A.M Rajabi; M Khamehchiyan; M.R Mahdavifar
Abstract
One of the most important effects of the Manjil earthquake (M=7/7, 20/6/1990) has been triggering of the numerous landslides in the area affected by the earthquake. To predict landslides locations during the future earthquakes, it is essential to analyze the distribution of landslides triggered by the ...
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One of the most important effects of the Manjil earthquake (M=7/7, 20/6/1990) has been triggering of the numerous landslides in the area affected by the earthquake. To predict landslides locations during the future earthquakes, it is essential to analyze the distribution of landslides triggered by the Manjil earthquake. In this paper, by employing the Monte Carlo Simulation, an area with 309 km2 including Chahar – Mahal and Chalkasar rectangles (near the Manjil Epicentre) was investigated in order to achieve a probabilistic model to predict earthquake-induced landslides. There is a good correlation between the provided map and the inventory map of landslides occurred during the Manjil earthquake.
A. Hezarkhani; P. Tahmasbi; O. Asghari
Abstract
Separation of alteration zones is one of the important processes in evaluation and identification of mining activities that provide great help to have better view of the region and its mineralization. Most of the alteration separation is based on petrological investigations and the other methods are ...
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Separation of alteration zones is one of the important processes in evaluation and identification of mining activities that provide great help to have better view of the region and its mineralization. Most of the alteration separation is based on petrological investigations and the other methods are less applied. Therefore, in this research, there is an attempt by applying RBPNN (Radial Basis Probabilistic Neural Network) to separate these alteration zones. Because of the special structure and easy designing of these networks, they are usually capable to solve the classification problem. The input data were 28 element analyses related to 45 geochemical samples and its outputs were classified alteration zones (potassic, transition, phyllic) that was coding for every inputs data. After selection the training and testing data, the network has been prepared for training and then the data were inputs and the results were outputs. According to the results, the network could distinguish the difficult spatial relation between the inputs, with 28 spatial variables and classify those correctly. The calculated MSE (Mean Square Error) is 0.0163, which shows the good performance of network in this field.
