Two-phase SPH modelling of waves caused by dam break over a movable bed

This paper describes the application of the Smoothed Particle Hydrodynamics（SPH） method for modeling two dimensional waves caused by dam break over a movable bed in two dimensions.The two phase SPH method is developed to solve the Navier-Stokes equations.Both fluid and sediment phases are described by particles as weakly compressible fluids and the incompressibility is achieved by the equation of state.The sediment phase is modeled as a non-Newtonian fluid using three alternative approaches of artificial viscosity and Bingham Model.In this paper,the new formulations for two-phase flows are proposed.The numerical results obtained from the developed SPH model show acceptable accuracy with comparison to experimental data.     

Improved normalization of time‐lapse seismic data using normalized root mean square repeatability data to improve automatic production and seismic history matching in the Nelson field

Updating of reservoir models by history matching of 4D seismic data along with production data gives us a better understanding of changes to the reservoir, reduces risk in forecasting and leads to better management decisions. This process of seismic history matching requires an accurate representation of predicted and observed data so that they can be compared quantitatively when using automated inversion. Observed seismic data is often obtained as a relative measure of the reservoir state or its change, however. The data, usually attribute maps, need to be calibrated to be compared to predictions. In this paper we describe an alternative approach where we normalize the data by scaling to the model data in regions where predictions are good. To remove measurements of high uncertainty and make normalization more effective, we use a measure of repeatability of the monitor surveys to filter the observed time‐lapse data. We apply this approach to the Nelson field. We normalize the 4D signature based on deriving a least squares regression equation between the observed and synthetic data which consist of attributes representing measured acoustic impedances and predictions from the model. Two regression equations are derived as part of the analysis. For one, the whole 4D signature map of the reservoir is used while in the second, 4D seismic data is used from the vicinity of wells with a good production match. The repeatability of time‐lapse seismic data is assessed using the normalized root mean square of measurements outside of the reservoir. Where normalized root mean square is high, observations and predictions are ignored. Net: gross and permeability are modified to improve the match. The best results are obtained by using the normalized root mean square filtered maps of the 4D signature which better constrain normalization. The misfit of the first six years of history data is reduced by 55 per cent while the forecast of the following three years is reduced by 29 per cent. The well based normalization uses fewer data when repeatability is used as a filter and the result is poorer. The value of seismic data is demonstrated from production matching only where the history and forecast misfit reductions are 45% and 20% respectively while the seismic misfit increases by 5%. In the best case using seismic data, it dropped by 6%. We conclude that normalization with repeatability based filtering is a useful approach in the absence of full calibration and improves the reliability of seismic data.     

Is climate change impacting Rideau Canal Skateway,the world’s longest skating rink?

Natural Hazards - When disaster occurs in coal mines, the complex network structure of roadways and the disaster evolution process usually cause serious casualties. An efficient escape plan becomes...     

Assessing the impacts of climate and land use change on streamflow,water quality and suspended sediment in the Kor River Basin,Southwest of Iran

This research addressed the separate and combined impacts of climate and land use change on streamflow, suspended sediment and water quality in the Kor River Basin, Southwest of Iran, using (BASINS–WinHSPF) model. The model was calibrated and validated for hydrology, sediment and water quality for the period 2003–2012. The model was run under two climate changes, two land use changes and four combined change scenarios for near-future period (2020–2049). The results revealed that projected climate change impacts include an increase in streamflow (maximum increases of 52% under RCP 2.6 in December and 170% under RCP 8.5). Projected sediment concentrations under climate change scenarios showed a monthly average decrease of 10%. For land use change scenarios, agricultural development scenario indicated an opposite direction of changes in orthophosphate (increases in all months with an average increase of 6% under agricultural development scenario), leading to the conclusion that land use change is the dominant factor in nutrient concentration changes. Combined impacts results indicated that streamflows in late fall and winter months increased while in summer and early fall decreased. Suspended sediment and orthophosphate concentrations were decreased in all months except for increases in suspended sediment concentrations in September and October and orthophosphate concentrations in late winter and early spring due to the impact of land use change scenarios.     

Applying different scenarios for landslide spatial modeling using computational intelligence methods

Landslides every year impose extensive damages to human beings in various parts of the world; therefore, identifying prone areas to landslides for preventive measures is essential. The main purpose of this research is applying different scenarios for landslide susceptibility mapping by means of combination of bivariate statistical (frequency ratio) and computational intelligence methods (random forest and support vector machine) in landslide polygon and point formats. For this purpose, in the first step, a total of 294 landslide locations were determined from various sources such as aerial photographs, satellite images, and field surveys. Landslide inventory was randomly split into a testing dataset 70% (206 landslide locations) for training the different scenarios, and the remaining 30% (88 landslides locations) was used for validation purposes. To providing landslide susceptibility maps, 13 conditioning factors including altitude, slope angle, plan curvature, slope aspect, topographic wetness index, lithology, land use/land cover, distance from rivers, drainage density, distance from fault, distance from roads, convergence index, and annual rainfall are used. Tolerance and the variance inflation factor indices were used for considering multi-collinearity of conditioning factors. Results indicated that the smallest tolerance and highest variance inflation factor were 0.31 and 3.20, respectively. Subsequently, spatial relationship between classes of each landslide conditioning factor and landslides was obtained by frequency ratio (FR) model. Also, importance of the mentioned factors was obtained by random forest (RF) as a machine learning technique. The results showed that according to mean decrease accuracy, factors of altitude, aspect, drainage density, and distance from rivers had the greatest effect on the occurrence of landslide in the study area. Finally, the landslide susceptibility maps were produced by ten scenarios according to different ensembles. The receiver operating characteristics, including the area under the curve (AUC), were used to assess the accuracy of the models. Results of validation of scenarios showed that AUC was varying from 0.668 to 0.749. Also, FR and seed cell area index indicators show a high correlation between the susceptibility classes with the landslide pixels and field observations in all scenarios except scenarios 10_RF and 10_SVM. The results of this study can be used for landslides management and mitigation and development activities such as construction of settlements and infrastructure in the future.     

Hydrothermal alteration and mineralisation of the Glen Eden Mo-W-Sn deposit: a leucogranite-related hydrothermal system,Southern New England Orogen,NSW, Australia

The Glen Eden Mo-Sn-W deposit in north-eastern New South Wales, Australia, is an example of a leucogranite-related, low-grade, large-tonnage hydrothermal system. It occurs in the southern part of the New England Orogen and is hosted within Permian felsic volcanic rocks, intruded at depth by dykes of porphyritic microleucogranite (Glen Eden Granite). The deposit is hosted within a pipe-like quartz-rich greisen breccia body about 500 m in diameter, surrounded by a greisen zone several hundred metres across, zoning out into altered volcanic rocks. The dominant ore minerals, largely hosted as open space fillings and disseminations in quartz and quartz-rich greisen, are molybdenite, wolframite and cassiterite; they are accompanied by minor to trace amounts of muscovite, fluorite, topaz, siderite, pyrrhotite, arsenopyrite, chalcopyrite, sphalerite, bismuth, bismuthinite, joseite A, cosalite, galenobismutite, beryl, anatase and late-stage dickite and kaolinite. Two types of breccia are recognised: (1) greisenised volcanic rock fragments (quartz + muscovite), cemented by hydrothermal quartz ± K-feldspar ± ore minerals, and (2) fragments of hydrothermal quartz ± cassiterite ± wolframite enclosed in quartz ± clay. In both types of breccia and in stockwork veins, there is evidence of early precipitation of Mo-Sn-W phases, followed by Bi minerals and base metal sulfides (± fluorite, siderite).Breccia formation and associated hydrothermal alteration (greisen, potassic, argillic, propylitic) are interpreted to be related to devolatilisation of the highly fractionated Glen Eden Granite of early Triassic age (240±1 Ma based on ⁴⁰Ar/³⁹Ar geochronology of greisen muscovite) as well as to fluid mixing with meteoric waters. The breccia pipe could have formed in part by rock dissolution and collapse, as well as by explosive degassing of boiling fluids. Fluid inclusion evidence is consistent with boiling, with breccia pipe formation and mineralisation having mainly occurred at 250–350 °C from fluids with salinity of 0.4–9 wt% NaCl equivalent in the dilute types and 30–47 wt% NaCl equivalent in the hypersaline types. Stable isotopic evidence (O, D, C, S) indicates a strong magmatic contribution to the hydrothermal fluids and metals in the breccia. The ¹⁸O values of quartz decrease outward from the breccia pipe (10.6–12.3 in the pipe to 3.4–8.7 in the peripheral quartz) indicating that there has been mixing with isotopically light (high latitude) meteoric fluids, mainly after formation of the breccia pipe.     

Using high-resolution satellite imagery to provide a relief priority map after earthquake

After the earthquake occurrence, collecting correct information about the extent of damage is essential for managing critical conditions and allocating limited resources. The prepared building damage maps sometimes bring about waste of time required for rescuing individuals under the rubble by wrongly conducting rescue teams toward regions with a lower rescue priority. In this research, an algorithm based on using a proposed standard at database level was developed to prioritize damaged buildings by considering five key elements of land use type, the degree of damage to buildings, the land use differentiation index, time of the highest population density in each land use, and time of disaster’s incidence. The steps of the proposed method which was implemented in the MATLAB environment include: detecting buildings on the pre- and post-event imagery, implementing texture features for each candidate building, choosing the optimal features by genetic algorithm, determining the degree of building damage in three classes of negligible damage, substantial damage, and heavy damage by using the difference between chosen features as inputs of the designed neurofuzzy inference system. Data collected from field observations were compared to the output obtained from the proposed algorithm. This comparison presented a general accuracy of 88% and Kappa coefficient of 79% in the classification of buildings into three damage classes. The proposed standard then was used for classifying damaged buildings into relief priorities of high, medium, and low. Findings revealed that the relief priority map could be a basis for correct guidance of relief and rescue teams during crucial times following earthquakes.