P. Haghighatmehr; M. J. Valadanzouj; R. Tajik; S. Jabari; M. R. Sahebi; R. Eslami; M. Ganjiyan; M. Dehghani
Abstract
A large area in Hashtgerd plain, in southwest of Tehran, is subject to the land subsidence induced by overexploitation of groundwater. In this paper, in order to study the subsidence SAR interferometry (InSAR) and global positioning system (GPS) are used. The small baseline subset (SBAS) algorithm is ...
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A large area in Hashtgerd plain, in southwest of Tehran, is subject to the land subsidence induced by overexploitation of groundwater. In this paper, in order to study the subsidence SAR interferometry (InSAR) and global positioning system (GPS) are used. The small baseline subset (SBAS) algorithm is used for deformation time series analysis. Time series analysis is performed using 6 interferograms calculated from 4 ENVISAT ASAR data spanning 4 months in 2008. A smoothing constraint that reduces the atmospheric noise, unwrapping and orbital errors whereas it preserves the non-linear deformation features is added to the least-squares solution. The time series results revealed that the area is subsiding continuously. Mean LOS deformation velocity map obtained from time series analysis demonstrated a considerable subsidence rate of 47 (mm/month). In order to assess the time series analysis results a dense GPS network consisting of 18 measuring stations is then established. The network design is carried out based on the subsidence spatial pattern extracted from an interferogram calculated from radar data of 2003-2004. The GPS stations are collecting the data simultaneously with radar data acquisitions. Horizontal and vertical components of the subsidence are extracted from GPS measurements. The comparison of InSAR and GPS time series shows the high compatibility of the results demonstrating the high performance of InSAR technique.
M. forutan; A. mansourian; M. Zareinejad; M. R. Sahebi
Abstract
Drilling in mine deposits has proven to be complicated, costly and time consuming process, hence it has identified the determining of optimum boreholes as a crucial issue in detailed studies. Because of some complexity in formation of mineral deposits, decreasing in risk and expenses of drilling ...
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Drilling in mine deposits has proven to be complicated, costly and time consuming process, hence it has identified the determining of optimum boreholes as a crucial issue in detailed studies. Because of some complexity in formation of mineral deposits, decreasing in risk and expenses of drilling may be continued by considering the wrapped condition of mineral deposits formation, followed by the integration of effective mineralization factors. By considering that in traditional methods of combination of mineralization’s factors like overlay and index overlay, is based on expert’s knowledge and expert knowledge is related to data accuracy, therefore, the accuracy of these methods could be remarkably decreased by large amount of data and noise. In order to solve these problems, utilization of flexible methods and powerful tools in data processing is obviously needed, especially in case of noise presence. Artificial Neural Networks are appropriate tools in large amount of data management and pattern recognition of noisy data, because of nonlinear, parallel and flexible architecture. So ANNs has been used in determining of proper position of boreholes. Neural Networks have various structures regarding their activation function and number of hidden layer and neurons in each layer. Consequently it is necessary to examine the performance of all these structures in determining the optimum position of boreholes.This paper represents a study on utilization GIS and four different Neural Networks namely: Multilayer peceptrons, Radial Basis Function, Generalized Neural Network and Probability Neural Network, for determining the position of boreholes of porphyry copper exploration in Chahfirouzeh region using cross correlation method. First, the mineralization factors are explained based on conceptual model of porphyry copper and predictor maps are produced, then, the training vectors are derived. After that, the networks are trained by geology, geochemistry and geophysics data layers. At the end, performances of the networks are compared. Implementation of Artificial Neural Networks reveals that two Neural Networks, GRNN and RBF, have the highest accuracy (approximately 80 to 83 %). Eventually, a potential map is produced by the best method.
N. Fouladi Moghaddam; A. A. Matkan; M. R. Sahebi; M. Roustaei
Abstract
Hydrocarbon fluid extraction from high compactable and low permeable reservoirs resulted in gradual surface deformation that causes significant costs due to overburden failures. However, surveying benchmarks make it possible to compare the repeated leveling measurements at the specific locations, then ...
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Hydrocarbon fluid extraction from high compactable and low permeable reservoirs resulted in gradual surface deformation that causes significant costs due to overburden failures. However, surveying benchmarks make it possible to compare the repeated leveling measurements at the specific locations, then it is necessary to introduce an effective method that is more real time and cost-effective. Differential SAR interferometry (DInSAR) is a new technology in which satellite images are used for field surface displacement monitoring. In this method, the high resolution images derived from Radar measurements are used for surface deformation rates assessment to improve the management and mitigation of traditional production costs. In this study, surface displacements caused by fluid withdrawal in Aghajari oil field are presented using Radar observations as the InSAR data reveal both subsidence and uplift signals for each production and observation wells distributed over the site. A number of production site inspections in a time series of interferograms reveal that the surface deformation signals developed due to extraction in several months as well as different subsidence or uplift rates and deformation styles occur locally depending on the geological conditions and excavation rates.
