Remote Sensing
mohammad sharifikia; jalal karami; Ehsan Falahati
Abstract
Optical Remote Sensing is a low-cost and efficient method to alteration zone detection. However in the area that have been covered by vegetation or alluvial, the identification of these areas is not very accurate with optical images. In this study fusion and integrating of ALOS-PALSAR L-band and ASTER ...
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Optical Remote Sensing is a low-cost and efficient method to alteration zone detection. However in the area that have been covered by vegetation or alluvial, the identification of these areas is not very accurate with optical images. In this study fusion and integrating of ALOS-PALSAR L-band and ASTER data by HSV, HSL, Maximum Likelihood and Artificial Neural Network has been done to discover and enhance the Argilic and Propylitic Alteration zones over the west part of Qazvin province in IRAN. For this purpose, Argilic and Propylitic alterations were primary identified unseeing ASTER image. Then based on geological data and field study, some areas with alterations covered by quaternary sediments, not detectable by ASTER images, were identified. In the following, the integration of the ALOS PALSAR L-band data and the ASTER SWIR bands with HSV, HLS, Maximum Likelihood and Artificial Neural Network were performed. The results of this study showed that the radar and optics data fusion, using HSV and HLS methods, increases the enhancement of visible argillic alteration zones in the study area. Also, the integration of radar and optics data with the Maximum Likelihood and the Artificial Neural Network methods,
A. H. Pasha; A. Sorbi; S. Behzadi
Abstract
Mass movements, especially landslides, are one of the natural hazards that to a large extent occur, are controlled, or are prevented by human. It is obvious that human interferences in nature regardless of stability conditions and its natural balance leads to physical reactions from the environment to ...
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Mass movements, especially landslides, are one of the natural hazards that to a large extent occur, are controlled, or are prevented by human. It is obvious that human interferences in nature regardless of stability conditions and its natural balance leads to physical reactions from the environment to return the sustainability and balance. Damages caused by the landslides, which have been growing in recent decades, have made humans to find appropriate solutions to reduce and control this phenomenon. Zonation of areas susceptible to landslide is one of the most widely used methods to avoid hazardous areas or applying controlling methods in hazardous areas. This research uses artificial neural network for zonation of landslide susceptibility in the Qazvin-Rasht quadrangle area. The studied area is one of the most susceptible areas for landslide event in terms of topography, climate, and geology, as the history of the area shows 338 recorded landslides. Fifteen variables studied in other researches as effective variables in occurrence of landslides were selected to investigate this area. By combining these variables and the map of existing landslides, value of each of the 15 variables was extracted for sliding points. In the next stage, a number of points (1000 points) were randomly selected from the area and values of these variables were extracted for them. Each of the two data sets was divided into two training (70%) and test (30%) categories. We combined each of the two training and test categories, and used their output for training and testing the network. The number of internal layers of the neural network was determined to be 9 layers based on trial and error method and calculation of the root mean square error value (RMSE = 0.4041). The constructed neural network is of feedforward networks type with back-propagation algorithm and its training algorithm is of Levenberg-Marquardt back-propagation training algorithm type. After training and testing the network and conducting necessary corrections on it, the constructed neural network was used to predict the sensitivity of landslides in studied area. We placed results of this prediction in a range from 0 to 1 and obtain the best zonation map of the landslide susceptibility by choosing a threshold. Final evaluation of the zonation map of landslide susceptibility in the Qazvin-Rasht quadrangle shows an error of approximately RMSE = 0.4164 and the constructed neural network identifies 298 out of 338 occurred landslides in the high-risk zone, indicating the accuracy of 88.1%.
T Azari; N Samani
Abstract
In recent years, the artificial neural networks (ANNs) are used as an alternative to the conventional type curve matching techniques for the determination of aquifer parameters. In this paper two multilayer perceptron networks (MLPNs) are developed for the determination of leaky confined aquifers parameters. ...
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In recent years, the artificial neural networks (ANNs) are used as an alternative to the conventional type curve matching techniques for the determination of aquifer parameters. In this paper two multilayer perceptron networks (MLPNs) are developed for the determination of leaky confined aquifers parameters. Leakage into the aquifer takes place from either the upper aquifer through the confining aquitard or the storage in the confining aquitard. The first and second networks are trained for the well functions of leaky aquifers (a) without and (b) with storage in the confining aquitard, respectively. By applying the principal component analysis (PCA) on the adopted training data sets the topology of both networks are reduced and their efficiency increased considerably. In contrast to the existing networks the topology of developed networks is fixed to (2×10×2) regardless of number of records in the pumping test data. The networks generate the match point coordinates for any individual pumping test data set. The match point coordinates are incorporated with Hantush-Jacob (1955) and Hantush (1960) analytical solutions and the aquifer parameter values are determined. The performance of the MLPNs is evaluated by three sets of real field data and their accuracy is compared with that of type curve matching techniques. The proposed MLPNs are recommended as simple and reliable alternatives to previous ANN methods and the type-curve matching techniques.
S Gholipour; A Kadkhodaie; M Makkipour; A .R Abadi chalaksaraee
Abstract
Total organic carbon content is one of the important parameters to evaluate the geochemical properties of oil- and gas-producing layers. In this study, total organic carbon content in the hydrocarbon-bearing formations was evaluated using log data in three stages. In the first stage, we used artificial ...
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Total organic carbon content is one of the important parameters to evaluate the geochemical properties of oil- and gas-producing layers. In this study, total organic carbon content in the hydrocarbon-bearing formations was evaluated using log data in three stages. In the first stage, we used artificial neural network to calculate the organic carbon content. In the second stage, total organic carbon was calculated by using ΔLogR computational method. Finally in the last stage, well log data were classified into a set of electrofacies, which were performed using the most efficient clustering analysis method, i.e. MRGC method. Based on cluster validity tests, this method is the best to cluster petrophysical data in certain electrofacies. Cluster analysis was employed for classification of data from both neural network and ΔLogR methods. The results showed that intelligent systems are more appropriate than traditional techniques which are based on ΔLogR approaches, and also have higher accuracy. The proposed method has been presented with a case study from the Azadegan oilfield.
