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).     

Applying an optimized proxy-based workflow for fast history matching

History matching is still one of the main challenging parts of reservoir study especially in giant brown oil fields with lots of wells. In these cases, history matching with conventional manual technique needs many runs and takes months to get a match. In this work, an innovative approach was suggested for fast history matching in a real brown field. The workflow was employed based on an optimized proxy model for history matching of a field consisting of 14 active wells with multiple responses (which are production rate and pressure data) in the south part of Iran. The main important features of the proposed algorithm were defining a proxy model which is response surface method in which 21 model parameters were incorporated based on cubic centered face method. The proxy model was then optimized by one of the most famous algorithms which is genetic algorithm. Proxy model was successfully performed using 256 samples leading into p- value of 0.531 and R ² of 0.91 dataset. As a result, the proposed workflow and algorithm showed good and acceptable results for history matching of studied real model.     

Comparative study of different wavelets for developing parsimonious Volterra model for rainfall-runoff simulation

Although the Volterra models are non-parsimonious ones, they are being used because they can mimic dynamics of complex systems. However, applying and identification of the Volterra models using data may result in overfitting problem and uncertainty. In this investigation we evaluate capability of different wavelet forms for decomposing and compressing the Volterra kernels in order to overcome this problem by reducing the number of the model coefficients to be estimated and generating smooth kernels. A simulation study on a rainfall?runoff process over the Cache River watershed showed that the method performance is successful due to multi-resolution capacity of the wavelet analysis and high capability of the Volterra model. The results also revealed that db2 and sym2 wavelets have the same high potential in improving the linear Volterra model performance. However, QS wavelet was more successful in yielding smooth kernels. Moreover, the probability of overfitting while identifying the nonlinear Volterra model may be less than the linear model.     

Hydrogeochemistry and carbonate saturation model of groundwater, Khanyounis Governorate—Gaza Strip, Palestine

Groundwater is a critical resource in Khanyounis city as it is the main source of water. The aquifer has deteriorated to a high degree, during the last two to three decades, in quality and quantity. More than 90% of the population get their drinking water from brackish water desalination plants. Fifteen domestic wells were sampled in 2002 to probe the hydrogeochemical components that influence the water quality. Na, K, Ca, Mg, Cl, SO₄, NO₃, and HCO₃ were analyzed. The data were statistically treated and plotted on the Piper diagram. A hydrogeochemical numerical model for carbonate minerals was constructed using the PHREEQ package. The results show that the groundwater is polluted with Cl, from seawater, and NO₃, sourced from fertilizers and sewage. The regression analysis shows that there are three groups of elements that are significantly and positively correlated. Na–Cl signature and plot show that seawater intrusion is advancing into the aquifer. The main hydrochemical facies of the aquifer (Na+K–Cl+SO₄), represents 60% of the total wells. Whereas 32.3% of the wells are located in the no pair up and no pair down fields on the Piper diagram. Calcite, dolomite, and aragonite solubility were assessed in terms of the saturation index where they show positive values indicating supersaturation. The hydrogeochemical behavior is rather complicated and is affected by anthropogenic and natural parameters.     

The Miduk porphyry Cu deposit,Kerman, Iran: A geochemical analysis of the potassic zone including halogen element systematics related to Cu mineralization processes

Miduk hypogene and supergene porphyry Cu–Mo mineralization occurs within the Miocene porphyritic quartz–diorite and host Eocene plagioclase–hornblende phyric andesitic pyroclastic and flow sequence. Both the host rocks were extensively altered by hydrothermal fluids to dominantly potassic, phyllic, and argillic with interstitial to distal propylitic types.     

An Introduction to MODISI and SCMOD Methods for Correction of the MODIS Snow Assessment Algorithm

Detection, monitoring and precise assessment of the snow covered regions is an important issue. Snow cover area and consequently the amount of runoff generated from snowmelt have a significant effect on water supply management. To precisely detect and monitor the snow covered area we need satellite images with suitable spatial and temporal resolutions where we usually lose one for the other. In this study, products of two sensors MODIS and ASTER both on board of TERRA platform having low and high spatial resolution respectively were used. The objective of the study was to modify the snow products of MODIS by using simultaneous images of ASTER. For this, MODIS snow index image with high temporal resolution were compared with that of ASTER, using regression and correlation analysis. To improve NDSI index two methods were developed. The first method generated from direct comparison of ASTER averaged NDSI with those of MODIS (MODISI). The second method generated by dividing MODIS NDSI index into 10 codes according to their percentage of surface cover and then compared the results with the difference between ASTER averaged and MODIS snow indices (SCMOD). Both methods were tested against some 16 MODIS pixels. It is found that the precision of the MODISI method was more than 96%. This for SCMOD was about 98%. The RMSE of both methods were as good as 0.02.     

Geostatistics as a groundwater exploration planning tool: case of a brackish-saline aquifer

This paper discusses a geostatistical approach to model a groundwater aquifer in 3-D. The study aims at utilizing geostatistics as a tool for characterizing zones of better-water quality in a brackish-saline aquifer. In particular, the geostatistical model was constructed to characterize the aquifer’s salinity, represented by total dissolved solids (TDS), using logs of porosity and resistivity. Quality-checked estimated TDS vertical profiles were employed to construct and model horizontal and vertical semivariograms. Parameters of semivariogram models were used to develop both the kriging plan and the generated model. Results of this modeling process are shown in the form of horizontal salinity distribution maps. The aquifer was sliced into 20 layers, each 20 m thick, to represent its overall thickness. Salinity layers maps reflect vertical stratification of TDS concentrations in the aquifer and show that water quality deteriorates with depth and toward the northern part of the aquifer. Relatively better-quality water (TDS ≤10,000 mg/l) can be found at depths between 100 and 250 m below the aquifer’s top in both eastern and southeastern parts. Water in the same interval to the western and southwestern parts reflects the presence of higher TDS concentration. From a planning point of view, it is more feasible to target the eastern part of the aquifer for pumping and desalination purposes. In addition, the generated model could be utilized as an initial condition for flow simulation.