series of high spatial resolution images based on limited acquisitions.
Mathematical Modeling of the Working Principle of Gravity Gradient Instrument
Gravity field is of great significance in geoscience, national economy and national security, and gravitational gradient measurement has been extensively studied due to its higher accuracy than gravity measurement. Gravity gradient sensor, being one of core devices of the gravity gradient instrument, plays a key role in measuring accuracy. Therefore, this paper starts from analyzing the working principle of the gravity gradient sensor by Newton’s law, and then considers the relative motion between inertial and non-inertial systems to build a relatively adequate mathematical model, laying a foundation for the measurement error calibration, measurement accuracy improvement.
Geofusion- Mapping of the 21st Century
The presentation ’Geofusion’ guides the audience with the help of maps in the global world of the 21st century through the quest for the winning nations, communities, leaders and powers of this age. The explorers and the geostrategists of this century are expected to present guidelines of our new world full of global social and economic challenges. To do so, new maps are needed which do not miss the wisdom and tools of the old but complement it with the new structure of knowledge. Using the lately discovered geographic and economic interrelations, the presentation tries to give a prognosis of the global processes. The methodology contains the survey and analysis of many recent publications worldwide, regarding geostrategic, cultural, geographical, social and economic surveys structured into global networks. The result is a collage of the global map of the 21st century mentioned above. In conclusion, the presentation displays the results of a several year long studies giving the audience an image how economic navigation tools can help the investors and travelers to get along in the changing new world.
Physical and Mechanical Phenomena Associated with Rock Failure in Brazilain Disc Specimens
Failure mechanism of rocks is one of the fundamental aspects to study rock engineering stability. Rock is a material that contains flaws, initial damage, micro-cracks, etc. failure of rock is a dynamic, gradual and cumulative process of nucleation, growth, propagation, coalescence of micro-cracks, which is a non-equilibrium, non-linear evolutionary process. In the present study, the effect of brittleness and loading rate on the physical and mechanical phenomena produced in rock during loading sequences is considered. For this purpose, Acoustic Emission (AE) technique is used to monitor fracturing process of three rock types (Onyx Marble, Sandstone and soft Limestone) with different brittleness and Sandstone samples under different loading rate. The results of experimental tests revealed that brittleness and loading rate have a significant effect on the mode and number of induced fracture in rocks. An increase in rock brittleness increases the frequency of induced cracks, and the number of tensile fracture decreases when loading rate increases.
A Hybrid Image Fusion Model for Generating High Spatial-Temporal-Spectral Resolution Data Using Operational Land Imager-Moderate Resolution Imaging Spectroradiometer-Hyperion Satellite Imagery
Nowadays, a large number of Earth observation satellites have been launching, with various Spatial, Temporal and Spectral Resolution (STSR), which contribute significantly to Earth surface monitoring ability. It seems that there is a boom in Earth observation field, nevertheless, because of the limitations of satellite sensor’s technology and budget constraints, there exist compromises between STSR of satellite data. That is to say, even though so many satellites have been launched, none of them can obtain high STSR data simultaneously. These compromises limit the application of existing remotely sensed data significantly, especially for the remote sensing applications that requires fine spatial detail, long-term and frequent coverage, and hyper-spectral (HS) satellite imagery, such as precise global or regional change detection, time series analysis, urban dynamic monitoring, natural disaster monitoring, real-time air quality monitoring, etc. Image fusion provides a feasible means to overcome the limitations of the current Earth observation data, mainly including spatial and spectral fusion, and spatial and temporal fusion. However, these fusion technologies can only solve part of the resolution enhancement problems, which cannot generate synthetic images with high STSR simultaneously. This study proposed a Hybrid Spatial-Temporal-Spectral image Fusion Model (HSTSFM) to generate synthetic satellite data with high STSR simultaneously, which adopts the Landsat-8 Operational Land Imager (OLI, including its panchromatic (PAN) and multi-spectral (MS) bands), Moderate Resolution Imaging Spectroradiometer (MODIS) and Hyperion images as data sources. HSTSFM blends the high spatial resolution from OLI-PAN image, i.e., 15 m, the high temporal resolution from MODIS images, i.e., daily observations, and the high spectral resolution from Hyperion image, i.e., 146 good-quality bands out of 242 bands to produce daily HS images with a spatial resolution of 15 m, which aims to satisfy the growing demand of high STSR satellite images. The proposed HSTSFM model contains three fusion steps: (1) PAN and MS image fusion with an Enhanced Synthetic Variable Ratio (ESVR) algorithm to get high spatial resolution OLI-MS image at the base date; (2) Spatial and temporal image fusion with One-Pair Image Dictionary Learning method to obtain high spatial resolution OLI-MS image at the prediction date; (3) Temporal and spectral image fusion with a Modified Color Resolution Improvement Software Package (MCRISP) algorithm to obtain the high spatial resolution Hyperion image at the prediction date, i.e., the final predicted high STSR image. The proposed method adopted the Beijing area, China as a test region, and used the actual OLI-PAN and Hyperion images at the prediction date to evaluate the precision of the fusion result from spatial detail information and spectral properties similarities aspects. The experimental results indicate that HSTSFM can capture the land cover changes accurately and has good spatial and spectral fidelity to the reference OLI-PAN and Hyperion images. The proposed HSTSFM can fuse spatial, temporal, and spectral information from different types of satellite sensors, which has the potential to generate synthetic data to support the studies that require high STSR satellite imagery.
Estimation of Reservoirs Fracture Network Properties Using an Artificial Intelligence Technique
The main objective of this study is to develop a subsurface fracture map of naturally fractured reservoirs by overcoming the limitations associated with different data sources in characterising fracture properties. Some of these limitations are overcome by employing a nested neuro-stochastic technique to establish inter-relationship between different data, as conventional well logs, borehole images (FMI), core description, seismic attributes, and etc. and then characterise fracture properties in terms of fracture density and fractal dimension for each data source. Fracture density is an important property of a system of fracture network as it is a measure of the cumulative area of all the fractures in a unit volume of a fracture network system and Fractal dimension is also used to characterize self-similar objects such as fractures. At the wellbore locations, fracture density and fractal dimension can only be estimated for limited sections where FMI data are available. Therefore, artificial intelligence technique is applied to approximate the quantities at locations along the wellbore, where the hard data is not available. It should be noted that Artificial intelligence techniques have proven their effectiveness in this domain of applications.
Inversion of Gravity Data for Density Reconstruction
Inverse problem generally used for recovering hidden information from outside available data. Vertical component of gravity field we will be going to use for underneath density structure calculation. Ill-posing nature is main obstacle for any inverse problem. Linear regularization using Tikhonov formulation are used for appropriate choice of SVD and GSVD components. For real time data handle, signal to noise ratios should have to be less for reliable solution. In our study, 2D and 3D synthetic model with rectangular grid are used for gravity field calculation and its corresponding inversion for density reconstruction. Fine grid also we have considered to hold any irregular structure. Keeping in mind of algebraic ambiguity factor number of observation point should be more than that of number of data point. Picard plot is represented here for choosing appropriate or main controlling Eigenvalues for a regularized solution. Another important study is depth resolution plot (DRP). DRP are generally used for studying how the inversion is influenced by regularizing or discretizing. Our further study involves real time gravity data inversion of Vredeforte Dome South Africa. We apply our method to this data. The results include density structure is in good agreement with known formation in that region, which puts an additional support of our method.
Monitoring of Cannabis Cultivation with High-Resolution Images
Cannabis is mostly used for drug production. In some countries, an excessive amount of illegal cannabis is cultivated and sold. Most of the illegal cannabis cultivation occurs on the lands far from settlements. In farmlands, it is cultivated with other crops. In this method, cannabis is surrounded by tall plants like corn and sunflower. It is also cultivated with tall crops as the mixed culture. The common method of the determination of the illegal cultivation areas is to investigate the information obtained from people. This method is not sufficient for the determination of illegal cultivation in remote areas. For this reason, more effective methods are needed for the determination of illegal cultivation. Remote Sensing is one of the most important technologies to monitor the plant growth on the land. The aim of this study is to monitor cannabis cultivation area using satellite imagery. The main purpose of this study was to develop an applicable method for monitoring the cannabis cultivation. For this purpose, cannabis was grown as single or surrounded by the corn and sunflower in plots. The morphological characteristics of cannabis were recorded two times per month during the vegetation period. The spectral signature library was created with the spectroradiometer. The parcels were monitored with high-resolution satellite imagery. With the processing of satellite imagery, the cultivation areas of cannabis were classified. To separate the Cannabis plots from the other plants, the multiresolution segmentation algorithm was found to be the most successful for classification. WorldView Improved Vegetative Index (WV-VI) classification was the most accurate method for monitoring the plant density. As a result, an object-based classification method and vegetation indices were sufficient for monitoring the cannabis cultivation in multi-temporal Earthwiev images.
Runoff Simulation by Using WetSpa Model in Garmabrood Watershed of Mazandaran Province, Iran
Hydrological models are applied to simulation and prediction floods in watersheds. WetSpa is a distributed, continuous and physically model with daily or hourly time step that explains of precipitation, runoff and evapotranspiration processes for both simple and complex contexts. This model uses a modified rational method for runoff calculation. In this model, runoff is routed along the flow path using Diffusion-Wave Equation which depend on the slope, velocity and flow route characteristics. Garmabrood watershed located in Mazandaran province in Iran and passing over coordinates 53° 10´ 55" to 53° 38´ 20" E and 36° 06´ 45" to 36° 25´ 30"N. The area of the catchment is about 1133 km2 and elevations in the catchment range from 213 to 3136 m at the outlet, with average slope of 25.77 %. Results of the simulations show a good agreement between calculated and measured hydrographs at the outlet of the basin. Drawing upon Nash-Sutcliffe Model Efficiency Coefficient for calibration periodic model estimated daily hydrographs and maximum flow rate with an accuracy up to 61% and 83.17 % respectively.
Ultrasonic Techniques to Characterize and Monitor Water-in-Oil Emulsion
Oil-water emulsions are commonly encountered in various industrial operations and at different stages of crude oil production and processing. Emulsions are often difficult to track and treat and can cause a number of costly problems which need to be avoided. The characteristics of the emulsion phase can vary with crude composition and types of impurities present in oil. The objectives of this study are the development of ultrasonic techniques to track and characterize emulsion phase generated during production and cleaning of crude oil. The position of emulsion layer is monitored with the help of ultrasonic probes suitably placed in the vessel. The sensitivity of the technique and its potential has been demonstrated based on extensive testing with different oil samples. The technique is also being developed to monitor emulsion phase characteristics such as stability, composition, and droplet size distribution. The ultrasonic parameters recorded are changes in acoustic velocity, signal attenuation and its frequency spectrum. Emulsion has been prepared with light mineral oil sample and the effects of various factors including mixing speed, temperature, surfactant, and solid particles concentrations have been investigated. The applied frequency for ultrasonic waves has been varied from 1 to 5 MHz to carry out a sensitivity analysis. Emulsion droplet structure is observed with optical microscopy and stability is examined by tracking the changes in ultrasonic parameters with time. A model based on ultrasonic attenuation spectroscopy is being developed and tested to track changes in droplet size distribution with time.
Analysis of Production Forecasting in Unconventional Gas Resources Development Using Machine Learning and Data-Driven Approach
Unconventional gas resources have dramatically changed the future energy landscape. Unlike conventional gas resources, the key challenges in unconventional gas have been the requirement that apply to different approach for production forecasting due to uncertainly and complexity of fluid flow. Therefore, it is important that accurate production forecasting in unconventional gas resources. In this paper, we present a production forecasting workflow involving machine learning and data-driven approach which is a straightforward process, production correlation of obtaining information from various data sets to perform production forecasting of unconventional gas resources. This study takes advantage of applying various machine learning method in order to investigate the impact of native and design parameters on production as well as optimizing the predictive model using artificial neural network method. We applied to a Eagle Ford Shale asset that includes 200 horizontal wells from 3 county-Webb, La Salle, Dimmit to make data sets for Input- output of the prediction model. The proposed workflow involves using decision of data sets to train and clustering using machine learning and importance weight analysis for input data of predictive model which, subsequently, is used to predict the well production and performance of both existing wells and new planed wells. The proposed method can be used as assistance of decision making process that employs various data from native and design parameters for forecasting of existing wells and new wells in unconventional resources. It is not only useful for evaluating the effects of various parameters on productivity, but also provide a reliable approach to predict well production towards different completion plan in anywhere filed.
Use of Landsat OLI Images in the Mapping of Landslides: Case of the Taounate Province in Northern Morocco
Northern Morocco is characterized by relatively young mountains experiencing a very important dynamic compared to other areas of Morocco. The dynamics associated with the formation of the Rif chain (Alpine tectonics), is accompanied by instabilities essentially related to tectonic movements. The realization of important infrastructures (Roads, Highways,...) represents a triggering factor and favoring landslides. This paper is part of the establishment of landslides susceptibility map and concerns the mapping of unstable areas in the province of Taounate. The landslide was identified using the components of the false color (FCC) of images Landsat OLI: i) the first independent component (IC1), ii) The main component (PC), iii) Normalized difference index (NDI). This mapping for landslides class is validated by in-situ surveys.
Accessibility Analysis of Urban Green Space in Zadar Settlement, Croatia
The accessibility of urban green spaces (UGS) is an integral element in the quality of life. Due to rapid urbanization, UGS studies have become a key element in urban planning. The potential benefits of space for its inhabitants are frequently analysed. A functional transport network system and the optimal spatial distribution of urban green surfaces are the prerequisites for maintaining the environmental equilibrium of the urban landscape. An accessibility analysis was conducted as part of the Urban Green Belts Project (UGB). The development of a GIS database for Zadar was the first step in generating the UGS accessibility indicator. Data were collected using the supervised classification method of multispectral LANDSAT images and manual vectorization of digital orthophoto images (DOF). An analysis of UGS accessibility according to the ANGst standard was conducted in the first phase of research. The accessibility indicator was generated on the basis of seven objective measurements, which included average UGS surface per capita and accessibility according to six functional levels of green surfaces. The generated indicator was compared with subjective measurements obtained by conducting a survey (718 respondents) within statistical units. The collected data reflected individual assessments and subjective evaluations of UGS accessibility. This study highlighted the importance of using objective and subjective measures in the process of understanding the accessibility of urban green surfaces. It may be concluded that when evaluating UGS accessibility, residents emphasize the immediate residential environment, ignoring higher UGS functional levels. It was also concluded that large areas of UGS within a city do not necessarily generate similar satisfaction with accessibility. The heterogeneity of output results may serve as guidelines for the further development of a functional UGS city network.
Rescaled Range Analysis of Seismic Time-Series: Example of the Recent Seismic Crisis of Alhoceima
Persistency, long-term memory and randomness are intrinsic properties of time-series of earthquakes. The Rescaled Range Analysis (RS-Analysis) was introduced by Hurst in 1956 and modified by Mandelbrot and Wallis in 1964. This method represents a simple and elegant analysis which determines the range of variation of one natural property (the seismic energy released in this case) in a time interval. Despite the simplicity, there is complexity inherent in the property measured. The cumulative curve of the energy released in time is the well-known fractal geometry of a devil’s staircase. This geometry is used for determining the maximum and minimum value of the range, which is normalized by the standard deviation. The rescaled range obtained obeys a power-law with the time, and the exponent is the Hurst value. Depending on this value, time-series can be classified in long-term or short-term memory. Hence, an algorithm has been developed for compiling the RS-Analysis for time series of earthquakes by days. Completeness time distribution and locally stationarity of the time series are required. The interest of this analysis is their application for a complex seismic crisis where different earthquakes take place in clusters in a short period. Therefore, the Hurst exponent has been obtained for the seismic crisis of Alhoceima (Mediterranean Sea) of January-March, 2016, where at least five medium-sized earthquakes were triggered. According to the values obtained from the Hurst exponent for each cluster, a different mechanical origin can be detected, corroborated by the focal mechanisms calculated by the official institutions. Therefore, this type of analysis not only allows an approach to a greater understanding of a seismic series but also makes possible to discern different types of seismic origins.
Analysing Trends in Rice Cropping Intensity and Seasonality across the Philippines Using 14 Years of Moderate Resolution Remote Sensing Imagery
Rice is grown on over 100 million hectares in almost every country of Asia. It is the most important staple crop for food security and has high economic and cultural importance in Asian societies. The combination of genetic diversity and management options, coupled with the large geographic extent means that there is a large variation in seasonality (when it is grown) and cropping intensity (how often it is grown per year on the same plot of land), even over relatively small distances. Seasonality and intensity can and do change over time depending on climatic, environmental and economic factors. Detecting where and when these changes happen can provide information to better understand trends in regional and even global rice production. Remote sensing offers a unique opportunity to estimate these trends. We apply the recently published PhenoRice algorithm to 14 years of moderate resolution remote sensing (MODIS) data (utilizing 250m resolution 16 day composites from Terra and Aqua) to estimate seasonality and cropping intensity per year and changes over time. We compare the results to the surveyed data collected by International Rice Research Institute (IRRI). The study results in a unique and validated dataset on the extent and change of extent, the seasonality and change in seasonality and the cropping intensity and change in cropping intensity between 2003 and 2016 for the Philippines. Observed trends and their implications for food security and trade policies are also discussed.
Hydro-Chemical Characterization of Glacial Melt Waters Draining from Shaune Garang Glacier, Himachal Himalaya
A detailed study of the ion chemistry of the Shaune Garnag glacier meltwater has been carried out to assess the role of active glacier in the chemical denudation rate. The chemical compositions of various ions in meltwater of the Shaune Garang glacier were analyzed during the melting period 2015 and 2016. Total 112 of melt water samples twice in a day were collected during ablation season of 2015 and 2016. To identify various factors controlling the dissolved ionic strength of Shaune Garang Glacier meltwater statistical analysis such as correlation matrix, Principle Component Analysis (PCA) and factor analysis were applied to deduce the result. Cation concentration for Ca²⁺ > Mg²⁺ > Na⁺ > K⁺ in the meltwater for both the years can be arranged in the order as Ca²⁺ > Mg²⁺ > Na⁺ > K⁺. Study showed that Ca²⁺ and HCO₃⁻ found to be dominant on the both melting period. Carbonate weathering identified as the dominant process controlling the dissolved ion chemistry of meltwater due to the high ratios of (Ca²⁺ + Mg²⁺) versus TZ+ and (Ca²⁺ + Mg²⁺) versus (Na⁺ + K⁺) in the study area. The cation denudation rate of the Shaune Garnag catchment is 3412.2 m⁻² a⁻¹, i.e. higher than the other glacierised catchment in the Himalaya, indicating intense chemical erosion in this catchment.
Triassic Magmatism in Southern Beishan Orogen, Northwest China: Zircon U–Pb Geochronology, Petrogenesis and Tectonic Implications
The tectonic evolution of the Beishan orogen, which forms part of the Central Asian Orogenic Belt, remains debated. This study reports the identification of three Triassic granitic plutons representing two distinct stages of magmatism in southern Beishan orogen. Zircon U–Pb dating constrains the early stage as 238–237 Ma and the late stage as 229–227 Ma. The granitoids belong to high-K calc-alkaline and shoshonitic series and exhibit alkalic-calcic and calc-alkalic features, and are weakly peraluminous rocks. Most of these granitoids are highly fractionated I-type and A-type granites. They have relatively high Isr values (0.7049–0.7086) and weak negative εNd(t) values of −1.5 to −2.1, with young Nd model ages of 1.04–0.91 Ga, indicating a crustal contribution. They also show markedly positive zircon εHf(t) values (+3.4 to +11.8) and two-stage Hf model ages of 1.06–0.69 Ga, indicating a mixture of mantle and crustal components. The lithospheric mantle beneath this region incorporating older subducted materials was metasomatized by fluids or melts. Partial melting of the metasomatized lithospheric mantle resulted in underplated magmas, which provided the heat and material input to generate the granitoids. The Middle Triassic granitic plutons show moderate negative Eu anomalies, enrichment of LILEs and depletion in Nb, Ta, and Ti suggesting partial melting of crustal components in response to the underplated mantle-derived magmas, probably linked to lithospheric delamination and asthenospheric upwelling. The Late Triassic granitic plutons show characteristics of post-orogenic granite with strong negative anomalies of Eu, Ba, Nb, Sr, P, and Ti, indicating fractional crystallization and crustal contamination during the emplacement process.
Dynamic Change of Floods Disaster Monitoring for River Central Bar by Remote Sensing Time-Series Images
The spatial extent and area of central river bars can always vary due to the impact of water level, sediment supply and human activities. In 2016, a catastrophic flood disaster caused by sustained and heavy rainfall happened in the middle and lower Yangtze River. The flood led to the most serious economic and social loss since 1954, and strongly affected the central river bar. It is essential to continuously monitor the dynamics change of central bars because it can avoid frequent field measurements in central bars before and after the flood disaster and is helpful for flood warning. This paper focused on the dynamic change of central bars of Phoenix bar and Changsha bar in the Yangtze River in 2016. In this study, GF-1 (GaoFen-1) WFV(wide field view) data was employed owing to its high temporal frequency and high spatial resolution. A simple NDWI (Normalized Difference Water Index) method was utilized for river central bar mapping. Human-checking was then performed to ensure the mapping quality. The relationship between the area of central bars and the measured water level was estimated using four mathematical models. Furthermore, a risk assessment index was proposed to map the spatial pattern of inundation risk of central bars. The results indicate a good ability of the GF-1 WFV imagery with a 16-m spatial resolution to characterize the seasonal variation of central river bars and to capture the impact of a flood disaster on the area of central bars. This paper observed a significant negative but nonlinear relationship between the water level and the area of central bars, and found that the cubic function fits best among four models (R² = 0.9839, P < 0.000001, RMSE = 0.4395). The maximum of the inundated area of central bars appeared during the rainy season on July 8, 2016, and the minimum occurred during the dry season on December 28, 2016, which are consistent with the water level measured by the hydrological station. The results derived from GF-1 data could provide a useful reference for decision-making of real-time disaster early warning and post-disaster reconstruction.
Finding the Optimal Meeting Point Based on Travel Plans in Road Networks
Given a set of source locations for a group of friends, and a set of trip plans for each group member as a sequence of Categories-of-Interests (COIs) (e.g., restaurant), and finally a specific COI as a common destination that all group members will gather together, in Meeting Point Based on Trip Plans (MPTPs) queries our goal is to find a Point-of-Interest (POI) from different COIs, such that the aggregate travel distance for the group is minimized. In this work, we considered two cases for aggregate function as Sum and Max. For solving this query, we propose an efficient pruning technique for shrinking the search space. Our approach contains three steps. In the first step, it prunes the search space around the source locations. In the second step, it prunes the search space around the centroid of source locations. Finally, we compute the intersection of all pruned areas as the final refined search space. We prove that the POIs beyond the refined area cannot be part of optimal answer set. The paper also covers an extensive performance study of the proposed technique.