A Simulated Annealing-Based Algorithm to Locate Additional Drillholes for Maximizing the Realistic Value of Information

Locating additional drillholes based on information gathered from the initial drilling is a very difficult decision-making step in the process of detailed explorations. The most appropriate locations for additional drillholes are those wherein the information gathered from drilling has more value compared to that from other locations. From among the common methods proposed in information systems for measuring the information value, use of the “realistic value” is a very practical one. The realistic value of information is derived from measuring the differences in the decision makers’ performances when provided with different information sets. On this basis, a mathematical model has been proposed in this paper for optimal location of additional drillholes where the information gathered from drillholes has the highest possible value. Due to the combinatorial nature of this model, use has been made of a simulated annealing-based algorithm for its solution. The proposed model has been applied in Sungun copper deposit for locating additional drillholes; results have revealed that the model is valid.     

Frequency analysis of climate extreme events in Zanjan, Iran

In this study, generalized extreme value distribution (GEV) and generalized Pareto distribution (GPD) were fitted to the maximum and minimum temperature, maximum wind speed, and maximum precipitation series of Zanjan. Maximum (minimum) daily and absolute annual observations of Zanjan station from 1961 to 2011 were used. The parameters of the distributions were estimated using the maximum likelihood estimation method. Quantiles corresponding to 2, 5, 10, 25, 50, and 100 years return periods were calculated. It was found that both candidate distributions fitted to extreme events series, were statistically reasonable. Most of the observations from 1961 to 2011 were found to fall within 1–10 years return period. Low extremal index (θ) values were found for excess maximum and minimum temperatures over a high threshold, indicating the occurrence of consecutively high peaks. For the purpose of filtering the dependent observations to obtain a set of approximately independent threshold excesses, a declustering method was performed, which separated the excesses into clusters, then the de-clustered peaks were fitted to the GPD. In both models, values of the shape parameters of extreme precipitation and extreme wind speed were close to zero. The shape parameter was less negative in the GPD than the GEV. This leads to significantly lower return period estimates for high extremes with the GPD model.     

Soil bentonite wall protects foundation from thrust faulting: analyses and experiment

When seismic thrust faults emerge on the ground surface,they are particularly damaging to buildings,bridges and lifelines that lie on the rupture path.To protect a structure founded on a rigid raft,a thick diaphragm-type soil bentonite wall(SBW) is installed in front of and near the foundation,at sufficient depth to intercept the propagating fault rupture.Extensive numerical analyses,verified against reduced–scale(1 g) split box physical model tests,reveal that such a wall,thanks to its high deformability and low shear resistance,"absorbs" the compressive thrust of the fault and forces the rupture to deviate upwards along its length.As a consequence,the foundation is left essentially intact.The effectiveness of SBW is demonstrated to depend on the exact location of the emerging fault and the magnitude of the fault offset.When the latter is large,the unprotected foundation experiences intolerable rigid-body rotation even if the foundation structural distress is not substantial.     

New climatic zones in Iran: a comparative study of different empirical methods and clustering technique

Theoretical and Applied Climatology - Recently in agricultural and industrial sectors, researchers have started to classify the climate of a region using empirical methods and clustering. This...     

Estimation of concentration and exposure doses due to radon by using CR-39 plastic track detectors in the residences of Sudhnuti, Azad Kashmir, Pakistan

This paper presents the results of indoor radon concentration measurements in 120 dwellings of district Sudhnuti of Azad Kashmir. Measurements were taken with CR-39 passive alpha track detector. CR-39 based box type radon detectors were installed in a bedroom and living rooms of each house. The detectors were retrieved after exposing to indoor radon for period of 6 months and then etched in 6 M NaOH at 80°C for 16 h, the observed track densities were converted in to the indoor radon concentration. Indoor radon concentration varied from 20 ± 12 to 170 ± 4 Bq m⁻³ for the houses of the district Sudhnuti. Arithmetic mean (AM), geometric mean (GM) and geometric standard deviations (GSD) were found to be 82 ± 6, 77 ± 6 and 1.51, respectively. The minimum value of weighted average radon concentration was recorded in one of the house of Mang town, whereas the maximum value was found in the Pattan Sher Khan region. Doses due to indoor radon exposure vary from 0.50 ± 0.31 to 4.28 ± 0.11 mSv year⁻¹ AM, GM and GSD. of mean effective doses were found to be 2.06 ± 0.13, 1.95 ± 0.18 and 1.51, respectively. According to the recommendations made by the Health Protection Agency, UK (200 Bq m⁻³) all the houses surveyed are within the safe limits.     

Hydrogeochemical evolution of groundwaters with excess fluoride concentrations from Dashtestan, South of Iran

Hydrogeochemical investigations were carried out in the Dashtestan, the eastern part of Borazjan, with a focus on fluoride content. The study area is underlain by a complex geology that is dominated by three lithological units, namely marl, alluvial sediments, and carbonate rocks. To assess the major geochemical factors controlling the fluoride enrichment in water, 37 groundwater and 12 surface water samples were collected from the three lithological units. Fluoride concentrations ranged up to 3?mg/L, and average concentrations varied from 1.12 (in carbonate aquifers) to 1.73 (in alluvial aquifers) to 1.82?mg/L (in marl aquifers). To study the influence of rocks and soils on groundwater quality, an additional 41 soil and rock samples were also taken and analyzed for fluoride. The order of average fluoride content in both rocks and soils is: marl?>?alluvial sediments?>?limestone, which confirms that marl is a likely source of fluoride.     

Probabilistic rainfall thresholds for flood forecasting: evaluating different methodologies for modelling rainfall spatial correlation (or dependence)

Rainfall threshold (RT) method is one of the evolving flood forecasting approaches. When the cumulative rainfall depth for a given initial soil moisture condition intersects the threshold rainfall curve, the peak discharge is expected to be equal or greater than the threshold discharge for flooding at the target site. Besides the total rainfall depth, spatial and temporal distribution of rainfall impacts the flood peak discharge and the time to peak. To revisit a previous study conducted by the authors, in which spatially independent rainfall pattern was assumed, the spatial distribution of rainfall was simulated following a Monte Carlo approach. The structure of the spatial dependence among sub‐watersheds' rainfalls was taken into account under three different scenarios, namely independent, bivariate copula (2copula) and multivariate Gaussian copula (MGC). For each set of generated random dimensionless rainfalls, the probabilistic RT curves were derived for dry moisture condition. Results were evaluated with both historical and simulated events. For the simulated events, threshold curves were assessed by means of categorical statistics, such as hit rate, false rate and critical success index (CSI). Results revealed that the best performance based on the CSI criterion corresponded to 50% curve in 2copula and MGC scenarios as well as 90% curve in the independent scenario. The recognition of 50% curve in 2copula and MGC scenarios is in agreement with our expectations that the mean probable curve should have the best performance. Moreover, the proposed inclusion of spatially dependent rainfall scenario improved the performance of RT curves by about 25% in comparison with the presumed spatially uniform rainfall scenario. Copyright © 2011 John Wiley & Sons, Ltd.     

Determination of Realistic and Statistical Value of the Information Gathered from Exploratory Drilling

Unlike other branches of geosciences, exploratory drilling has not been investigated within the framework of an information system; so, the expression “value of exploratory drilling information” (despite its common usage) is vague. This article presents a model for the evaluation of value of the information gathered from exploratory drilling after studying different mineral exploration and exploratory drilling systems within the framework of an “information system.” Although this model does not present the economic value of information, it is a suitable tool for comparing different drilling patterns. The model was verified on the basis of drilling data for the Gol-Gohar XIIA anomaly.     

Burden prediction in blasting operation using rock geomechanical properties

Burden prediction is a vital task in the production blasting. Both the excessive and insufficient burden can significantly affect the result of blasting operation. The burden which is determined by empirical models is often inaccurate and needs to be adjusted experimentally. In this paper, an attempt was made to develop an artificial neural network (ANN) in order to predict burden in the blasting operation of the Mouteh gold mine, using considering geomechanical properties of rocks as input parameters. As such here, network inputs consist of blastability index (BI), rock quality designation (RQD), unconfined compressive strength (UCS), density, and cohesive strength. To make a database (including 95 datasets), rock samples are used from Iran’s Mouteh goldmine. Trying various types of the networks, a neural network, with architecture 5-15-10-1, was found to be optimum. Superiority of ANN over regression model is proved by calculating. To compare the performance of the ANN modeling with that of multivariable regression analysis (MVRA), mean absolute error (E _a), mean relative error (E _r), and determination coefficient (R ²) between predicted and real values were calculated for both the models. It was observed that the ANN prediction capability is better than that of MVRA. The absolute and relative errors for the ANN model were calculated 0.05 m and 3.85%, respectively, whereas for the regression analysis, these errors were computed 0.11 m and 5.63%, respectively. Moreover, determination coefficient of the ANN model and MVRA were determined 0.987 and 0.924, respectively. Further, a sensitivity analysis shows that while BI and RQD were recognized as the most sensitive and effective parameters, cohesive strength is considered as the least sensitive input parameters on the ANN model output effective on the proposed (burden).     

An SVM-based machine learning method for the separation of alteration zones in Sungun porphyry copper deposit

Sungun porphyry copper deposit is in East Azarbaijan province, NW of Iran. There exist four hypogene alteration types in Sungun: potassic, propylitic, potassic–phyllic, and phyllic. Copper mineralization is essentially associated more with the potassic and less with the phyllic alterations and their separation is, therefore, quite important. This research has tried to separate these two alteration zones in Sungun porphyry copper deposit using the Support Vector Machine (SVM) method based on the fluid inclusion data, and seven variables including homogenization temperatures, salinity, pressure, depth, density and the Cu grade have been measured and calculated for each separate sample. To apply this method, use is made of the radial basis function (RBF) as the kernel function. The best values for λ and C (the most important SVM parameters) that perform well in the training and test data are 0.0001 and 1, respectively. If these values for λ and C are applied, the phyllic and potassic alteration zones in the training and test data will be separated with an accuracy of about 95% and 100%, respectively. This method can help geochemists in separating the alteration zones because classifying and separating samples microscopically is not only very hard, but also quite time and money consuming.