A Monte Carlo adapted finite element method for dislocation simulation of faults with uncertain geometry

Dislocation modelling of an earthquake fault is of great importance due to the fact that ground surface response may be predicted by the model. However, geological features of a fault cannot be measured exactly, and therefore these features and data involve uncertainties. This paper presents a Monte Carlo based random model of faults with finite element method incorporating split node technique to impose the effects of discontinuities. Length and orientation of the fault are selected as random parameters in the domain model, and hence geometrical uncertainties are encountered. Mean and standard deviation values, as well as probability density function of ground surface responses due to the dislocation are computed. Based on analytical and numerical calculation of dislocation, two approaches of Monte Carlo simulations are proposed. Various comparisons are examined to illustrate the capability of both methods for random simulation of faults.     

Identification of hydrogeochemical processes and pollution sources of groundwater resources in the Marand plain,northwest of Iran

The main aims of the present study are to identify the major factors affecting hydrogeochemistry of groundwater resources in the Marand plain, NW Iran and to evaluate the potential sources of major and trace elements using multivariate statistical analysis such as hierarchical clustering analysis (HCA) and factor analysis (FA). To achieve these goals, groundwater samples were collected in three sampling periods in September 2013, May 2014 and September 2014 and analyzed with regard to ions (e.g., Ca²⁺, Mg²⁺, Na⁺ and K⁺, HCO₃ ^?, SO₄ ^2?, Cl^?, F^? and NO₃ ^?) and trace metals (e.g., Cr, Pb, Cd, Mn, Fe, Al and As). The piper diagrams show that the majority of samples belong to Na–Cl water type and are followed by Ca–HCO₃ and mixed Ca–Na–HCO₃. Cross-plots show that weathering and dissolution of different rocks and minerals, ion exchange, reverse ion exchange and anthropogenic activities, especially agricultural activities, influence the hydrogeochemistry of the study area. The results of the FA demonstrate that 6 factors with 81.7% of total variance are effective in the overall hydrogeochemistry, which are attributed to geogenic and anthropogenic impacts. The HCA categorizes the samples into two clusters. Samples of cluster C1, which appear to have higher values of some trace metals like Pb and As, are spatially located at the eastern and central parts of the plain, while samples of cluster C2, which express the salinization of the groundwater, are situated mainly westward with few local exceptions.     

Effects of climatic factors and soil salinity on the distribution of vegetation types containing <Emphasis Type="Italic">Anabasis aphylla</Emphasis> in Iran: a multivariate factor analysis

In order to identify factors affecting the distribution of Anabasis aphylla in rangelands of Iran, 132 climatic parameters which contributed to the species distribution (based on the ecological conditions of the study area) were selected. Factor analysis was then applied to determine the most important factors affecting species distribution. In the next stage, the spatial distribution map of An. aphylla throughout Iran was prepared. According to the results of factor analysis, ten factors with Eigenvalues greater than one explained 92.96% of the total variance. These factors were temperature below zero, winter rainfall, summer rainfall, wind, sunlight, warm season dust, rainfall in fall, thunderstorms, relative humidity in September, and cloudy days in winter and explained 34.34, 9.71, 9.69, 8.85, 5.99, 5.35, 4.97, 4.78, 4.73, and 4.51% of the total variance, respectively. Moreover, six vegetation types containing An. aphylla were identified throughout the country. These types were distributed in the central areas of Iran and also in patches within the southeastern parts of the country. The results showed that in addition to the nine above climatic factors, soil variables particularly salinity influence establishing the vegetation types in their distribution areas and the vegetation types are located in low to relatively high salinity, and soils with these salinity levels (low to relatively high salinity) are suitable for these types of plants.     

A Set of Classified Integer Programming (IP) Models for Optimum Transition from Open Pit to Underground Mining Methods

Natural Resources Research - During the last few decades, open pit mines have been deepened to the remote depths of the ground such that removing great volumes of waste rocks may jeopardize their...     

Synoptic and thermodynamic characteristics of 30 March–2 April 2009 heavy rainfall event in Iran

A heavy rainfall event during the period from 30th of March to 2nd of April 2009 has been studied using upper air and surface data as well as NOAA HYSPLIT model. This observational study attempts to determine factors responsible for the occurrence of heavy rainfall over Iran induced by Mediterranean cyclone, a western severe sub-tropical storm that made rainfall on most regions of the country. On the surface chart, cyclones, anticyclones and weather fronts were identified. The positions of the cold and warm fronts, which extended from a two-core low pressure center, were quite in good agreements with directions of winds i.e., westerly, southerly and easterly flows as well as the regions of precipitation. The heavy rain event occurred due to a Mediterranean cyclone’s activity over the study area, while other conditions were also responsible for this event such as an unstable atmosphere condition with abundant low-level moisture, which the warm and moist air parcels were brought by the southwesterly low-level jet into the country from Persian Gulf, Oman Sea, Indian Ocean and Caspian Sea at lower levels as well as Mediterranean Sea, Red Sea and Persian Gulf at upper levels over the examined period. A strong low-level convergence zone was observed along the wind-shift line between the southwesterly flow because of the low-level jet and the northeasterly flow due to the Russian high pressure. The amount of precipitable water varied between 20 and 24 kg m^?2, surface moisture convergence exceeded 2.5 g kg^?1 s^?1 and the highest CAPE value in the sounding profiles was observed in Birjand site with 921 J kg^?1 during the study period. The HYSPLIT model outputs confirmed the observed synoptic features for the examined system over the country.     

Rainfall and rainy days trend in Iran

In this study, long-term annual and monthly trends in rainfall amount, number of rainy days and maximum precipitation in 24?h are investigated based on the data collected at 33 synoptic stations in Iran. The statistical significance of trend and climate variability is assessed by the Mann-Kendall test. The Linear trend analysis and the Mann-Kendall test indicate that there are no significant linear trends in monthly rainfall at most of the synoptic stations in Iran. However, the maximum number of stations with negative trends have been observed in April (29 station), and then in May (21 stations) and February (21 stations) and with positive trends in December (26 stations) and July (24 stations). The significant linear trends, with a significant level of 0.05, in annual rainfall have been noticed only at five stations. The monthly number of rainy days does not show any significant linear trend for most areas in Iran. The maximum number of stations with monthly negative trends in rainy days has also been observed in April with the minimum in December. In April, out of 24 stations with negative trends, 12 stations have a significant negative trend. Contrary to that, in October there is no significant linear trend. Most stations have positive trends in annual number of rainy days. Also, the monthly maximum precipitation in 24?h does not show any significant linear trend for most areas in Iran. The maximum number of stations with monthly negative trends in maximum precipitation has also been observed in February with the minimum in December. In spite of that, there are almost no significant precipitation variations in Iran during the last 50-odd years, the tendency of decreasing rainfall amount in April and increasing rainfall amount in December and July could indicate an eventual climate change in this area in the future.     

An improved classification of mineralized zones using particle swarm optimization: A case study from Dagh-Dali ZnPb (±Au) prospect,Northwest Iran

Classification of mineralized areas into different geochemical classes in terms of prospectivity is crucial in the optimal management of exploration risk and costs. Machine learning (ML) algorithms can be served as appropriate alternatives for separating ore-related anomalies due to avoiding the assumptions of statistical distribution and compatibility with the multivariate nature of geochemical features. By hybridizing the ML with a metaheuristic algorithm called particle swarm optimization (PSO), this contribution aims to provide an innovative approach to optimize the classification of geochemical anomalies within the study area. The algorithm, PSO, is inspired by simulating the social behavior of flocks of birds in search of food. The Dagh-Dali ZnPb (±Au) mineral prospect in northwest Iran was subjected as a case study to examine the integrity of the proposed method. Mineralization-related features were extracted by applying principal component analysis (PCA) on metallogenic elements analyzed in soil samples as PC1 and PC2 with elemental assemblages of AgAuPbZn and PbZn, respectively. The silhouette index was employed to estimate the number of underlying geochemical clusters within the adopted feature space. To constitute a comparative analysis, two k-means clustering and PSO-based learning (PSO-L) algorithms were implemented to classify the gridded data of PC1 and PC2 within the study area. The results indicated that the use of PSO has improved the cost function of the clustering problem (up to 4%). Adapting the mineralization classes with the metallogenic evidence demonstrated by boreholes drilled in the study area indicated that PSO-L was superior to the traditional k-means method, improving the accurate estimation of subsurface mineralization classes by 7%. By overcoming the drawbacks of conventional methods for trapping at the local optima, PSO-based learning possesses the potential to highlight weak mineralization signals that are numerically located in boundary conditions. The results show that the proposed approach can serve as an effective medium for optimal modeling of geochemical classes and management of detailed exploration operations.     

Landslide susceptibility assessment in the Anfu County,China: comparing different statistical and probabilistic models considering the new topo-hydrological factor (HAND)

The present study is aimed at producing landslide susceptibility map of a landslide-prone area (Anfu County, China) by using evidential belief function (EBF), frequency ratio (FR) and Mahalanobis distance (MD) models. To this aim, 302 landslides were mapped based on earlier reports and aerial photographs, as well as, carrying out several field surveys. The landslide inventory was randomly split into a training dataset (70%; 212landslides) for training the models and the remaining (30%; 90 landslides) was cast off for validation purpose. A total of sixteen geo-environmental conditioning factors were considered as inputs to the models: slope degree, slope aspect, plan curvature, profile curvature, the new topo-hydrological factor termed height above the nearest drainage (HAND), average annual rainfall, altitude, distance from rivers, distance from roads, distance from faults, lithology, normalized difference vegetation index (NDVI), sediment transport index (STI), stream power index (SPI), soil texture, and land use/cover. The validation of susceptibility maps was evaluated using the area under the receiver operating characteristic curve (AUROC). As a results, the FR outperformed other models with an AUROC of 84.98%, followed by EBF (78.63%) and MD (78.50%) models. The percentage of susceptibility classes for each model revealed that MD model managed to build a compendious map focused at highly susceptible areas (high and very high classes) with an overall area of approximately 17%, followed by FR (22.76%) and EBF (31%). The premier model (FR) attested that the five factors mostly influenced the landslide occurrence in the area: NDVI, soil texture, slope degree, altitude, and HAND. Interestingly, HAND could manifest clearer pattern with regard to landslide occurrence compared to other topo-hydrological factors such as SPI, STI, and distance to rivers. Lastly, it can be conceived that the susceptibility of the area to landsliding is more subjected to a complex environmental set of factors rather than anthropological ones (residential areas and distance to roads). This upshot can make a platform for further pragmatic measures regarding hazard-planning actions.     

Optimally locating additional drill holes in three dimensions using grade and simulated annealing

Average kriging variance is a standard tool used in optimization of the location of additional drill holes. However, this tool cannot distinguish between areas with different priorities. This limitation could be eliminated by using weighted average kriging variance. This paper extends the problem of optimal location to three dimensional cases, use grade as a weight and search optimum locations by simulated annealing. Weighted average kriging variance is used as objective function. The method is applied to a copper deposit. Results have shown that weighting of the estimation variance with ??grade?? is effective only when the difference among the grades estimated for different blocks is considerable.